Integrated Science of Global Epidemics (Integrated Science, 14) [1st ed. 2023] 3031177770, 9783031177774

Table of contents :
Preface
Contents
1 Introduction to Integrated Science of Global Epidemics
1 Introduction
1.1 No Matter Silent or Noisy
1.2 The Problem: Health Inequality
1.3 One World, One Epidemic, One Health
1.4 Coronavirus Epidemics: More than One per Decade
2 Important Concerns and Challenges
2.1 Urbanization
2.2 Mass Casualties
2.3 Discrimination Issues
2.4 Psychological Assessment Limitations
3 Important Facilitators
3.1 Social Marketing
3.2 Artificial Intelligence
4 Physical Inactivity and Obesity
5 Global Epidemics and Children and Adolescents’ Health
5.1 The Impact of a Pandemic on children’s Development
5.2 HIV/AIDS Among Adolescents
5.3 Education
6 Before Experience
6.1 Integrated Science for COVID
6.1.1 Integrated Diagnostics
Sensor Systems
Imaging
6.1.2 Integrated Healthcare System
6.1.3 Integrated Education System
6.1.4 Integrated Recovery Care
6.1.5 Integrated Mental Health Interventions
6.1.6 Integrated Understanding Pathogenesis
6.1.7 Integrated Environmental and Medical Science
6.2 Integrated Science for Obesity
6.3 Integrated Science for HIV/AIDS
7 Conclusion
References
2 Emerging Viral Infections in Human Population
1 Introduction
2 Contributory Factors to the Emergence of Viruses
3 Impact of Increased Travel on Emerging Diseases
4 Reservoirs in Virus Transmission Dynamics
5 Wildlife as a Compounding Factor
6 Role of Companion Animals and Animals Held in Captivity in Emerging Viruses
7 Civil Unrest and War as Catalysts for Emerging Viruses
8 National Prevention Strategy
9 Public Health Collaboration Between Agencies at Points of Entry
10 Public Health Risk Assessment
11 Surveillance of Health Challenges
12 SARS-CoV-2 Pandemic
13 Global Disease Information Systems
14 Prophylactic and Control Measures
15 Dangerous Twist of Ebola Virus in West Africa
16 Approaching Emerging Diseases from One Health Perspective
17 Conclusion
References
3 One Health as an Integrated Approach: Perspectives from Public Services for Mitigation of Future Epidemics
1 Introduction
2 The Interrelation of Public Services and Health and Well-Being
2.1 Health Services
2.2 Water and Sanitation Services
2.3 Provision of Green Spaces
2.4 The Food System, Land-Use Planning, and Conservation
3 Health Implications of Different Public Services for the Health of Humans, Animals, and the Environment
3.1 Health Services
3.2 Water and Sanitation Services
3.3 Provision of Green Spaces
3.4 Food System
3.5 Land-Use Planning and Conservation
4 OH Actions to Promote Public Health Services: Enabling Health and Reducing Risks
4.1 Health Services
4.2 Water and Sanitation Services
4.3 Green Spaces Provision
4.4 Food Systems
4.5 Land Use Planning and Conservation
5 Conclusion
References
4 A Multipronged Approach to Combat COVID-19: Lessons from Previous Pandemics for the Future
1 Introduction
2 Lessons from Previous Pandemics for Future
2.1 Lessons from 1918–1919 H1N1 “Spanish Flu” Pandemic
2.2 Lessons from 1957–1958 H2N2 “Asian Flu” Pandemic
2.3 Lessons from 1968–1969 H3N2 “A2/Hong Kong Flu” Pandemic
2.4 Lessons from the 2003 SARS Pandemic
2.5 Lessons from 2009–2010 pH1N1/09 “Swine Flu” Pandemic
3 A Multipronged Approach to Combat COVID-19
3.1 Fast, Extensive Testing
3.2 Contact Tracing Technology
3.3 Restrictive Public Health Measures
3.4 Healthcare Technology and Treatments
3.5 Healthcare Policy and Communication
4 Conclusion
References
5 Response to Disease Outbreaks in Africa: A Call to Build Resilient Health Systems
1 Introduction
2 Historical Trends of Disease Outbreaks in Africa
2.1 How Long Does It Take to Detect, Respond to, and Control Epidemics in Africa
3 Conceptual Clarity on Resilient Health System
4 The Resilience of Health Systems in African Countries: What Do We Know?
5 What Will It Take to Improve Health System Resilience?
6 Conclusion
References
6 Navigating Global Public Influenza Surveillance Systems for Reliable Forecasting
1 Introduction
2 The Use of Surveillance Data for Modeling Disease Outbreaks
2.1 Establishing Traveling Waves of Infections
2.2 Developing Dynamic Mapping Combined with Risk Factors
2.3 Considering Seasonal Migration and the Snowbird Effect
2.4 Considering the Individual Vulnerability of the Aging Population
2.5 Forging Insights for Testing: From Non-Specific to Specific
2.6 Creating National Disease Calendars
2.7 Integrating Social Calendars into Modeling
2.8 Creating Regional Integrated Disease Calendars
2.9 Consolidating the Efforts of Multiple Agencies
3 Attributes of a Mature Surveillance System
3.1 FluNet Data Integrity
3.1.1 NICs and Facilities
3.1.2 Reporting Lag
3.1.3 Case Definition
3.1.4 Surveillance Strategy
3.2 FluNet Data Completeness
4 Conclusion
References
7 Three Respiratory Syndrome Epidemics (SARS, MERS, COVID-19) in Two Decades: Clinical Epidemiological Considerations
1 Introduction
2 Clinical Epidemiological Investigation for an Emerging Respiratory Syndrome Epidemic
2.1 The Pre-Epidemiological Data Collection Process
2.2 The Epidemiological Data Collection Process
2.3 The Post-Epidemiological Data Collection Process
3 Clinical, Epidemiological Issues of Three Important Respiratory Syndrome Epidemics (SARS, MERS, COVID-19)
3.1 Clinical, Epidemiological Considerations on SARS
3.2 Clinical, Epidemiological Considerations on MERS
3.3 Clinical, Epidemiological Considerations on COVID-19
4 Lessons Learnt from Clinical, Epidemiological Investigation on the Three Respiratory Syndrome Epidemics
5 Conclusion
References
Epidemic in Complex Networks and Some Ideas About the Impact of Isolation Strategies in the Context of the COVID-19 Pandemic
1 Introduction
2 Mathematical Models of Infection Diseases
2.1 Formulation of Two Well Known Epidemiology Models
3 Application of Models to Virus Spreading in Networks
3.1 Fast Extinction Result
3.2 SIR Variation of Chakrabarti Model
3.3 Simulations of Chakrabarti SIS and SIR Model
4 My Own Model
4.1 Fast Extinction Result
4.2 Fast Extinction of My Own Model
4.3 Simulations of My Model
5 The Different Impact of Isolation Strategies
5.1 Adaptation of My Own Model to the COVID-19 Pandemic
6 Conclusions and Future Work
7 My Opinion About Future of My Field 30 Years Later
9 Optimal Control: Application and Applicability in Times of Pandemics
1 Introduction
1.1 General Context and Goal of the Chapter
1.2 The Past, Present, and Future of Interventions
1.3 Applied Control Models in Times of Pandemics
2 Novel Modeling and Control Approaches
2.1 Recent Contributions to the Spatiotemporal Control Modeling of Pandemics
2.2 A Reopening Control Approach Amid COVID-19 Using a New Dynamic Model
2.3 Applying the Model in a Reopening Strategy Evaluation Analysis
3 Discussion: Alternative and Promising Optimal Control Methods for Better Applicability
4 Conclusion
References
Analysis of a COVID-19 Model Implementing Social Distancing as an Optimal Control Strategy
1 Introduction
2 Formulation of Mathematical Model
3 Positivity and Boundedness
4 Equilibrium Analysis
4.1 Basic Reproduction Number (R0)
5 Sensitivity Analysis
6 Stability Analysis
6.1 Local Stability
6.2 Global Stability
7 Bifurcation Analysis at R0 =1
8 Numerical Results Without Implementing Control Strategy
9 Optimal Control Problem
9.1 Deduction of Total Cost Which Needs to Be Minimized
10 Numerical Simulation of the Optimal Control Problem
10.1 Effect of Weight Constants w3 and w4 on Optimal Control Policies
11 Conclusion
11 Impact of Policy Implementations on the Propagation of COVID-19 Before Vaccine
1 Introduction
2 A Helicopter View of Australia and New Zealand
3 The Policy Response in Australia
3.1 Five-Phase Response in Australia
3.1.1 Phase 1: Containment
3.1.2 Phase 2: Period of Indecision
3.1.3 Phase 3: Large Scale Testing But Weak Enforcement of Quarantine
3.1.4 Phase 4: Stricter Measures Imposed
3.1.5 Phase 5: Starting a “New Normal”
4 The Policy Response in New Zealand
4.1 Analysis of the New Zealand Response
5 Should We Compare Pandemic Propagation Between New Zealand and Australia?
6 Transmission in Queensland
7 Comparing Queensland and New Zealand for COVID-19 Propagation
8 Spill Over from COVID-19
8.1 Impact on Crime
9 Public Policy and Elections
10 Conclusion
References
12 Environmental Factors Associated with Global Pandemic Transmission and Morbidity
1 Introduction
2 Methods
2.1 Time and Regions Included in This Study
2.2 Data Sources of the Study
2.3 Data Testing by Statistics Tools
3 Results
3.1 Evaluation of Incidence and Casualties Associated with COVID-19
3.2 Evaluation of Environmental Factors
3.3 Determination of Association Between Weather and COVID-19
3.4 Frequency of Distributions of Age and Gender of COVID-19 Cases
3.5 Analysis of Different Factors Affecting the Outcome of COVID-19
4 Discussion
5 Conclusion
References
13 Urbanization and the Epidemiology of Infectious Diseases: Towards the Social Framing of Global Responses
1 Introduction
2 Urbanization: Urban Living and Health
3 Theorizing the Nexus Between Urbanization and Infectious Diseases and Pandemics
4 Urbanization and the Spread of Infectious Diseases Within and Across Cities
5 Urbanization: Infectious Diseases and Global Pandemic Risks
6 Urbanization, Deglobalization, and Stemming Pandemics
7 Framework for Global Social Responses to Infections and Pandemics
7.1 Inclusive Urban Governance
7.2 Inclusive Urban Health and Housing Policies
7.3 Effective and Proactive Surveillance
7.4 Dynamic Health Promotion
7.5 The Global Compact on Health Equity
7.6 Global Social Compact on the Marginalized and Poor
8 Conclusion
References
14 Pandemics and Mass Casualties: Cornerstones of Management
1 Introduction
2 Awareness and Preparedness
2.1 Awareness
2.2 Economical Preparedness
2.3 Technical and Tactical Preparedness
3 First Response to a Mass Casualty During a Pandemic
3.1 Rapidity
3.2 Modularity
3.3 Protection of Victims and Health Care Workers
3.4 Non-Specialized Staff Education and Training
3.5 Data, Information, and Communication Channels
4 Maintenance and Adaptation
4.1 Resources Implementation
4.2 Support for Health Care Workers
4.3 Data Analysis and Scientific Production
5 Conclusion
References
15 Social Marketing Contributions to Mitigate Global Epidemics
1 Introduction
2 Social Marketing in Public Health
3 Multidisciplinary Theories and Approaches Used in Social Marketing
4 Social Marketing Mix and Forms of Exchange
5 Social Marketing Appeals
6 Social Marketing Influencers and Digital Media
7 Upstream, Mid-Stream, and Downstream Social Marketing
8 Social Marketing Control
9 Conclusion
Acknowledgements
References
16 The Significance of Super Intelligence of Artificial Intelligence Agencies in the Social Savageries of COVID-19: An Appraisal
1 Introduction
2 Use of AI During COVID-19 Pandemic
3 AI is Used to Recognize, Predict, and Forecast the Path of Outbreaks
3.1 AI for Detection of the Virus and Tracing COVID-19 Real and Unreal Patient
3.2 AI for Rapid Detection of Patient Number by Contact Tracing
3.3 AI for Predicting Patient Number to Take Precaution
3.4 AI in Robotic Cleaning to Prevent Spreading of COVID-19
4 AI for Diagnostic Purposes
4.1 Use of AI in the Development of Drugs, Vaccines, and Technical Check-Ups of Patients and Doctors
4.2 Use of Drones to Deliver Medical Suppliers
4.3 Use of Robots for Sterilizing and Touchless Sanitation
4.4 Use of AI for Money Transaction and Marketing Systems in the Pandemic
4.5 Use of AI in Coronavirus Prevention
5 AI for Social Relation and Emotional Bond During the COVID-19 Pandemic
6 AI for Education, Entertainment, and the Mental Growth During Lockdowns
7 Conclusion
Acknowledgements
References
17 Integration of Sex and Gender Approaches in National Ethics Committees’ Mandate to Appraise COVID-19 Research Protocols: Lessons from West Africa
1 Introduction
2 Theoretical and Methodological Approaches to the ‘Framework’ Development
3 The RESULT of the PROCESS: The Framework
3.1 1st Step
3.2 2nd Step
3.3 3rd Step
4 Future Prospects
5 Conclusion
Acknowledgements
References
18 Psychology, Law, Ethics, Telehealth, and the Global Pandemic
1 Introduction
2 Recent Literature Review
2.1 Ethics
2.1.1 According to the APA
Comment
Practice
2.1.2 Consolidated Model
Comment
2.2 Psychological Services in COVID-19 Times
2.2.1 Flattening the Distress
2.2.2 Tele-Assessments
2.2.3 Guidelines
2.2.4 Comment
2.3 Informed Consent
2.3.1 The APA
2.3.2 The Present Author
3 Conclusion
Acknowledgements
Appendix 1: Informed Consent Explanation for Telehealth/Telepsychology Procedures and Technology
The Risks, Limitations, and Implications of Telehealth/Telepsychology
Informed Consent Form to Sign for Telehealth/Telepsychology
References
19 Physical Inactivity, Sedentarism, and Low Fitness: A Worldwide Pandemic for Public Health
1 Introduction
2 Consequences Associated with Physical Inactivity, Sedentary Behavior, and Physical Fitness
2.1 Physical Inactivity
2.2 Sedentary Behavior
2.3 Physical Fitness
2.4 Interaction of Cardiorespiratory and Musculoskeletal Fitness
3 Temporal Trends in the Prevalence of Physical Activity, Sedentary Behavior, and Fitness Levels
3.1 Physical Inactivity
3.2 Sedentary Behavior
3.3 Physical Fitness
4 Conclusion
Acknowledgements
References
20 The Global Epidemic of Diabesity: Are We Heading for an Unsustainable Future?
1 Introduction
2 The Past, the Present and the Future of Diabesity Epidemiology
3 The Diabesity Epidemic Drivers
4 Social Determinants of Health and Diabesity
5 The Health Impact of Diabesity
6 The COVID-19 Pandemic and Diabesity
7 The Economic Impact of Diabesity
8 Climate Change and Diabesity Epidemic
9 Is the Future Sustainable?
10 What Can Be Done?
11 Conclusion
References
21 Nutrition, Function, and Quality of Life in Older Adults Socially Isolated Due to the COVID-19 Pandemic: A Focus on Telehealth Interventions
1 Introduction
1.1 The COVID-19 Version of Social Isolation
2 Unique Challenges of the COVID-19 Pandemic for Older People
2.1 The Risk to Older Adults
2.2 Compliance with Social Distancing
2.3 Interruption and Deferral of Health Care
2.4 Physical Isolation and Inactivity
2.5 Social Isolation and Perceived Social Isolation
2.6 Ageism
3 Particular Concerns for High-Risk Populations
3.1 Death of Loved Ones
3.2 Worsening Health and Chronic Illnesses
3.3 Sensory Impairment
3.4 Retirement
3.5 Changes in Income
4 Nutrition for Older Adults in COVID-19 Social Isolation: Concerns and Solutions
4.1 The Net Impact of Social Isolation on Nutritional Status
4.2 Acute Impact of Isolation on Nutrition and Hydration
4.3 The Long-Term Impact of Isolation on Nutritional Status and Diet Quality
4.4 Optimizing Food Access for Older People During COVID-19 Isolation
5 Telehealth: Promoting Function, Life Quality, and Nutrition in Older People
5.1 Use of Technology by Older Adults
5.2 Telehealth and Older Adults
5.3 Remote Lifestyle Interventions in Older Adults
5.4 Remote Interventions During COVID-19
5.5 Overcoming Barriers to Telehealth Use in Older Adults
6 Conclusion
Acknowledgements
References
22 Africa’s Response to Pandemics
1 Introduction: African Culture, Religion, and Traditional Medicine
2 COVID-19 and Africa’s Response
3 African Traditional Medicine Versus Western Medicine
4 Conclusion
References
23 Vaccine Hesitancy and Refusal: History, Causes, Mitigation Strategies
1 Introduction
2 Historical Outline of Anti-vaccination Movements
3 Causes of Vaccine Hesitancy
4 Mitigation Strategies: Tackling Vaccine Hesitancy
5 Conclusion
References
24 Vaccine Compliance Versus Trust in a Pandemic
1 Introduction
2 The Danger of the Rescue Narrative
3 Conclusion
References
25 Child Development in a Crisis: A Pandemic
1 Introduction
2 Crisis
2.1 Stages in Crisis
2.1.1 Impact Stage
2.1.2 Coping Phase
2.1.3 Adaptation-Withdrawal Stage
2.2 Features of Crisis
2.2.1 An Unusual Situation
2.2.2 Being a Vulnerable Situation
2.2.3 Existence of Accelerating Factors
2.3 Factors Influential in the Formation of Crises
2.3.1 Familial Factors
2.3.2 Economic Factors
2.3.3 Social Factors
2.3.4 Critical Life Satisfaction
2.3.5 Natural Factors
3 Pandemic: A Crisis
4 Effects of Pandemic on Children
4.1 Infancy Period (0–2 years)
4.2 Early Childhood Period (3–5 years)
4.3 School Period (6–11 years)
4.4 Adolescent Period (12–18 years)
5 Conclusion
References
26 HIV/AIDS Problems and Policies in Adolescent Population
1 Introduction
2 Adolescence and Health
3 Health Problems Among Adolescents
4 HIV/AIDS Trends Among Adolescents Between 2000–2019
5 Risk Factors of HIV/AIDS Among Adolescents
6 HIV/AIDS Intervention, Prevention Program, and Policy for Adolescents
7 Conclusion
References
27 Addressing Needs of Foreign Schoolchildren to Combat a Global Epidemic of Dengue Virus Infection: Transnational and Trans-Sectoral Initiatives
1 Introduction
1.1 Public Education Initiatives in the New Decade
1.2 Global Burden of Mosquito-Borne Dengue on the Rise
1.3 The Study Site: Singapore
1.4 Dengue Virus Infection in Singapore
1.5 Dengue Health Threats Among Children in Singapore
2 The Transnational and Trans-Sectoral Prescription for Anti-Dengue Efforts
2.1 The Japanese Residents: A Demographic Segment Difficult to Conduct Outreach to in Singapore
2.2 Dengue Incidence at a Japanese Primary School in Singapore
2.3 The First Dengue Outreach Exercise at a Japanese Primary School in Singapore
2.4 Results of After-Class Quizzes
2.5 Schoolyard On-Site Inspection
3 Lessons Learnt from the First Outreach Exercise
4 Science Communication for Community Engagement: Project Wolbachia–Singapore
5 Conclusions
Acknowledgements
References
28 Integrated Science of Global Epidemics 2050
1 Introduction
2 How Future Epidemics Are
3 How to Prepare for Future Epidemics
3.1 Artificial Intelligence (AI)
3.2 Computer Science
3.3 Public Health Sociology
3.4 Resilient Health Systems
4 Interventions for Infectious Epidemics
4.1 Multidimensional Approaches
4.2 “One Health”
4.3 International Partnerships
4.4 Telehealth
4.5 Super-Specialized Treatments
4.6 Global Policy Response/Responsibility
5 Other Challenges
5.1 HIV/AIDS
5.2 Africa
5.3 Child Development
5.4 Extraordinary Friction
6 Interventions for Non-infectious Epidemics: Optimal Nutritional Strategies and Physical Activity
7 Conclusion
References
The Puzzle of Integrated Science of Global Epidemics
Index

Citation preview

Integrated Science 14

Nima Rezaei   Editor

Integrated Science of Global Epidemics

Integrated Science Volume 14 Editor-in-Chief Nima Rezaei

, Tehran University of Medical Sciences, Tehran, Iran

The Integrated Science Series aims to publish the most relevant and novel research in all areas of Formal Sciences, Physical and Chemical Sciences, Biological Sciences, Medical Sciences, and Social Sciences. We are especially focused on the research involving the integration of two of more academic fields offering an innovative view, which is one of the main focuses of Universal Scientific Education and Research Network (USERN), science without borders. Integrated Science is committed to upholding the integrity of the scientific record and will follow the Committee on Publication Ethics (COPE) guidelines on how to deal with potential acts of misconduct and correcting the literature.

Nima Rezaei Editor

Integrated Science of Global Epidemics

123

Editor Nima Rezaei Universal Scientific Education and Research Network (USERN) Stockholm, Sweden

ISSN 2662-9461 ISSN 2662-947X (electronic) Integrated Science ISBN 978-3-031-17777-4 ISBN 978-3-031-17778-1 (eBook) https://doi.org/10.1007/978-3-031-17778-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 Chapter 27 is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). For further details see license information in the chapter. This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

This book series would not have been possible without the continuous encouragement of my family. I dedicate this book series to my daughters, Ariana and Arnika, hoping that integrated science could solve complex problems and make a brighter future for the next generation.

Preface

It has been a long time since the first known pandemic occurring to humanity in 165 AD—the Antonine plague—but pandemics did not die but survived, and with their great capacity to evolve, have entered the scene with their new faces and manifestations every few years, being the cause of painful global surprise. Writing this preface, the pandemic COVID is still ongoing, with more than 482 million cases and six million deaths.1 I cannot precisely satisfy your curiosity about how many of my family members, colleagues, and friends contracted COVID; however, I can answer: indeed, all, with most, were once tested positive. Regretfully, I can one-by-one say who I have lost during this pandemic; for none of them, I had the opportunity to mourn, to express and share my sadness with others, and to hug someone as I used to. Quarantine, face masks, and social distancing did not allow me to be the one I liked to be. These measures were the only way to prevent disease transmission and have shown that they definitely work if implemented correctly. I do, however, continue asking myself what about the future pandemic? Should we see another global epidemic to kill humans? Wait and watch has not been a good idea to deal with global epidemics. It was proved during the pandemic COVID. But, integrated efforts are necessary to study epidemics to, for example, forecast epidemics, design multi-level surveillance, monitor the current situation, analyze the temporospatial spread of disease and identify transmission patterns, assess the impact of the epidemic on health in the post-epidemic world to mitigate the hazards and better manage the post-epidemic world, modeling environment-individual behaviors relation to evaluate their influence on epidemics, develop diagnostic tests, reduce health inequalities, detect the emergence of zoonotic pathogens early, optimize communication strategies for education, run the prevention measures, etc.

1

https://www.worldometers.info/coronavirus/.

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Preface

As a part of this effort, Integrated Science of Global Epidemics hopes to eliminate the pain of future epidemics and reduce that of ongoing epidemics. Tehran, Iran April 2022

Nima Rezaei, M.D., Ph.D.

Acknowledgment I would like to express my gratitude to the Editorial Assistant of this book series, Dr. Amene Saghazadeh. Without a doubt, the book would not have been completed without her contribution.

Contents

1

Introduction to Integrated Science of Global Epidemics . . . . . . . . . Nima Rezaei and Amene Saghazadeh

1

2

Emerging Viral Infections in Human Population . . . . . . . . . . . . . . Anyebe Bernard Onoja

19

3

One Health as an Integrated Approach: Perspectives from Public Services for Mitigation of Future Epidemics . . . . . . . . Sandul Yasobant, Ana Maria Perez Arredondo, Jéssica Francine Felappi, Joshua Ntajal, Juliana Minetto Gellert Paris, Krupali Patel, Merveille Koissi Savi, Dennis Schmiege, and Timo Falkenberg

4

5

47

A Multipronged Approach to Combat COVID-19: Lessons from Previous Pandemics for the Future . . . . . . . . . . . . . . . . . . . . Barbara W. K. Son

73

Response to Disease Outbreaks in Africa: A Call to Build Resilient Health Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Juliet Nabyonga-Orem, James Avoka Asamani, and Hillary Kipruto

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6

Navigating Global Public Influenza Surveillance Systems for Reliable Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Ryan B. Simpson, Jordyn Gottlieb, Bingjie Zhou, Shiwei Liang, Xu Jiang, Meghan A. Hartwick, and Elena N. Naumova

7

Three Respiratory Syndrome Epidemics (SARS, MERS, COVID-19) in Two Decades: Clinical Epidemiological Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Viroj Wiwanitkit

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Epidemic in Complex Networks and Some Ideas About the Impact of Isolation Strategies in the Context of the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Carlos Rodríguez Lucatero

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Contents

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Optimal Control: Application and Applicability in Times of Pandemics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Ilias Elmouki, Ling Zhong, Abdelilah Jraifi, and Aziz Darouichi

10 Analysis of a COVID-19 Model Implementing Social Distancing as an Optimal Control Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Sangeeta Saha and G. P. Samanta 11 Impact of Policy Implementations on the Propagation of COVID-19 Before Vaccine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Tapen Sinha 12 Environmental Factors Associated with Global Pandemic Transmission and Morbidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Nadim Sharif and Shuvra Kanti Dey 13 Urbanization and the Epidemiology of Infectious Diseases: Towards the Social Framing of Global Responses . . . . . . . . . . . . . 307 Edlyne E. Anugwom and Kenechukwu N. Anugwom 14 Pandemics and Mass Casualties: Cornerstones of Management . . . 329 Federico Coccolini, Enrico Cicuttin, Dario Tartaglia, Camilla Cremonini, and Massimo Chiarugi 15 Social Marketing Contributions to Mitigate Global Epidemics . . . . 347 Beatriz Casais and João F. Proença 16 The Significance of Super Intelligence of Artificial Intelligence Agencies in the Social Savageries of COVID-19: An Appraisal . . . . 361 Kabita Das, Manaswini Pattanaik, and Biswaranjan Paital 17 Integration of Sex and Gender Approaches in National Ethics Committees’ Mandate to Appraise COVID-19 Research Protocols: Lessons from West Africa . . . . . . . . . . . . . . . . . . . . . . . 383 Guillermo Z. Martínez-Pérez 18 Psychology, Law, Ethics, Telehealth, and the Global Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Gerald Young 19 Physical Inactivity, Sedentarism, and Low Fitness: A Worldwide Pandemic for Public Health . . . . . . . . . . . . . . . . . . . 429 Javier Bueno-Antequera and Diego Munguía-Izquierdo 20 The Global Epidemic of Diabesity: Are We Heading for an Unsustainable Future? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 Sarah Cuschieri and Stephan Grech

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21 Nutrition, Function, and Quality of Life in Older Adults Socially Isolated Due to the COVID-19 Pandemic: A Focus on Telehealth Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 Kathryn N. Porter Starr, Marshall G. Miller, Nia S. Mitchell, and Connie W. Bales 22 Africa’s Response to Pandemics . . . . . . . . . . . . . . . . . . . . . . . . . . . 489 Kevin Y. Njabo 23 Vaccine Hesitancy and Refusal: History, Causes, Mitigation Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503 Alessandro Siani 24 Vaccine Compliance Versus Trust in a Pandemic . . . . . . . . . . . . . . 519 John Stone 25 Child Development in a Crisis: A Pandemic . . . . . . . . . . . . . . . . . . 529 Neriman Aral and Gül Kadan 26 HIV/AIDS Problems and Policies in Adolescent Population . . . . . . 545 Ni Komang Yuni Rahyani 27 Addressing Needs of Foreign Schoolchildren to Combat a Global Epidemic of Dengue Virus Infection: Transnational and Trans-Sectoral Initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563 Minako Jen Yoshikawa, Atsuo Hamada, and Christina Liew 28 Integrated Science of Global Epidemics 2050 . . . . . . . . . . . . . . . . . 587 Nima Rezaei, Amene Saghazadeh, Abdelilah Jraifi, Alessandro Siani, Ana Maria Perez Arredondo, Anyebe Bernard Onoja, Atsuo Hamada, Aziz Darouichi, Barbara W. K. Son, Beatriz Casais, Bingjie Zhou, Biswaranjan Paital, Camilla Cremonini, Carlos Rodríguez Lucatero, Christina Liew, Connie W. Bales, Dario Tartaglia, Dennis Schmiege, Diego Munguía-Izquierdo, Edlyne E. Anugwom, Elena N. Naumova, Enrico Cicuttin, Federico Coccolini, G. P. Samanta, Gerald Young, Guillermo Z. Martínez-Pérez, Gül Kadan, Hillary Kipruto, Ilias Elmouki, James Avoka Asamani, Javier Bueno-Antequera, Jéssica Francine Felappi, João F. Proença, John Stone, Jordyn Gottlieb, Joshua Ntajal, Juliana Minetto Gellert Paris, Juliet Nabyonga-Orem, Kabita Das, Kathryn N. Porter Starr, Kenechukwu N. Anugwom, Kevin Y. Njabo, Krupali Patel, Ling Zhong, Manaswini Pattanaik, Marshall G. Miller, Massimo Chiarugi, Meghan A. Hartwick, Merveille Koissi Savi, Minako Jen Yoshikawa, Nadim Sharif, Neriman Aral, Ni Komang Yuni Rahyani, Nia S. Mitchell, Ryan B. Simpson, Sandul Yasobant, Sangeeta Saha, Sarah Cuschieri, Shiwei Liang, Shuvra Kanti Dey, Stephan Grech, Tapen Sinha, Timo Falkenberg, Viroj Wiwanitkit, and Xu Jiang

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The Puzzle of Integrated Science of Global Epidemics. . . . . . . . . . . . . . . 609 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611

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Introduction to Integrated Science of Global Epidemics Nima Rezaei and Amene Saghazadeh

During this pandemic, the most vulnerable have been the hardest hit … We must increase our resilience. We must work together and take an integrated approach to health, hunger, climate, and equity crisis — no one is safe from COVID-19 until everyone is safe. Volkan Bozkir

Summary

An introduction to the content of Integrated Science of Global Epidemics is presented in this chapter, mainly including the epidemiology of respiratory viral infectious epidemics—with emphasis on COVID, important concerns and challenges, e.g., urbanization, mass casualties, discrimination issues, and psychological assessment limitations, important facilitators, i.e., social marketing and artificial intelligence, advancing epidemics of physical inactivity and obesity, and children and adolescents’ health in relation to global epidemics.

N. Rezaei (&)
A. Saghazadeh Integrated Science Association (ISA), Universal Scientific Education and Research Network (USERN), Tehran, Iran e-mail: [email protected] Research Center for Immunodeficiencies, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran N. Rezaei Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_1

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Graphical Abstract/Art Performance

Integrated science of global epidemics (Adapted with permission from the Health and Art (HEART), Universal Scientific Education and Research Network (USERN); Painting by Lavin Hasanzadeh Oskoei)

The code of this chapter is 01101110 01100101 01110100 01100001 01100101 01100100 01100111 01001001 01110010 01110100. Keywords

Global epidemics

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Integrated science

Introduction

1.1 No Matter Silent or Noisy Pandemics can be noisy or silent. Pandemics are noisy events as they happen to humanity suddenly, propagate on a large scale, are often associated with a high transmission rate, kill many people, cause people to be socially isolated with the hope of halting the chain of transmission of the disease, and have no definite medical treatment and prevention. Looking in history, the noisiest pandemics have been HIV/AIDS, the third plague, COVID, with around 25–35 million, 12 million,

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and six million deaths to the present time, of which HIV/AIDS and COVID are still ongoing.1 Pandemics can be, however, silent and cause the burden as bad and even worse than noisy pandemics. Obesity, metabolic syndrome, and type 2 diabetes are the prototype of such pandemics. These conditions are known as cardiovascular risk factors, accounting for a substantial burden of non-communicable diseases and economic consequences [1]. Regardless of how loud they are, global epidemics are accompanied by socioeconomic consequences; yet challenging in many, still different respects for tackling, prevention, and treatment.

1.2 The Problem: Health Inequality Health is related to the multiple dimensions of effective life in terms of the structure, function, and emotion one experiences as an individual or as a society’s member. Health inequalities are differences that populations, groups, or organizations, present with regards to health-related outcomes. At the level of population, health is referred to as “the average, distribution and inequalities in health within a society” [2]. To reduce health inequalities and improve population health, many integrated approaches have been proposed. To work for their success is, however, a long path, that begins necessarily with understanding health inequalities and their political, economic, and cultural dimensions and involves collaboration not only between local organizations involved, but also between these organizations and citizens [3, 4]. The emergence of a pandemic worsens the situation with health inequalities, threatening the global health security [5]. To be compatible, surveillance systems need to incorporate epidemiological data as well sociodemographic data, identify risk factors, consider social determinants of health, and estimate the effect of different policies by modeling and simulating. Along with all, social sciences integration is a key element. For example, through international analyses, a global health inequality is in adult and child mortality [6]. Countries with a high child mortality occur mainly in Africa and are more likely to suffer from extreme poverty, female illiteracy, and lack of access to safe water, sanitation, and immunizations. Also, they have higher numbers of people living in rural areas, whereas they are estimated to spend less per capita expenditure on healthcare. Multivariate analyses identified HIV/AIDS as associated with high adult mortality. These findings support that to solve the problem with health inequalities, there is a variety of social, economic, and environmental to be considered. This implies the importance of integrated science for international problems.

1.3 One World, One Epidemic, One Health Global epidemics are the diseases of the world, so their surveillance approaches and systems should reflect that. This is the focus of five chapters opening the Integrated 1

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Science of Global Epidemics volume. Emerging viral infections and related reservoirs, transmission dynamics, and contributory factors are discussed from the “One Health” perspective to justify them as health challenges that have involved the whole world [7] and so call for public health collaboration, risk assessment, and preventive and control measures at both national and international levels (this chapter). Following the same approach, the need for protection at all human, animal, and environmental levels is pronounced, given that emerging infectious epidemics arise from animals, of course, in association with human activities to the environment [8]. Therefore, preparedness for global epidemics involves public health services like food, education, water and sanitation, green spaces, etc. (Chap. 2). Moreover, the management of a viral epidemic comprises screening and diagnosing, contact tracing, social distancing, isolation, therapeutics, vaccines, transparent communication, healthcare authorities, and pandemic decision-making. These need to be coordinated in a multipronged approach (Chap. 4), bringing together a resilient health system (Chap. 5) that is responsive and take actions effective enough to manage a newly emergent epidemic [9]. Such a system crucially requires a forecasting system that works in a valid, reliable, and updated fashion (Chap. 6) and can remain active in a sustainable manner beyond the time of an epidemic to prepare for future epidemics and adopt changes once needed [10–12].

1.4 Coronavirus Epidemics: More than One per Decade Three infectious respiratory epidemics due to coronaviruses happened to humanity over the last two decades: severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and coronavirus disease (COVID) (Chap. 7). Data on the epidemiological aspects of these epidemics would be useful for modeling purposes to draw the transmission dynamics of the disease, identify major factors contributing to the disease transmission and mortality [13], and accordingly determine the control measures of potential effect [14, 15]. For example, during the SARS epidemic, the clues were: healthcare workers, comprising more than 20% of cases; age, presenting with noticeably different manifestations in children and older people; and origin, wild animal [15]. Moreover, reviewing lessons we have learned, there have been mainly about the shock and fear that the medical services experienced and how they did resource allocation, the responses different communities provided, the molecular evolution of the pathogen—i.e., a coronavirus—during the epidemic period, and the consequences in the post-epidemic world, in particular, with regards to the national and world economy, education, and mental and physical health [16–23]. Simulating epidemic scenarios would allow to measure the impact of human movement and geographic mobility on the disease transmission and evaluate it in relation to public health control interventions [24]. These simulations can help understand epidemic projections and forecasting [25]. The function of different strategies is tested using simulations to determine which are the optimal control measures, with COVID being the epidemic of interest. Approaches used in the modeling are a dynamical system approach that

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observes virus propagation in complex social networks (Chap. 8) and can analyze it over various spatiotemporal scales (Chap. 9). Consequently, one might conclude that no single social distancing measure operates optimal, but a combination of measures is required to combat a global epidemic (Chap. 10). Implementing these measures to manage human mobility is of utmost importance for containing an ongoing epidemic. It will be well-represented when comparing the impact of different national health policies on the outcome of COVID in different countries (Chap. 11). At the same time, a wide range of environmental factors have been reported to affect disease transmission and need to be sufficiently controlled to prevent the next epidemic (Chap. 12).

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Important Concerns and Challenges

2.1 Urbanization Urbanization is a hazard being the source of disease occurrence, transmission, and maintenance (Chap. 13). Urbanization accompanies the formation of new megacities that pave the way for the development and transmission of emerging infectious epidemics [26]. In the case of influenza, cities with higher population densities revealed a more diffuse epidemic in terms of an extended season of influenza compared to those with lower population densities [27]. Urbanization has also caused a transition in lifestyles, notably being associated with increased adoption of sedentary behaviors and nutritional strategies that are high in lipids—explaining the epidemic of obesity that both children and adults are increasingly experiencing [28, 29]. Therefore, urbanization has been the origin of global epidemics of both communicable and non-communicable diseases [30, 31].

2.2 Mass Casualties Mass gatherings potentially contribute to the transmission of infections. Such a gathering is the Hajj. Mass casualties are events inevitably occurring during disaster situations (Chap. 14)—when professionals are committed to screening, diagnosing, and treating people in low-resources settings—which are, in turn, due to mass gatherings. Management of mass casualties is an important public health issue [32].

2.3 Discrimination Issues Doing research on global epidemics is challenged with discrimination issues. These issues are different, including stigma due to, for example, HIV/AIDS (Chap. 26) and other sexually transmitted diseases, prejudice, gender and gender-based abuse,

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etc. [33–38]. There is a need to provide a context to incorporate sex and gender approaches in global epidemic research (Chap. 17).

2.4 Psychological Assessment Limitations Given numerous psychological assessment limitations during global epidemics, telepsychology and telehealth, in general, have been recommended by psychologists and psychiatrists. Though it is in the first steps—with being of high interest in 2020 following the emergence of the COVID pandemic [39–41], telepsychology offers unique opportunities; it can provide mental health services that are available, accessible, and affordable to both patients and practitioners [42] as well as to both children and adults [43, 44]. Although it is very demanding in the digital age, there are barriers to using it ethically and effectively, highlighting the need for further research [45] to ensure the continuity of psychological assessments during epidemics (Chap. 18).

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Important Facilitators

3.1 Social Marketing Social marketing strategies can apply to all national, regional, and global levels and help behavioral changes to help epidemic prevention and global health promotion (Chap. 15) [46]. In particular, they have been incorporated into HIV/AIDS prevention programs, stop smoking aids, human rights protection programs, etc. [47– 49]. For example, in the context of COVID, people’s responses to epidemics might be traditional to prevent them from accepting the importance of medicines in the treatment of epidemics (Chap. 22) and vaccines in elimination and prevention (Chaps. 23 and 24). Social marketing was thought to foster behavioral changes to reduce the disease spread, overcome vaccine hesitancy, and improve social responsibility [50–52]. Therefore, social marketing has been a subject of interest in public health, with multidisciplinary theories and approaches, forms of exchange, and appeals being available to facilitate its optimization and implementation.

3.2 Artificial Intelligence Artificial intelligence-based technologies have provided us with great opportunities to deal with a global epidemic, mainly through public health surveillance, tracking human behaviors, modeling epidemic scenarios, as well as building social marketing platforms (Chap. 16) [53–55]. To combat COVID, artificial intelligence could apply to different study scales to discover new medicines or repurpose old drugs, detect and predict cases, diagnose or confirm diagnosed cases, assess disease

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prognosis based on clinical and paraclinical data, and map the flow of information from the epidemic to the infodemic [56, 57]; it was the public health professionals’ right hand.

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Physical Inactivity and Obesity

These advancing epidemics are concerning in terms of the vulnerability to chronic non-communicable diseases, the economic costs, and the increasing prevalence in children and adolescents [58, 59]. There is a large heterogeneity across countries with regards to the prevalence of physical inactivity ranging from about 2% in Comoros to 71% in Mauritania, occurring more in older adults and urban areas [60]. Physical inactivity accounts for more than five million deaths annually [61]. Obesity and type 2 diabetes are the aftermaths of physical inactivity that have shown a noticeable increase in the global prevalence in all, including children, female adults, and male adults [62]. Prevention and management of these epidemics, therefore, requires an integrated approach. In this volume, physical inactivity, sedentarism, and low fitness are reviewed as global epidemics that affect cardiovascular and musculoskeletal fitness (Chap. 19). Similarly, obesity and diabesity are discussed as another epidemic (Chap. 20). The issue with these existing non-infectious epidemics is complicated more than ever when a viral epidemic is added, like COVID (Chaps. 19 and 20), and therefore, public control measures are implemented that might prevent people from outdoor activities. To mitigate the effect of this harmful interaction, physical activity, and healthy nutrition need to be planned, especially in vulnerable populations, such as older adults (Chap. 21).

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Global Epidemics and Children and Adolescents’ Health

Children and adolescents are a vulnerable population in global epidemics.

5.1 The Impact of a Pandemic on children’s Development Importantly, global epidemics are crises that might negatively influence different developmental stages, from the infancy and early childhood period to the school and adolescent period. Different factors mediate the impact of a crisis on children’s development, which mainly include familial, economic, social, and natural factors (Chap. 25).

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5.2 HIV/AIDS Among Adolescents Moreover, HIV/AIDS happens especially to female adolescents with high transmission rates, mortality, and morbidity (Chap. 26). The risk factors vary across populations; specific to which, HIV/AIDS interventions, prevention programs, and policies need to be made.

5.3 Education The third is about problems foreign children might face in school and with regard to educational goals during a global epidemic (Chap. 27). All these challenges exist along with a demand for attention and action from the whole society, an integrated approach that comprises different strategies for prevention and treatment, and cross-cultural and transactional initiatives for education.

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Before Experience

A few opportunities provided with integrated efforts for COVID, obesity, and HIV/AIDS are given.

6.1 Integrated Science for COVID Early when the COVID emerged, scientists from different fields expressed their opinion that the integrated science is essential for knowledge systems to become pleural [63], enabling us to combat this global epidemic [64]. Below are only two important products of this integration.

6.1.1 Integrated Diagnostics Sensor Systems These systems apply to diagnosis with a range of capacities, importantly in terms of cost-effectiveness and short turnover time [65]. Being combined with smart materials, their performance increases even further to yield a higher sensitivity, specificity, and a linear dynamic range. In the context of COVID, diagnostic strategies initially available included polymerase chain reaction (PCR)-based systems and serological assays. The smart materials-integrated sensors, e.g., nanobiosensors and electrochemical biosensors, could offer an earlier and more accurate tool for COVID diagnosis [66, 67]. Using artificial intelligence and internet of things, these sensors will be suitable for consideration as point-of-care tests [68]. Also, in the wearable form, these artificial intelligence-assisted biosensors can serve as personal healthcare providers for remote health monitoring [69, 70].

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Imaging To improve diagnostic accuracy, a combination of imaging modalities, including computed tomography, chest X-ray, and ultrasound is recommended [71]. Artificial intelligence-based methods, machine learning, and deep learning then can apply to develop diagnostic algorithms [72, 73]—that is called as radiomics.

6.1.2 Integrated Healthcare System Healthcare systems were challenged with the COVID pandemic due to uncertainties in many respects, including, but not limited to, transmission, infrastructure, supplies, resources, and workforce [74]. To prevent healthcare collapse, it was, therefore, required to manage virtual care in practice that is telemedicine by considering the digital advantage. 6.1.3 Integrated Education System The control measures implemented during the COVID pandemic problematized the education platforms, too [75]. To maintain learning and education, models and systems integrating distance and blended learning, students’ activities, online and remote leaning, and work and learning were, then, implemented [76–78]. 6.1.4 Integrated Recovery Care COVID care is totally integrated. It comprises numerous hubs [79]. For example, post-COVID mental health care is provided by psychiatrists, psychologists, and liaison and community services; care in the community by general practitioners, community psychologists, and physiotherapists. 6.1.5 Integrated Mental Health Interventions The COVID pandemic was not only a pandemic of the physical health, but its negative effects to mental health have also been well documented, in particular emotional distress, so the world has witnessed a crisis of both physical health and mental health. To intervene with such a crisis, efforts are necessary to integrate, for example, behavioral health into public health responses [80], mobile app-based interventions into mental health services [81], mental health services with social services [82], arts into mental health care [83], and primary care practice into mental health care [84]. 6.1.6 Integrated Understanding Pathogenesis COVID manifested as a multi-system disease, which correlates with changes to different scenarios of pathogenesis. Most importantly, immune response abnormalities commonly occur leading to hyperinflammation and autoimmunity. Moreover, metabolic changes are also seen in patients with COVID. Endocrine effects appear to play a role in COVID, as demonstrated in studies linking the biological sex to COVID outcomes [85]. Host–pathogen interactions could be elaborated better though the analysis of multiomics data [86, 87]. To understand COVID pathogenesis more fully requires an integrated profiling.

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6.1.7 Integrated Environmental and Medical Science The environment, of course under human actions, provided the way for the emergence of COVID; on the other hand, COVID has deeply affected the environment, society, and economy [88, 89]. Only an integrated approach can assess all these related crises, highlight public health opportunities [90], and provide solutions to manage the uncertain environment for a sustainable future.

6.2 Integrated Science for Obesity This epidemic—the today’s challenge—calls for integrated science for different purposes. Obesity is the result of genetic and environmental factors [91] associated with different chronic diseases. Models for its prevention and treatment are, therefore, integrated to involve both clinical and community systems [92]. Environmental approaches are also provided for prevention of both obesity and eating disorders. These approaches are mainly based on behavioral change rather than weight reduction as the focus of an obesity prevention program [93]. Multi-level approaches are also integrated in terms of considering both the individual and the environment. They aim to provide obesity prevention in a sustainable manner and, as their name implies, are applicable to the different levels of prevention, from achieving the goal that are effective responses to maintaining them [94]. Systematic reviews support the notion that an integrated program is crucial for obesity management. Such a program will succeed better if contains nutritional strategies and diet tips, physical activity, and lifestyle behaviors-modifying strategies [95].

6.3 Integrated Science for HIV/AIDS For this ongoing viral epidemic, there has long been a need for different integrated responses to care about mental health, provide maternal, neonatal, and child health services [96, 97], implement a platform for electronic medical record and public health information exchange [98], and combine HIV/AIDS care with primary care [99]. Particularly, for children and adolescents, who made up a population vulnerable to HIV/AIDS, an integrated strategy is required to enhance motivation to learn about HIV/AIDS, provide relevant education, and therefore lead to behavioral change suitable for HIV/AIDS prevention [100].

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Conclusion

As for other volumes of the Integrated Science book series,2 finally, an Expert Opinion chapter, Chap. 28, has been prepared, gathering authors’ views about how would be the future of integrated science for global epidemics. Welcome to Integrated Science of Global Epidemics. Core Messages

• Coronaviruses have caused more than one epidemic per decade during the last two decades. • Data on the epidemiological aspects of epidemics would be useful for modeling and simulation purposes. • Important concerns and challenges include urbanization, mass casualties, discrimination issues, and psychological assessment limitations. • Important facilitators are social marketing and artificial intelligence. • Epidemics of physical inactivity and obesity are in progress.

References 1. Yach D, Stuckler D, Brownell KD (2006) Epidemiologic and economic consequences of the global epidemics of obesity and diabetes. Nat Med 12(1):62–66 2. McCartney G, Popham F, McMaster R, Cumbers A (2019) Defining health and health inequalities. Public Health 172:22–30. https://doi.org/10.1016/j.puhe.2019.03.023 3. Wilderink L, Bakker I, Schuit AJ, Seidell JC, Renders CM (2020) Key elements of a successful integrated community-based approach aimed at reducing socioeconomic health inequalities in the Netherlands: a qualitative study. PLoS ONE 15(10):e0240757 4. Navarro V, Muntaner C (2020) Introduction: toward an integrated political, economic, and cultural understanding of health inequalities. In: Political and economic determinants of population health and well-being. Routledge, pp 1–6 5. Quinn SC, Kumar S (2014) Health inequalities and infectious disease epidemics: a challenge for global health security. Biosecur Bioterror 12(5):263–273 6. Ruger JP, Kim H-J (2006) Global health inequalities: an international comparison. J Epidemiol Community Health 60(11):928–936 7. Calistri P, Iannetti S, Danzetta LM, Narcisi V, Cito F, Di Sabatino D, Bruno R, Sauro F, Atzeni M, Carvelli A (2013) The components of ‘one world–one health’ approach. Transbound Emerg Dis 60:4–13 8. Mackenzie JS, Jeggo M (2019) The one health approach—why is it so important?, vol 4. Multidisciplinary Digital Publishing Institute 9. Durski KN, Osterholm M, Majumdar SS, Nilles E, Bausch DG, Atun R (2020) Shifting the paradigm: using disease outbreaks to build resilient health systems. BMJ Glob Health 5(5): e002499 2

https://www.springer.com/series/16554.

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10. Abdolazimi O, Esfandarani MS, Salehi M, Shishebori D, Shakhsi-Niaei M (2021) Development of sustainable and resilient healthcare and non-cold pharmaceutical distribution supply chain for COVID-19 pandemic: a case study. Int J Logist Manag 11. El Bcheraoui C, Weishaar H, Pozo-Martin F, Hanefeld J (2020) Assessing COVID-19 through the lens of health systems’ preparedness: time for a change. Glob Health 16(1):1–5 12. Gebremeskel AT, Otu A, Abimbola S, Yaya S (2021) Building resilient health systems in Africa beyond the COVID-19 pandemic response. BMJ Spec J 6 13. Anderson RM, Fraser C, Ghani AC, Donnelly CA, Riley S, Ferguson NM, Leung GM, Lam TH, Hedley AJ (2004) Epidemiology, transmission dynamics and control of SARS: the 2002–2003 epidemic. Philos Trans R Soc Lond B Biol Sci 359(1447):1091–1105 14. Knock ES, Whittles LK, Lees JA, Perez-Guzman PN, Verity R, FitzJohn RG, Gaythorpe KAM, Imai N, Hinsley W, Okell LC (2021) Key epidemiological drivers and impact of interventions in the 2020 SARS-CoV-2 epidemic in England. Sci Transl Med 13 (602):eabg4262 15. Xu R-H, He J-F, Evans MR, Peng G-W, Field HE, Yu D-W, Lee C-K, Luo H-M, Lin W-S, Lin P (2004) Epidemiologic clues to SARS origin in China. Emerg Infect Dis 10(6):1030 16. Lau JTF, Yang X, Tsui H, Kim JH (2003) Monitoring community responses to the SARS epidemic in Hong Kong: from day 10 to day 62. J Epidemiol Community Health 57 (11):864–870 17. Wong TW, Gao Y, Tam WWS (2007) Anxiety among university students during the SARS epidemic in Hong Kong. Stress Health J Int Soc Invest Stress 23(1):31–35 18. Feng D, De Vlas SJ, Fang LQ, Han XN, Zhao WJ, Sheng S, Yang H, Jia ZW, Richardus JH, Cao WC (2009) The SARS epidemic in mainland China: bringing together all epidemiological data. Trop Med Int Health 14:4–13 19. Huang Y (2004) The SARS epidemic and its aftermath in China: a political perspective. In: Learning from SARS: preparing for the next disease outbreak, pp 116–136 20. Chinese SMEC (2004) Molecular evolution of the SARS coronavirus during the course of the SARS epidemic in China. Science 303(5664):1666–1669 21. Chang H-J, Huang N, Lee C-H, Hsu Y-J, Hsieh C-J, Chou Y-J (2004) The impact of the SARS epidemic on the utilization of medical services: SARS and the fear of SARS. Am J Public Health 94(4):562–564 22. Hung LS (2003) The SARS epidemic in Hong Kong: what lessons have we learned? J R Soc Med 96(8):374–378 23. Dye C, Gay N (2003) Modeling the SARS epidemic. Science 300(5627):1884–1885 24. Huang C-Y, Sun C-T, Hsieh J-L, Lin H (2004) Simulating SARS: small-world epidemiological modeling and public health policy assessments. J Artif Soc Soc Simul 7(4) 25. Massad E, Burattini MN, Lopez LF, Coutinho FAB (2005) Forecasting versus projection models in epidemiology: the case of the SARS epidemics. Med Hypotheses 65(1):17–22 26. Neiderud C-J (2015) How urbanization affects the epidemiology of emerging infectious diseases. Infect Ecol Epidemiol 5(1):27060 27. Dalziel BD, Kissler S, Gog JR, Viboud C, Bjørnstad ON, Metcalf CJE, Grenfell BT (2018) Urbanization and humidity shape the intensity of influenza epidemics in US cities. Science 362(6410):75–79 28. Brody J (2002) The global epidemic of childhood obesity: poverty, urbanization, and the nutrition transition. Nutr Bytes 8(2) 29. Akpan EE, Ekpenyong CE (2013) Urbanization drift and obesity epidemic in sub-Saharan Africa: a review of the situation in Nigeria. Eur J Sustain Dev 2(2):141–141 30. Kuddus A, Rahman A (2015) Affect of urbanization on health and nutrition. Int J Stat Syst 10(2):164–174 31. Wu T, Perrings C, Kinzig A, Collins JP, Minteer BA, Daszak P (2017) Economic growth, urbanization, globalization, and the risks of emerging infectious diseases in China: a review. Ambio 46(1):18–29

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32. Memish ZA, Steffen R, White P, Dar O, Azhar EI, Sharma A, Zumla A (2019) Mass gatherings medicine: public health issues arising from mass gathering religious and sporting events. The Lancet 393(10185):2073–2084 33. Parker R, Aggleton P (2007) HIV-and AIDS-related stigma and discrimination: a conceptual framework and implications for action. In: Culture, society and sexuality. Routledge, pp 459–474 34. Parker R, Aggleton P, Attawell K, Pulerwitz J, Brown L (2002) HIV/AIDS-related stigma and discrimination: a conceptual framework and an agenda for action 35. Parker R (2012) Stigma, prejudice and discrimination in global public health. Cad Saude Publica 28(1):164–169 36. Gilmore N, Somerville MA (1994) Stigmatization, scapegoating and discrimination in sexually transmitted diseases: overcoming ‘them’ and ‘us.’ Soc Sci Med 39(9):1339–1358 37. Heise L (1994) Gender-based abuse: the global epidemic. Cad Saude Publica 10:S135–S145 38. Türmen T (2003) Gender and HIV/aids. Int J Gynecol Obstet 82(3):411–418 39. Moazzami B, Razavi-Khorasani N, Moghadam AD, Farokhi E, Rezaei N (2020) COVID-19 and telemedicine: immediate action required for maintaining healthcare providers well-being. J Clin Virol 126:104345 40. Tonkaboni A, Ziaei H, Rezaei N (2020) Teledentistry during COVID-19. Authorea Preprints 41. Azadnajafabad S, Saeedi Moghaddam S, Rezaei N, Ghasemi E, Naderimagham S, Azmin M, Mohammadi E, Jamshidi K, Fattahi N, Zokaei H (2021) A report on statistics of an online self-screening platform for COVID-19 and its effectiveness in Iran. Int J Health Policy Manag 42. McCord C, Bernhard P, Walsh M, Rosner C, Console K (2020) A consolidated model for telepsychology practice. J Clin Psychol 76(6):1060–1082 43. Slone NC, Reese RJ, McClellan MJ (2012) Telepsychology outcome research with children and adolescents: a review of the literature. Psychol Serv 9(3):272 44. Nelson EL, Bui T (2010) Rural telepsychology services for children and adolescents. J Clin Psychol 66(5):490–501 45. Drum KB, Littleton HL (2014) Therapeutic boundaries in telepsychology: unique issues and best practice recommendations. Prof Psychol Res Pract 45(5):309 46. Firestone R, Rowe CJ, Modi SN, Sievers D (2017) The effectiveness of social marketing in global health: a systematic review. Health Policy Plan 32(1):110–124 47. Thrasher JF, Huang L, Pérez-Hernández R, Niederdeppe J, Arillo-Santillán E, Alday J (2011) Evaluation of a social marketing campaign to support Mexico City’s comprehensive smoke-free law. Am J Public Health 101(2):328–335 48. Pfeiffer J (2004) Condom social marketing, Pentecostalism, and structural adjustment in Mozambique: a clash of AIDS prevention messages. Med Anthropol Q 18(1):77–103 49. Odigbo B, Eze F, Odigbo R (2020) COVID-19 lockdown controls and human rights abuses: the social marketing implications. Emerald Open Res 2 50. Evans WD, French J (2021) Demand creation for COVID-19 vaccination: overcoming vaccine hesitancy through social marketing. Vaccines 9(4):319 51. Lee NR (2020) Reducing the spread of COVID-19: a social marketing perspective. Soc Mark Q 26(3):259–265 52. Cho H, Guo Y, Torelli C (2021) Collectivism fosters preventive behaviors to contain the spread of COVID‐19: implications for social marketing in public health. Psychol Mark 53. Ganasegeran K, Abdulrahman SA (2020) Artificial intelligence applications in tracking health behaviors during disease epidemics. In: Human behaviour analysis using intelligent systems. Springer, pp 141–155 54. Zeng D, Cao Z, Neill DB (2021) Artificial intelligence-enabled public health surveillance— from local detection to global epidemic monitoring and control. In: Artificial intelligence in medicine. Elsevier, pp 437–453

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55. Ogden NH, Fazil A, Arino J, Berthiaume P, Fisman DN, Greer AL, Ludwig A, Ng V, Tuite AR, Turgeon P (2020) Artificial intelligence in public health: modelling scenarios of the epidemic of COVID-19 in Canada. Can Commun Dis Rep 46(8):198 56. Bullock J, Luccioni A, Pham KH, Lam CSN, Luengo-Oroz M (2020) Mapping the landscape of artificial intelligence applications against COVID-19. J Artif Intell Res 69:807– 845 57. Raza K (2020) Artificial intelligence against COVID-19: a meta-analysis of current research. In: Big data analytics and artificial intelligence against COVID-19: innovation vision and approach, pp 165–176 58. González K, Fuentes J, Márquez JL (2017) Physical inactivity, sedentary behavior and chronic diseases. Korean J Fam Med 38(3):111 59. Katzmarzyk PT, Janssen I (2004) The economic costs associated with physical inactivity and obesity in Canada: an update. Can J Appl Physiol 29(1):90–115 60. Guthold R, Ono T, Strong KL, Chatterji S, Morabia A (2008) Worldwide variability in physical inactivity: a 51-country survey. Am J Prev Med 34(6):486–494 61. Lee IM, Bauman AE, Blair SN, Heath GW, Kohl HW, Pratt M, Hallal PC (2013) Annual deaths attributable to physical inactivity: whither the missing 2 million? The Lancet 381 (9871):992–993 62. Jaacks LM, Vandevijvere S, Pan A, McGowan CJ, Wallace C, Imamura F, Mozaffarian D, Swinburn B, Ezzati M (2019) The obesity transition: stages of the global epidemic. Lancet Diabetes Endocrinol 7(3):231–240 63. El-Hani CN, Machado V (2020) COVID-19: the need of an integrated and critical view. Ethnobiol Conserv 9 64. Moradian N, Ochs HD, Sedikies C, Hamblin MR, Camargo CA, Martinez JA, Biamonte JD, Abdollahi M, Torres PJ, Nieto JJ (2020) The urgent need for integrated science to fight COVID-19 pandemic and beyond. J Transl Med 18(1):1–7 65. Erdem Ö, Derin E, Sagdic K, Yilmaz EG, Inci F (2021) Smart materials-integrated sensor technologies for COVID-19 diagnosis. Emerg Mater 4(1):169–185 66. Krishnan S, Kumar Narasimhan A, Gangodkar D, Dhanasekaran S, Kumar Jha N, Dua K, Thakur VK, Kumar Gupta P (2022) Aptameric nanobiosensors for the diagnosis of COVID-19: an update. Mater Lett 308:131237. https://doi.org/10.1016/j.matlet.2021. 131237 67. Kaushik AK, Dhau JS, Gohel H, Mishra YK, Kateb B, Kim N-Y, Goswami DY (2020) Electrochemical SARS-CoV-2 sensing at point-of-care and artificial intelligence for intelligent COVID-19 management. ACS Appl Bio Mater 3(11):7306–7325 68. Mujawar MA, Gohel H, Bhardwaj SK, Srinivasan S, Hickman N, Kaushik A (2020) Nano-enabled biosensing systems for intelligent healthcare: towards COVID-19 management. Mater Today Chem 17:100306. https://doi.org/10.1016/j.mtchem.2020.100306 69. Zheng Y, Tang N, Omar R, Hu Z, Duong T, Wang J, Wu W, Haick H (2021) Smart materials enabled with artificial intelligence for healthcare wearables. Adv Func Mater 31 (51):2105482 70. Mirjalali S, Peng S, Fang Z, Wang CH, Wu S (2022) Wearable sensors for remote health monitoring: potential applications for early diagnosis of covid-19. Adv Mater Technol 7 (1):2100545 71. Trovato P, Simonetti I, Rinaldo C, Grimaldi D, Verde F, Lomoro P, Codella U, De Rosa F, Corvino A, Giovine S (2021) COVID-19 integrated imaging: our experience and literature review. Pol J Radiol 86:e78 72. Maftouni M, Law ACC, Shen B, Grado ZJK, Zhou Y, Yazdi NA (2021) A robust ensemble-deep learning model for covid-19 diagnosis based on an integrated CT scan images database. Institute of Industrial and Systems Engineers (IISE), pp 632–637 73. Tamal M, Alshammari M, Alabdullah M, Hourani R, Alola HA, Hegazi TM (2021) An integrated framework with machine learning and radiomics for accurate and rapid early diagnosis of COVID-19 from chest X-ray. Expert Syst Appl 180:115152

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74. Baumgart DC (2020) Digital advantage in the COVID-19 response: perspective from Canada’s largest integrated digitalized healthcare system. NPJ Digit Med 3(1):1–4 75. Jasmis J, Aziz AA, Hasrol Jono MNH, Zamzuri ZF, Elias SJ (2021) An analysis model for an integrated student activities management system for higher education during RMO/CMCO/PASCA COVID-19 period in Malaysia. Procedia Comput Sci 179:798–803. https://doi.org/10.1016/j.procs.2021.01.067 76. Jasmis J, Aziz AA, Jono MNHH, Zamzuri ZF, Elias SJ (2021) An analysis model for an integrated student activities management system for higher education during RMO/CMCO/PASCA COVID-19 period in Malaysia. Procedia Comput Sci 179:798–803 77. Al-Hunaiyyan A, Alhajri R, Bimba A (2021) Towards an efficient integrated distance and blended learning model: how to minimise the impact of COVID-19 on education. Int J Interact Mob Technol 15(10) 78. Dean BA, Campbell M (2020) Reshaping work-integrated learning in a post-COVID-19 world of work. Int J Work-Integr Learn 21(4):355–364 79. O’Brien H, Tracey MJ, Ottewill C, O’Brien ME, Morgan RK, Costello RW, Gunaratnam C, Ryan D, McElvaney NG, McConkey SJ (2021) An integrated multidisciplinary model of COVID-19 recovery care. Irish J Med Sci (1971–) 190(2):461–468 80. Kaslow NJ, Friis-Healy EA, Cattie JE, Cook SC, Crowell AL, Cullum KA, Del Rio C, Marshall-Lee ED, LoPilato AM, VanderBroek-Stice L (2020) Flattening the emotional distress curve: a behavioral health pandemic response strategy for COVID-19. Am Psychol 75(7):875 81. Satre DD, Meacham MC, Asarnow LD, Fisher WS, Fortuna LR, Iturralde E (2021) Opportunities to integrate mobile app-based interventions into mental health and substance use disorder treatment services in the wake of COVID-19. Am J Health Promot 35 (8):1178–1183 82. Moon KJ (2021) Addressing emotional wellness during the COVID-19 pandemic: the role of promotores in delivering integrated mental health care and social services. Prev Chronic Dis 18 83. Rezaei N, Vahed A, Ziaei H, Bashari N, Afkham SA, Bahrami F, Bakhshi S, Ghanadan A, Ghanadan A, Hosseini N (2021) Health and art (HEART): integrating science and art to fight COVID-19. In: Coronavirus disease-COVID-19. Springer, pp 937–964 84. Bartek N, Peck JL, Garzon D, VanCleve S (2021) Addressing the clinical impact of COVID-19 on pediatric mental health. J Pediatr Health Care 35(4):377–386 85. Scully EP, Haverfield J, Ursin RL, Tannenbaum C, Klein SL (2020) Considering how biological sex impacts immune responses and COVID-19 outcomes. Nat Rev Immunol 20 (7):442–447 86. Aggarwal S, Acharjee A, Mukherjee A, Baker MS, Srivastava S (2021) Role of multiomics data to understand host–pathogen interactions in COVID-19 pathogenesis. J Proteome Res 20(2):1107–1132 87. Li C-X, Gao J, Zhang Z, Chen L, Li X, Zhou M, Wheelock ÅM (2022) Multiomics integration-based molecular characterizations of COVID-19. Brief Bioinform 23(1):bbab485 88. Verma AK, Prakash S (2020) Impact of covid-19 on environment and society. J Glob Biosci 9(5):7352–7363 89. Gautam S, Hens L (2020) COVID-19: impact by and on the environment, health and economy, vol 22. Springer 90. Hassan AM, Megahed NA (2021) COVID-19 and urban spaces: a new integrated CFD approach for public health opportunities. Build Environ 204:108131 91. Speakman JR (2004) Obesity: the integrated roles of environment and genetics. J Nutr 134 (8):2090S-2105S 92. Dietz WH, Solomon LS, Pronk N, Ziegenhorn SK, Standish M, Longjohn MM, Fukuzawa DD, Eneli IU, Loy L, Muth ND (2015) An integrated framework for the prevention and treatment of obesity and its related chronic diseases. Health Aff 34 (9):1456–1463

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93. Sánchez-Carracedo D, Neumark-Sztainer D, López-Guimerà G (2012) Integrated prevention of obesity and eating disorders: barriers, developments and opportunities. Public Health Nutr 15(12):2295–2309 94. Lakerveld J, Brug J, Bot S, Teixeira PJ, Rutter H, Woodward E, Samdal O, Stockley L, De Bourdeaudhuij I, van Assema P, Robertson A, Lobstein T, Oppert J-M, Ádány R, Nijpels G (2012) Sustainable prevention of obesity through integrated strategies: the SPOTLIGHT project’s conceptual framework and design. BMC Public Health 12(1):793. https://doi.org/ 10.1186/1471-2458-12-793 95. Robertson C, Archibald D, Avenell A, Douglas F, Hoddinott P, van Teijlingen E, Boyers D, Stewart F, Boachie C, Fioratou E (2014) Systematic reviews of and integrated report on the quantitative, qualitative and economic evidence base for the management of obesity in men. Health Technol Assess (Winchester, England) 18(35):v 96. Lindegren ML, Kennedy CE, Bain‐Brickley D, Azman H, Creanga AA, Butler LM, Spaulding AB, Horvath T, Kennedy GE (2012) Integration of HIV/AIDS services with maternal, neonatal and child health, nutrition, and family planning services. Cochrane Database Syst Rev (9) 97. Remien RH, Stirratt MJ, Nguyen N, Robbins RN, Pala AN, Mellins CA (2019) Mental health and HIV/AIDS: the need for an integrated response. AIDS (London, England) 33 (9):1411 98. Herwehe J, Wilbright W, Abrams A, Bergson S, Foxhood J, Kaiser M, Smith L, Xiao K, Zapata A, Magnus M (2012) Implementation of an innovative, integrated electronic medical record (EMR) and public health information exchange for HIV/AIDS. J Am Med Inform Assoc 19(3):448–452 99. Schull MJ, Cornick R, Thompson S, Faris G, Fairall L, Burciul B, Sodhi S, Draper B, Joshua M, Mondiwa M (2011) From PALSA PLUS to PALM PLUS: adapting and developing a South African guideline and training intervention to better integrate HIV/AIDS care with primary care in rural health centers in Malawi. Implement Sci 6(1):1–10 100. Gebreeyesus Hadera H, Boer H, Kuiper WAJM (2007) Using the theory of planned behaviour to understand the motivation to learn about HIV/AIDS prevention among adolescents in Tigray, Ethiopia. AIDS Care 19(7):895–900

Nima Rezaei gained his medical degree (M.D.) from Tehran University of Medical Sciences (TUMS) in 2002 and subsequently obtained an M.Sc. in Molecular and Genetic Medicine in 2006 and a Ph.D. in Clinical Immunology and Human Genetics in 2009 from the University of Sheffield, UK. He also spent a short-term fellowship in Pediatric Clinical Immunology and Bone Marrow Transplantation in the Newcastle General Hospital. Since 2010, Prof. Rezaei has worked at the Department of Immunology and Biology, School of Medicine, TUMS; he is now the Full Professor and Vice Dean of International Affairs, School of Medicine, TUMS, and the co-founder and Head of the Research Center for Immunodeficiencies. He is also the founding President of Universal Scientific Education and Research Network (USERN). He has edited more than 40 international books, has presented more than 500 lectures/posters in congresses/meetings, and has published more than 1000 articles in international scientific journals.

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Amene Saghazadeh gained her M.D. from TUMS in 2019. She researches clinical immunology, genetics, epigenetics, and nutrition at the Research Center for Immunodeficiencies, Children’s Medical Center, TUMS. She is the manager of the Integrated Science Association (ISA) in the USERN.

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Emerging Viral Infections in Human Population Anyebe Bernard Onoja

If we can provide even a few months of early warning for just one pandemic, the benefits will outweigh all the time and energy we are devoting. Imagine preventing health crises, not just responding to them. Nathan Wolfe

Summary

Emerging viral diseases threaten the existence of man and animals, often without warning. Their spatio-temporal confinement is a result of host factors, restriction of vectors, and mutations in their genetic makeup. The epidemic potential of many is high, sometimes resulting in high case fatality rates because of the acute onset. The global impact warrants concerted effort by international governments to curb the menace and prevent epidemics, epizootics, or pandemics. Ebola virus jumped from native monkeys in the forests of the DRC in Central Africa, landing in West Africa resulting in heavy human casualties, making it the largest outbreak of Ebola in history. In 2019, respiratory illnesses of severe magnitude were reported in Wuhan a few years after severe acute respiratory syndrome coronavirus (SARS-CoV)-1 originated in Asia and the Middle East respiratory syndrome (MERS) in Saudi Arabia. Millions of people were infected as respiratory infections spread across the globe, and the world economy plummeted. Dangerous viruses will be back with potentially high virulence and case fatality rates. The global community must be prepared for the A. B. Onoja (&) Department of Virology, College of Medicine, University of Ibadan, Ibadan, Nigeria e-mail: [email protected] Integrated Science Association (ISA), Universal Scientific Education and Research Network (USERN), Ibadan, Nigeria © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_2

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reemergence of these pathogens. In this chapter, highlighted are factors that determine the emergence of these viruses, as well as the impact of increased trans-border and intercontinental travel. Companion and wild animals held in captivity serve as reservoirs of some viruses, while civil unrest and natural disasters are catalysts for virus emergence. Surveillance of diseases and efforts of Public Health agencies at Points of Entry were considered with One Health approaches to addressing emerging infections. Graphical Abstract/Art Performance

Emerging infectious diseases and humanity

The code of this chapter is 01100110 01100011 01110100 01100101 01101001 01101111 01001001 01101110 01110011 01101110. Keywords

Emerging viruses

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Epidemics
One health
Vectors

Introduction

Emerging viral infections are diseases that have either infected new hosts, spread into new geographic areas, altered characteristic genomic features, or are caused by agents that were not previously known to be pathogenic [1]. Understanding global health in this regard entails re-conceptualizing the boundaries of global public health

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attention. Spatially, we must open our eyes to the things around us while professionally looking beyond biomedicine and technology toward the practice of public health [2]. Globally, an increase in emerging infections has been reported [3]. About 1,415 species of infectious organisms that affect human beings have been identified. Among these are 175 microorganisms that constitute emerging global health threats; 77 of them are viruses [4]. Viruses greatly affect human and animal health [5]; annually, emergent viruses are reported to originate from animals. When the ecological system is altered, the host–pathogen balance is affected. Increased urbanization and behavioral changes of man impact this balance. Many new viruses have genomes that are made up of ribonucleic acid (RNA) as their genetic material, which have the tendency to mutate rapidly, thereby yielding new variants in response to environmental and immunological pressure from their hosts [6]. Identifying the trend in virus emergence, molecular details, and immune dynamics by which they jump species is important for public health safety and emergency preparedness plans, as medical professionals, veterinarians, zoologists, sociologists, and allied professionals need to appreciate the rapidly evolving field to avert epidemics and pandemics. This brings to fore the concept of “One Health, One Medicine,” which is needed to curb the devastative effect. As a result of the difficulty in predicting when viruses emerge rather than using a magic wand, scientists have employed mathematical models and epidemiology as predictive tools. Some models predicted that tropical areas in Africa, Asia, and South America are the likely areas where vector-borne microorganisms and those that jump from animals will emanate from. Before then, emerging infectious diseases (EIDs) were thought to occur predominantly in exotic geographical areas with the highest concentration of people in Southeast Asia, Japan, Europe, and the United States, which are located within 30°–60° N and 30°–40° S of the latitude [1]. Over the last two decades, researchers have become keenly interested in the growing number of viral infections that are emerging and unpredictably depleting resources. In developed countries, local and national stockpiles of vaccines and medications were depleted too quickly; the outcome was a large number of deaths. The situation in developing countries is that of neglect due to poverty and weak health systems; also, the lack of early warning systems and faulty disease reporting systems, whereas many diseases originate there. The key to infection control is early detection of an emerging crisis and surveillance of progress and interdiction where possible, with quarantine if necessary, to control the spread of the pandemic viral infection. As dangerous as Ebola is, the virus was restricted to a region on the continent of Africa before its breakout to West Africa and beyond. What accounts for the transmission of a disease that is known to be confined to a region is the movement of incidental or principal hosts or seasonal migration. Non-human primates are sold for economic gains. In the process, they are shipped with diseases because of weak health systems or no quarantine protocols. In the twenty-first century, infectious agents easily move across boundaries with heightened air travel. All countries should prepare national strategic responses for emerging viral infections by preemptive measures to deploy modern quarantine facilities, including improving the

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quality of training for first responders so that they do not expose themselves unnecessarily. During the Ebola outbreak, the cases of Americans who were transported in air ambulances from West Africa to mainland United States for better healthcare were considered good, but the action did not help West African countries. Those countries affected needed state-of-the-art facilities at the time for quarantine and treatment. Also, transporting patients into other countries raises the potential threat of virus outbreaks in the destination country regardless of precautions taken. Developing countries lack the capacity to prevent people from contracting new infections, and they cannot effectively manage clinical cases. The creation of awareness about the disease using technology, social media, and the deployment of effective protective gears can combat EIDs. Since the available emergency services are in scarce supply in developing countries, it puts pressure on existing facilities; hence the casualty figures increase when the police, paramedics, or emergency workers are called to save the situation. Unfortunately, it is usually too late as they become patients infected by the same microbe. In the United States, despite the millions of deaths recorded from the Spanish flu of 1918, the government and health experts should have been fully integrated to prevent large-scale epidemics because of their sophisticated technologies. The Ebola virus showed that the United States Centers for Disease and Control (CDC) lacked the constitutional backing to call for national or local quarantine. Also, during the SARS-CoV-2 pandemic, it became clearer that government officials did not understand the level of risk involved to call for quarantine; presidents and governors at the time did not have the political will. Governments globally must act more prudently in the face of a potentially fast-moving pandemic that easily crosses borders.

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Contributory Factors to the Emergence of Viruses

Rodents can easily adapt to abrupt climatic and environmental changes. A little change in climate and food supply can increase their population in the semi-desert and arid regions. The quantity and quality of food affect their population, which may be the reason for an increase in the incidence of Lassa fever in parts of Nigeria and West Africa, as they feed on kitchen waste and raw food stuff that are abundant during the harvest periods. Increased spread of the multi-mammate rat Mastomys natalensis is responsible for the disease. In the 1960s, a long period of drought in the Beni area of Bolivia was accompanied by heavy rains, which exponentially increased the number of Calomys callosus (field voles), leading to a reduction in the local peri-domestic cat population which hitherto controlled feral rodents. This change in the climate led to the incidence of Bolivian hemorrhagic fever caused by the Machupo virus. Similarly, in the early 1990s, prolonged drought in some parts of the United States resulted in a massive reduction in snakes, coyotes, and birds that preyed on rodents. Eventually, when the drought was over, heavy rainfall brought about plenty of grasshoppers

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and pinon nuts that resulted in the explosion of different rodents’ population, which included deer mice that transmits hantavirus [6]. In 1994, an outbreak of a virus in Venezuela followed a similar pattern which turned out to be the Guanarito virus, a novel RNA virus [7]. In many places in Southeastern Asia and the Amazonian basin, deforestation has led to the release of predators that normally kept insects, rodents, and vectors under check [8]. As biodiversity reduces, this leads to the distribution of certain opportunistic microbes, which increase local infection [6]. There is evidence that the Crimean-Congo hemorrhagic fever virus (CCHFV) poses a substantial risk in parts of Asia, sub-Saharan Africa, the Middle East, and the Balkans [9]. Some human infections are acquired by infected tick bites during their removal from animals or the human body or direct contact with infected tissues or blood from the livestock. Most cases are among shepherds, ranchers, and abattoir workers living or working in endemic areas in close proximity to goats, cattle, sheep, or ostriches. Nosocomial outbreaks have been reported in medical personnel after surgical intervention or treatment of infected patients [10]. Social determinants that affect health include economic factors such as poverty and inadequate or low level of education. These factors result in health inequalities which impose great but unseen costs. Between 2003 and 2006, these health inequalities cost the United States $1.24 trillion [11]. Annually in developing countries, 12.2 million children below five years of age die because their parents do not have a few cents to pay for their hospital bills. Poverty is responsible for their inability to be vaccinated or to get therapy [12].

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Impact of Increased Travel on Emerging Diseases

Over 100 million people travel annually by air. With the fast airplanes in the fleet of airlines now, people can easily traverse three continents in a short time. A high rate of air travel is a major driver for the transmission of EIDs. In 2003, this was evident when SARS spread to seventeen countries from China in less than a week [6]. Similarly, SARS-CoV-2 first showed up in December 2019 in the Wuhan area of China. By January 2020, it spread across several continents, with heavy casualties in Europe and the United States. This led to restrictions of international flights from China and then a ban of international flights globally [13]. In the United States, the West Nile virus (WNV) similarly spread to a new host range. Although it was initially isolated in the Ugandan West Nile area, the index case in the United States was identified in 1999 in New York [14]. From that time to 2001, there were only a few cases that were localized to the East Coast. But from 2002 to 2003, it expanded to the West Coast, affecting about 3,000 people representing the largest arboviral meningoencephalitis and neuroinvasive WNV epidemic reported in the United States to date [15]. Closely related viruses circulated in Romania and Eastern parts of Europe at the time. Air passengers from Israel who flew to the United States were considered to have imported the virus because of the ongoing outbreak at the time [15, 16]. In 2009, when the H1N1 influenza pandemic began, air traffic from

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Mexico indicated that transmission risk was higher with increased traffic [17]. It was estimated that 80,000 wild animals were freighted by air, with a lot of them quarantined in close proximity to human residential areas [18]. Although an effective quarantine service exists in some countries, it may be difficult to detect pathogens that are outside their surveillance parameters. It was observed that in air travel, private jets/airplanes might not comply with international regulations, thereby compromising safety standards. In 2021, monkeypox was imported to the United States by travelers after visiting Nigeria at different times. This happened after a report of importation was made in the United Kingdom from the same country where the West African clade of the monkeypox virus is endemic. Ground transportation is another effective means of movement of people worldwide. In Europe, about 17% of movement is done by ground transport, while air transport accounts for 0.2% of the kilometers covered [19]. In Africa, there is a lot of travel by road with many people covering over 500 km. As they traverse borders, they encounter vegetation zones that contain arthropods that transmit several viral diseases [20]. Regional integration of East and Southeast Asia is increasing rapidly with the removal of Visa restrictions and relaxation of previous embargos to facilitate travel while airlines are providing budget-friendly fares. Africa is witnessing growing trade with China, including air travel. Exports of goods and commodities from 2003–2008 jumped by 20%. This Africa-Asia trade alliance facilitates air travel which in turn spreads EIDs. The inter-regional transport networks in Asia will also aid the dispersal of new microorganisms between humans and animals. About 2.5 billion travels were recorded in China as they entered into the new year of 2011. Such great movement was made possible by rapid interconnectivity, which results in the spread of disease agents, making it difficult to track and control them [21]. On the other hand, trains and buses rarely have high-efficiency air filters to capture microbes [6]. During the global SARS-CoV-2 pandemic, this was a fundamental problem that led to lockdown in most cities in the world with the cessation of public transport services. Depending on how sophisticated their vessels are, seafarers contribute to the incidence of diseases by carrying arthropod larva and rodents on board. Aedes albopictus, which is incriminated in Dengue virus transmission and was restricted to certain parts of the world, is now commonly found in many parts of the world. The Asian tiger mosquito was identified in several locations in the Netherlands recently, and government safety experts are battling to eradicate it. Although imported into West Africa through trade in used tires from Asia, these mosquitoes are now the most predominant daytime biting species in Nigeria. In a study conducted from 2013 to 2014, it was found that the diurnal temperature of mosquito activity in the tropical rainforest of Nigeria was 31 °C. On the other hand, Aedes aegypti that were known to be in the forests have now found their way into the urban populations where they maintain viruses [22, 23]. These vectors are infected for life and can transmit the viruses to their offspring, thereby maintaining the continuous presence of the pathogens (Figs. 1 and 2).

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Fig. 1 Aedes albopictus (Asian tiger mosquito) from the tropical rain forest region of West Africa

Fig. 2 Aedes aegypti incriminated in the etiology of many arboviral diseases

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Reservoirs in Virus Transmission Dynamics

Agricultural activities allow people to encroach into ecosystems to clear forests, stump tree roots, thereby upsetting the ecological balance. Rodents are forced out of their burrows to search for alternative ecological niches. In the process, the sylvatic transmission of viral diseases occurs. Monkeys have been incriminated as reservoirs of yellow fever virus, dengue virus, and other viruses. Fruit-eating and insectivorous bats are accidental carriers of a host of viruses, including the recently

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discovered zoonotic bat coronavirus. They are reservoirs of diseases because they seldom come down with infections. In a recent study to detect flaviviruses in West Africa, oral bat specimens tested positive while birds in the wild are known to be reservoirs of some alphaviruses [24]. It is more difficult to control viruses that have reservoirs, even when there are vaccines. This is because the reservoirs transmit the virus but may not manifest clinical signs suggestive of the disease. Rabies is an example; it is maintained by several animals like raccoons, foxes, and skunks. To eradicate rabies, there is a need to understand the wildlife population and vaccinate them alongside domestic dogs. The forest landscape is extensive, and this may be a herculean task in developing countries to transport the vaccines and maintain their potency in remote locations.

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Wildlife as a Compounding Factor

As a biosecurity measure, the Food and Agriculture Organization (FAO) recommended that interaction between domestic and wild animals be reduced to safeguard human health. This came to the fore because of the involvement of some feline species cats in the epidemiology of certain respiratory viruses, especially SARS-CoV-2, as well as feral avians in the spread of highly pathogenic influenza strains to Europe. Wildlife trade facilitates zoonotic transmission of pathogens [25– 27]. In Asia, wild animal products are important as symbols of wealth; they are eaten as delicacies, tonics, or traditional medicines. Although all Asian countries are signatories to the treaty banning international trade on endangered fauna and flora, they spearhead the booming global trade in wildlife [28]. Wildlife is the reservoir of some viruses, and humans contract the diseases when they enter the ecosystems of these animals. The Tacaribe viruses belong to genus arenavirus, family Arenaviridae which includes Tamiami virus, Bear canyon virus, and whitewater Arroyo virus in the United States; Chapare virus (CHPV) found in Trinidad, Guanarito virus (GTOV) in Venezuela, Serbia virus (SABV) in Brazil, Machupo virus (MACV) in Bolivia and Junin virus (JUNV) in Argentina [29]. In the United States, a few viruses have been assigned provisional membership of this group; they include Tonto creek virus, Catarina virus, Big Bushy and Skinner Tank viruses [30–32], and Real de Catorce virus in Mexico [33]. Five of these viruses cause hemorrhagic fever in humans (CHPV, JUNV, SABV, GTOV, and MACV) [34, 35]. Rodents belonging to the family Cricetidae are the primary hosts for Tacaribe viruses [36]. In Venezuela, Zygodontomys brevicauda, a cane mouse, is the major host of GTOV [7, 37], while in mainland Argentina, Calomys musculinus, the dry vesper mouse, is the major host of JUNV [38]. Historically, large epidemics of hemorrhagic fever were reported in Mexico from 1545–1815 [39]. It affected people in the highlands and was theorized to have been caused by rodents native to Mexico or those viruses belonging to the Tacaribe complex [40]. In 1967, a new virus was isolated from Peromyscus mexicanus, a deer mouse in the Southern part of Mexico, close to the

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hotspot of the 1967 epidemic. The phylogenetic study revealed a significant degree of homology with Tacaribe viruses, resulting in the name Ocozocoautla de Espinosa viruses [41]. Bats are natural hosts of several viruses like severe acute respiratory syndrome virus, lyssavirus, henipavirus, filovirus, etc. [42]. Zoonotic viruses are vectored by both insectivorous and fructivorous bats; the latter spread diseases faster because they eat the flesh of fruits and defecate it alongside viral particles. It is noteworthy that the Ebola virus has been found for up to 3 weeks in the blood and organs of bat species such as Epomops fraqueti and Hypsignathus monstrosus. Asymptomatic Ebola infections are documented in Africa among trapped bats that feed on insects [43]. Rousettus species dwell in caves and eat fruits; antibodies against Ebola and Marburg virus were found in them. Further, the Marburg virus was reported in Rhinolophus eloquens and Miniopterus inflatus [44]. In 1998, during the Hendra virus outbreak, it was observed that fruit bats that were infected with the virus having neutralizing antibodies inhabited trees under which horses sought shelter. That was the epidemiological link. Young people were exposed to the Nipah virus while picking date palms or fruits in the area infested with Pteropus bats [45]. Although rabies is not an emerging virus, it is a major re-emerging health challenge from raccoons, skunks, foxes, hyenas, and jackals. From December 2009 through December 2011, 133 raccoons in Manhattan central park were infected with the virus. Subsequently, a response plan was instituted by a task force comprising members from the United States Department of Agriculture and Wildlife Services, New York Department of Health, New York City, Animal Care and Control Unit, New York Department of Environmental Conservation, and New York Department of Health and Mental Hygiene. The Trap-Vaccinate and Release (TVR) plan was enforced to reduce transmission among wild animals and prevent exposure of pets or humans. Previously, TVR was used to vaccinate raccoons orally and reduce their population by 80% [46–50]. This is the level of government response that we expect globally to interdict and control potential outbreaks. Alphaviruses circulate in a wide-animal range. They are transmitted by mosquitoes and include Bebaru virus, Chikungunya virus, Fort Morgan virus, Cabassou virus, Everglades virus, Barmah Forest virus, Highlands J virus, Getah virus, Kyzylagagach virus, Mayaro virus (MAYV), Middelburg virus, Mucambo virus, Ndumu virus, O’nyong-nyong virus, Pixuna virus, Rio Negro (AG80) virus, Ross River virus, Whataroa virus, Semliki Forest virus, Sindbis virus, Western equine encephalitis virus, Venezuelan equine encephalitis virus, etc. Humans are dead-end hosts, and infection is incidental; thus, it cannot be spread further to another person except through a vector. In South America, MAYV occurs in sylvatic cycles like yellow fever, which is vectored by Haemagogus spp. with non-human primates as the reservoir host. Clinical diseases have been reported with the isolation of MAYV. Human infections are sporadic through occupational hazards or during recreational activities in forests where there are spillovers [51].

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Fig. 3 Wild aquatic birds in a riverine area in Nigeria, West Africa

Wild birds facilitate zoonotic infections through their flight patterns. Their migration facilitates the intercontinental movement of new vectors and pathogens to receptive ecosystems. The spread of the highly pathogenic avian influenza virus (HPAIV) in countries within Europe was because of wild birds’ migration [52]. Feral birds found near bodies of water are capable of maintaining infection with influenza strains, whereas HPAIVs produce asymptomatic infections in waterfowl [53]. In 2002, several waterfowls died in Penfold Park, Hong Kong. Not too long, another outbreak occurred in Kowloon Park [54]. Since then, wild birds in the country have been sources of H5 and H7 subtypes. In 2005, about 6184 geese, ruddy shelducks, gulls, and Cormorants in Qinghai Lake China died in a large outbreak of HPAIV caused by H5N1 [55]. Similarly, WNV is spread by wild birds [56]. Thus, inter-seasonal models which connect migratory routes of birds can be used to predict the spread of EIDs that follow similar transmission patterns [57] (Fig. 3).

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Role of Companion Animals and Animals Held in Captivity in Emerging Viruses

Increased contact with cats, dogs, horses, and other companion animals is a risk factor for zoonotic diseases. Animals living in close proximity to humans cause infections of veterinary and conservation interest [58, 59]. In some rural areas in Uganda and Nigeria, dogs are kept either for hunting, guarding, and herding livestock or for security. They scavenge for food in refuse dump sites and around human dwellings, including abattoirs where they feed on the carcass of dead or

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discarded premature animals. Their instinctive nature, which allows them to smell everything, makes it easy for them to contract respiratory infections. As a result, they are reservoirs for some pathogens and can be surveyed to provide early warning of some diseases [60]. Veterinary services are well established with strict vaccination compliance in developed countries like the United States. However, most dogs receive no vaccination prophylaxis and roam about freely in many parts of Africa and the developing world. Some affluent people keep wild animals as pets, and private zoos are now an asset to wealthy people in rich regions like the United Arab Emirates and the United States. Wild animal trade is booming globally as an estimated 350,000 animals are sold annually in the international market, thereby increasing the chances of diseases that spread from animals to man. The new arenavirus found in a boa constrictor has caused researchers to study the frequency of such occurrence in animals held in captivity [61]. After sequencing the virus isolate, a significant similarity was observed with the genome as well as host cells. Recombination was thought to have occurred because the virus showed close similarity with glycoproteins of filoviruses and arenaviruses In 2003, a monkeypox outbreak occurred in Wisconsin, United States, because prairie dogs intended for sale were kept near giant rats and certain squirrels of the Funisciurus species that were brought in from Africa [62]. Aracatuba virus, a vaccinia-like virus, caused pockets of infections from humans to livestock [63].

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Civil Unrest and War as Catalysts for Emerging Viruses

The war in the Darfur region displaced many people causing coercive migration of millions of individuals into refugee camps or across the border, creating a humanitarian crisis. The yellow fever epidemic in Darfur, Sudan, is the worst report on the African continent with a case fatality rate of 32% and several deaths in 20 geographical locations. A joint release by the Ministry of Health and WHO reported that there were 171 deaths in January of 2013. By February of the same year, few sporadic cases were confirmed in neighboring Chad [64]. The virus-vector association is responsible for the epidemics, and this is a result of the poor environmental hygiene resulting from the conflicts and breakdown of law and order.

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National Prevention Strategy

The strategy is to improve the population health of individuals in a country at every stage of life. Typically, a person who has a chronic ailment spends at least 30 min quarterly with the doctor, and this is 0.2% of the entire life. In real-life situations, the other 99.98% is spent interacting with the environment, e.g., in the home, workplace, school, religious premises, and making health decisions. When considering health care spending in the United States, 3.1% is committed to approaches

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that will prevent diseases, with the larger proportion expended on therapy. It is necessary to focus on prevention-based health mechanisms with better funding at the state and local levels since this is where most people reside [65]. In most African countries, the budgetary allocation is grossly inadequate and never enough for the running cost of health facilities. Nigeria is the most populous African country, yet primary health care facilities are few. State capitals have about 38 medical doctors attending to 3,949 patients while 2–10 medical doctors attend to as many as 216,466 patients in the rural areas [66], so the question of prevention is not considered to a very large extent in many facilities. The key goal is surveillance, detection, and interdiction of disease before it has a chance to spread via the vectors that are most prevalent in any geographical area. This should be integrated into existing health and veterinary structures for an effective health system. In recent times national centers for disease control have been established, with support from the African Centre for Disease and Control. These centers helped during the COVID-19 pandemic by training and supporting partner laboratories to rise to the global challenge, particularly in Africa. In developed countries, they have national guidelines that are activated following alerts from robust disease integrated reporting systems.

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Public Health Collaboration Between Agencies at Points of Entry

International health regulations (IHR) are meant to address challenges of international movements, as well as global health issues at seaports, airports, and land borders. The WHO did not have pre-existing effective programs of surveillance, detection, and interdiction of a suspected disease in these areas. There is a need to leverage inter-agency and multi-sectoral collaboration to achieve public health objectives in ports [67]. The International Civil Aviation Organization facilitated a mutual agreement through which countries realized IHR objectives pertaining to travel by air [68]. Other initiatives include the International Tourism Response Network [69], the European CDC guideline for assessing risks of infectious agents via air travel [70], and the ship sanitation training of the European Union Commission [71]. Ports, Airports, and Ground Crossings network (PAGnet) is supported by the WHO to share information and coordinate health activities at entry points in airports, seaports, and land borders [72]. PAGnet was pivotal in the dissemination of information during the 2009 influenza A pandemic caused by the H1N1 strain, during the 2010–2011 Cholera epidemic in Haiti, and during the 2011 accidental nuclear emission in Japan. It facilitated timely sharing of information which prevented over-reaction and barriers to overseas trade and travel. However, countries differ widely in IHR capacity, including responsibilities assigned to various officers involved at the entry points as well as priority areas. These differences make it challenging to provide relevant national and local guidelines at these entry points

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worldwide. Private sectors and commercial enterprises involved in government health are major players in implementing travel provisions of IHR. The WHO as a powerful organ is neutral and can facilitate engagements between the various actors to reach a consensus and achieve public health [67]. This is because, despite these measures in place in mainland United States, the cholera outbreak following the earthquake in Haiti did jump the geographical water barrier in Haiti to reach the urban center of Miami in South Florida. The United States CDC and the state of Florida government failed to impose any kind of quarantine on travelers arriving in Florida from Haiti during the outbreak of the disease. National public health authorities employ several approaches for disease reporting and communication, which are different from the approach used by veterinary authorities. Human and animal diseases are monitored separately in most countries, including IHR bodies [73]. In the United States, veterinary professionals only learned of human WNV infections from the media coverage. This disconnect greatly affects the rate of transmission of diseases that crossover from both humans and animals. Bureaucratic bottlenecks in the dissemination of information between agencies dealing with animal and human health make it difficult to intercept target pathogens. This is partly because reporting of disease outbreaks leads to reduced patronage in the tourism industry as a result of embargos placed by various countries. The economy suffers greatly from the reporting, thereby triggering political tension, which often limits information sharing and delays reporting. The national authority in the East Asian region was under intense political and economic pressure to delay the report of the first HPAI outbreak for fear of sense economic losses [74]. However, the delay in reporting and interdiction action due to political considerations can potentially result in a huge number of deaths such that by the time government acts, it would be too late to contain the disease. It may be difficult to have a universal protocol that will enhance public health policy collaboration and joint action, but steps should be taken to reduce the gaps. Challenges such as geographical boundaries and living in poor countries with limited access to health facilities are major risk factors for EIDs. Incentives can be given by the international community to facilitate information dissemination and coordination. Efficient disease reporting and communication protocols are helpful in outbreak response. To achieve this, incentives have to be consistent at the national level across professional bodies and institutions. IHR governs human health and legally requires countries to report a disease of potential global health concern within 24 h to the WHO. However, there are no clear sanctions for delays in reporting by countries [75].

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Public Health Risk Assessment

SARS experience informed the need for transparency and prompt information sharing on risks and control measures between nations and the WHO [76]. In 2005, the IHR was revised to mitigate and control the international transmission of disease by avoiding obstruction to human movement and trade [77]. It facilitates rapid, secure, and open information sharing on emerging infections and integrated responses. It provides an avenue for discussions with the WHO and national focal point officers through a web portal on an Event Information Site. In the United States, the IHR became operational in 2007 to the effect that true federalism was respected and information shared between federal and state governments. It is now clear that the WHO should be notified of any health issue that can expand quickly worldwide, not minding where it emanated from or whether it was naturally occurring or deliberately released in terms of bioweapons, chemicals, or radio-nuclear pollution [78]. When preparing to respond to EIDs, scientists and public health officials rapidly assess the risk and make policy decisions [79]. The Health Protection Agency in the United Kingdom coordinates the risk of human-animal infections with experts from veterinary, medical, and allied health workers called upon [80]. Several considerations have to be made in dealing with public health issues and the role of wildlife and reservoir hosts. They monitor the extent of potential threats from EIDs to the United Kingdom [81]. A specialized group of medical entomologists and zoonoses experts provide technical support to the HPA on vector-borne infections. It is important to know the ecology and transmission cycle of these diseases to predict outbreaks. This will enable preparation to mitigate the impact of the disease. The complexity can be appraised by ecological observation to break the disease cycle [79].

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Surveillance of Health Challenges

A major problem to achieving global surveillance is addressing the imbalance in surveillance mechanisms for public health issues. Provision of resources for surveillance activities is based on global initiatives which target health priorities such as funding for HIV/AIDS, malaria, tuberculosis, and COVID-19 [82]. Many public health concerns which should be prioritized are not given consideration. The leading causes of death among the poorest countries in the world are chronic non-communicable diseases (NCDs); hence, the need for surveillance on non-infectious diseases like obesity and the use of Tobacco. Vehicular injury, cancers, cardiovascular disease, and emerging arthropod-borne infections are not systematically monitored; the essential information for intervention-like rates are based on estimates from developed countries. For the sustenance of global health, surveillance systems in both industrialized and the poorest countries have to be enhanced [83, 84]. There are usually hidden requirements that obligate countries

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that receive medical aid to purchase and deploy certain equipment or use recommended surveillance mechanisms that may fragment, complicate, and compromise the national health security apparatus [85]. A global standard for comprehensive surveillance will enable countries to conform to such standards by adapting them to individual country’s epidemiological and disease control efforts [86]. Agreements should take into cognizance ethical issues relating to the use of humans in health research, confidentiality, privacy, and data sharing [87, 88]. The use of information technology to achieve surveillance activities has gained wide acceptance in many countries in recent times because of the numerous potentials. However, implementation is not properly coordinated and harmonized to conform to standard practices. This has made countries experiment and adapt the technologies to suit their national surveillance frameworks [89]. New communication technologies in information science can enhance global health. For instance, mobile telephone services are being used in the poorest populations and most remote areas. Accessibility to network coverage is improving at a rapid rate [90]. Improved internet access has continued to speed up, and this is helping global interaction. Surveys are now conducted using devices enabled to link up with the global positioning system for prompt data capturing, accuracy, supervisory functions, analysis, and dissemination of findings [91, 92]. Miniaturized laboratory testing devices are now developed to overcome the long time spent to obtain results for assays and the technical requirements in locations with weak investments in health infrastructures [93, 94]. To achieve this, some pioneers in global health initiatives have funded ideas with the potentials for transforming healthcare delivery through the Grand Challenge grant [95]. When the technological innovations are successful, it is needful to fashion out international best practices that are collectively harmonized for resource-limited settings [89]. The earthquake in Nepal and its surroundings caused massive loss of lives. Although the country had a good national public health system, public health services in the disaster area were disrupted. There were tainted food and water supplies and unrecovered dead remains from the rubble. There was great concern that epidemics would spread to remote locations when survivors and aid workers moved away from the disaster area to unaffected areas. Similarly, after the Haiti earthquake, several Florida-based aid workers returned to Florida with contagious infections. Besides recovery and rescue activities, people should focus on disease surveillance, identification, and interdiction (quarantine). However, outside support may be needed to prevent potential pandemics in other nations that receive survivors and aid workers returning from the catastrophe area. In this regard, quarantine is very crucial to curbing the spread of diseases, and foreign public health services should embrace this. In an interview with BBC America, a former President of Liberia, Madam Ellen Johnson Sirleaf, when asked what she would have done differently if she went back in time to the early days of the Ebola outbreak, opined that quarantine would have been the most appropriate prevention strategy to curb the spread to many of her countrymen. Similarly, an early response to highly infectious diseases would be to quarantine and stockpile medications rather than risk epidemics. There are disputes over sovereignty and the sharing of pathogen samples, and this hampers

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international collaborations. In 2011, the WHO endorsed a framework for influenza virus sharing and benefits that may accrue, such as vaccines. This initiative charts the course for other researchers and players in the surveillance industry [96].

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SARS-CoV-2 Pandemic

An outbreak of pneumonia broke out in the Wuhan city of China in December 2019. In early January 2020, about 44 confirmed cases of the disease were reported to the WHO [13]. The link was established between the exposure of patients in Wuhan seafood and the wild animal market [97]. A name was announced for the pandemic respiratory virus as it was designated as SARS-CoV-2 by the International Committee on Taxonomy of Viruses (ICTV). Before long, the outbreak spread to other countries and was declared an international health crisis requiring immediate global action. International travel facilitated transmission from the epicenter in China to Western Europe and the United States. This virus represents the seventh known coronavirus responsible for human coronavirus diseases [98]. Millions of people were infected in more than 210 countries around the world. Due to increased transmission across borders, it was declared a pandemic [99]. Circumstantial evidence linked the index case of COVID-19 to the seafood market where different live animals were sold. Inadequate biosecurity measures and poor sanitary conditions coupled with frequent human-animal contact heightened the risk of emerging zoonotic infections [100]. Following the SARS-CoV-2 outbreak, China banned businesses involving wildlife. Further, the seafood market was closed to prevent continuous transmission and evolution of new strains that may occur due to immunological pressure. The Danish Veterinary and Food Administration detected SARS-CoV-2 in minks from Jutland [101]. The virus was detected in cats in Belgium, Spain, Germany, Russia, and France, as well as in a tiger, a lion, and a dog in the United States. It was also detected in domestic cats in the United States and minks in Denmark and Netherlands [102]. Scientists believe that the crowded market at Wuhan facilitated the spread from an infected person to several others [103]. SARS-CoV-2 has demonstrated that pathogens can pose a formidable threat to mankind. Like many zoonotic infections that jump to humans, rapid mutations in humans can result in evolutionary changes in them, facilitating transmission to humans. SARS-CoV-2 was thought to have spilled from bats through pangolins which is the intermediate host to humans [104]. Bats engage in large-scale migration across regions; hence, they appear to be the source of the virus [105]. Global vaccine research for the virus was revved up since none existed, while efforts to get antiviral agents were intensified. Everyone resorted to social distancing and adorning of nose masks as a preventive measure which greatly reduced the incidence of the disease. Significant effort was made to increase laboratory capacity and testing; the pressure on companies manufacturing COVID-19 testing kits was intense.

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Global Disease Information Systems

Weak public health systems across the world, especially in developing countries, make it difficult to track, monitor, and control epidemics. To overcome this challenge, relevant and functional information systems have been developed. They include: • Arbo-NET: this is the integrated surveillance system for arthropod-borne infections, which is domiciled in the United States; • The Global Public Health Intelligence Network (GPHIN): it monitors disease outbreaks of public health emergency from official and unofficial sources, including media and the internet; • The Global Outbreak Alert and Response Network (GOARN): it is under the purview of WHO and follows up on diseases that have been notified by GPHIN. It supports governments to identify and characterize the pathogens, prepare for outbreaks and help communities or affected countries as a whole; • The Global Early Warning System (GLEWS): it enhances the legal obligation required to notify the Office des International Epizootics (OIE) of EIDs. The OIE collects data from several international sources and verifies with the country Chief Veterinary Officers concerned. However, it cannot recommend an export ban until officially verified by the CVO. Major sources of information are WHO, including the GPHIN and networks like ProMED; • The Program for Monitoring Emerging Diseases (ProMED): This is under the auspices of the International Society of Infectious Diseases and relies on a global network of researchers who provide up-to-date information on disease outbreaks in their domains from formal and informal sources. Information received is reviewed by experts who are regional moderators in infectious diseases. Reporting from sub-Saharan Africa is very poor while the United States supplies the largest source of information; • The World Animal Health Information Database (WAHID): it stores and summarizes reports on diseases from OIE; • Med-Vet-Net: it maintains an archival information system in Europe for zoonotic and food-borne disease prevention and control; • The Global Emerging Infections Surveillance and Response System (GEIS): it is a United States platform in the Department of Defense (DOD) that monitors infectious diseases that can pose a risk to military personnel; and • The Emerging Infectious Diseases Network (EIDN): it comprises interns and public health professionals. It was established at the University of Iowa with support from the United States CDC to combat infectious diseases.

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Prophylactic and Control Measures

Outbreaks of EIDs occur in different ways. Some like influenza can be predicted annually because of several epidemiological studies that have been carried out; whereas, it may be difficult in the case of the potentially dangerous Marburg virus or even SARS-CoV-2, which started as a joke. As a result, working out one interventional plan for the numerous diseases is practically not feasible, especially because of mutant and reverting strains in vaccinated populations. Therefore, improved and increased surveillance activities are the best strategy for combating EIDs in the long-term. It requires proper mobilization and training of scientists and clinicians for EIDs. However, paucity of funds is a major drawback to high-quality training in many countries. Also, this has led to the unavailability of containment facilities for identification and prevention of spread. The problem is huge in Sub-Saharan Africa because significant disease outbreaks frequently occur acutely. They potentially break out from the original geographic locus of the disease before spreading. In Uganda and Gabon, on the ground, expertise was improved with technical support provided by the United States CDC, which ultimately facilitated major gains in the area of capacity [6]. However, veterinarians are usually not involved in several programs packaged to strengthen local capacity and promptly respond to outbreaks, especially in developing countries. In 1999, ample time was lost before the first cases of WNV occurred in humans and birds in New York City [105]. Veterinarians should be called upon and properly involved in every suspected outbreak of zoonotic diseases. Technological tools are now to monitor and predict disease outbreaks. Satellite images of changes in vegetation cover have been useful for monitoring rainfall patterns. In East Africa, maps taken by satellite accurately made predictions of Rift Valley Fever virus (RVFV) outbreaks with the abundance of vectors [106]. Rapid dissemination of information on disease occurrence has been enhanced using the internet. It helped identify and control the 2003 SARS epidemic and 2010 swine flu caused by H1N1. Delays in reporting outbreaks can escalate the number of cases that threaten local technical support [107]. In 1996, hospitals were closed in Zaire and Sudan following news reports of Ebola virus outbreaks which limited transmission and contained it. In the Kampala area of Uganda, investment in infrastructure led to the strengthening of detection capacity at the Entebbe Virus Research Institute by the United States CDC, which led to the identification of a novel virus called Bundibugyo strain of Ebola virus and rapid detection of several new cases. Unless a critical epidemiological review of disease outbreaks is done and clinical parameters assessed, infectious diseases can still spread in countries with sophisticated equipment [108]. A rapid quarantine decision must be made early to prevent a disease from traveling along its normal vector route. This is a decision at the recommendation of appropriate public health organizations on site.

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Dangerous Twist of Ebola Virus in West Africa

In 1967, as medical laboratory scientists were preparing kidney tissues from green monkeys brought in from Uganda, they became infected with a hemorrhagic fever in Marburg, Germany, and Belgrade, Yugoslavia. Nine years later, two powerful epidemics occurred in the rainforest region of Sudan and the DRC. The Ebola virus (EBV) strains were named Ebola-Z and Ebola-S. Of all known hemorrhagic fevers, these three viruses have the highest case fatality rates. Although Ebola-Reston causes mortality in monkeys, no human disease has been reported despite the presence of antibodies to this particular strain [109]. An outbreak was detected in 2014 in a remote rain forest region of Guinea, but in neighboring Sierra Leone and Liberia, the outbreak spread faster. Before this time, EBV was restricted to East and Central Africa, where outbreaks occurred sporadically [110]. It was difficult to initially identify the outbreak until three months when it became a significant public health problem. The index case was a little boy in Gueckedou, Guinea, who died as a result of an infection, which was caused by the Zaire strain [111]. The number of confirmed Ebola cases in Guinea and some other West African countries rose dramatically [112]. Dr. Kent Brantly, one of the two Americans initially stricken by EBV in Liberia, was transported in a specially outfitted plane and moved to Emory University Hospital in Atlanta, Georgia, United States, where he was cared for in a special isolation unit with equipment and infrastructure that provided an extraordinarily high level of clinical isolation. Dr. Nancy, a colleague of Brantly in Liberia, also became sick after treating over 100 patients. Although the United States has the capacity for isolating patients with advanced treatment of mass casualties, modern laboratory facilities, and well-trained staff, there was a lack of political will on the part of the government to act quickly to contain the spread of the dangerous disease at the time. The first Ebola victim in Nigeria arrived onboard a flight from Liberia and died after suffering extreme bouts of vomiting and diarrhea in a private hospital. This created anxiety in the most populous black nation [112]. The epicenter of the epidemic in Sierra Leone became a ghost town, with everyone deserting the town for fear of being infected. Communities seriously affected in Liberia were quarantined, and movement in and out restricted. In Liberia, quarantining did not curb the spread at the time because the EBV had evolved and established itself as the quarantine process was delayed for political reasons. It was worsened by the fact that people resisted another order to leave corpses in affected homes for decontamination and burial by trained and dedicated officials [112]. A team of researchers published sequence data showing rapid inter-host and intra-host genetic mutations that could affect diagnostic and vaccine efforts [113]. Nigeria was able to contain the situation because of the early declaration of a national emergency, which led to increased surveillance activities and quarantining of close contacts or individuals suspected to have symptoms. The primary reason for the fast spread in other West African countries was the relatively weak health infrastructure and limited resources. Also, porous borders and increased economic

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activities among member countries are factors that contributed to the spread. Liberia had few health workers and poorly equipped hospitals per 10,000 persons [114]. When designing management strategies in resource-limited settings, appropriate mechanisms to deploy countermeasures into the outbreak area should be in place [115]. Containment is the surest way to control EVD; a strict quarantine protocol should be enforced where infected patients are treated and first responders protected from occupational exposure. As the virus travels when people move to new locations, infected people with the finances quickly opt for better care in developing countries, so they jet out, and the disease goes with them. Since economic growth does not translate to immunity of a population from infection with deadly viruses, it can spread to points far away from the origin. For instance, some African countries and the United States were treating Ebola patients in their homeland where Ebola never occurred. Therefore, it remains a risk if the disease does not get out of the quarantine perimeter. But, if Ebola enters any country, the risk of spread is high, especially for close contacts of the infected people or health care workers. Indeed, the Ebola virus broke out of the geographical space where it was contained and moved as people and certain animals moved. If Ebola mutates into a more virulent strain with increased vector competence and transmission, it has the potential to become a true global killer of pandemic potential.

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Approaching Emerging Diseases from One Health Perspective

Human and veterinary health workers became institutionally and technically separated in the twenty-first century in both training and practice. Calvin Schwabe is considered the father of veterinary epidemiology, and in 1960 he raised objections about the various divisions in public health. Therefore, he coined the term “One Medicine” to put into perspective the interrelatedness regarding the health of various species and the need to reduce risks posed by zoonotic infections to humans, including their food and economies [116]. In 1975, the FAO and WHO jointly came up with a position on the important contribution of veterinarians to public health and established veterinary public health as a means to advance cooperation. This later became important in the global fight against avian flu. One health as a discipline was broadened to include the health of the entire ecosystem, including domestic animals, wildlife, and man. In 2004, the World Conservation Society convened a meeting on One Health to facilitate better communication between services linked to humans, animals, as well as wildlife, with a view to embracing social and environmental interrelationships [117]. The One Health principle was defined by members of the American Veterinary Medical Association as a synergy of local, national, and international efforts by many disciplines working for the good of animal and human health in the world [118]. One Health means a more comprehensive engagement of

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human, animal, and environmental health. The 1997 avian flu was initially considered a local problem in Hong Kong until wild birds were identified as the source. Such mistakes could have been avoided if field biologists were initially involved.

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Conclusion

Emerging viruses continue to cause serious global problems in the twenty-first century. Climate change will contribute to EIDs, just like outbreaks of diseases because of increased intercontinental air travel and sea voyages. Porous borders in developing countries make it easy for vectors and reservoirs of viruses to cross national borders. Respiratory viruses spread easily, causing high morbidity and mortality. SARS-CoV-2 pandemic demonstrated how the global economy could be crippled with devastating effects across the world. If the travel embargo was not instituted, the number of deaths would have been worse than the high mortality recorded. International partnerships galvanized support for information sharing to provide an early warning system through oversight activities of infectious disease specialists. Wildlife trade in some animal species has been sanctioned to prevent the transmission of emerging diseases. Since civil unrest is a catalyst for emerging diseases, efforts should be in place to prevent environmental hygiene, which breeds disease vectors. To achieve global health, the synergy of professional input is key. Therefore, “One Health’’ should be activated to bring veterinary, medical, and allied health care workers together to effectively tackle emerging viruses. Core Messages

• Climate change facilitates processes that enhance emerging viruses leading to devastating public health impacts. • Poverty and inadequate education are key social determinants of emerging viruses. • Frequent travel facilitates the spread of emerging viruses. • There should be no barrier to accessing care and support in periods of outbreaks.

References 1. Olival KJ, Daszak P (2005) The ecology of emerging neurotropic viruses. J Neurovirol 11:441–446 2. Ventures W, Page T (2013) Bring global health and global medicine home. Acad Med 88:907–908 3. Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, Daszak P (2008) Global trends in emerging infectious diseases. Nature 451:990–993

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4. Taylor LH, Latham SM, Woolhouse MEJ (2001) Risk factors for human disease emergence. Philos Trans R Soc Lond Ser B Biol Sci 356:983–989 5. Tyler K (2009) Emerging viral infections of the central nervous system. Arch Neurol 66 (8):939–948 6. Howard CR, Fletcher NF (2012) Emerging virus diseases: can we ever expect the unexpected? Emerg Microbes Infect 1(e46):1–29 7. Milazzo ML, Cajimat MN, Duno G, Duno F, Utrera A, Fulhorst CF (2011) Transmission of Guanarito and Pirital viruses among wild rodents, Venezuela. Emerg Infect Dis 17:2209– 2215 8. Patz JA, Daszak P, Tabor GM, Aguirre AA, Pearl M, Epstein J, Wolfe ND, Kilpatrick AM, Foufopoulos J, Molyneux D, Bradley DJ, Working Group on Land Use Change and Disease Emergence (2004) Unhealthy landscapes: policy recommendations on land use change and infectious disease emergence. Environ Health Perspect 112:1092–1098 9. ProMED (2013) Crimean-congo hemorrhagic fever-India (03): (Gujarat) fatal 10. Schmaljohn CS, Nichol ST (2007) Bunyaviridae. In: Knipe DM, Howley PW (eds) Fields virology, 5th edn. Lippincott Williams & Wilkins, pp 1742–1779 11. LaVeist TA, Gaskin DJ, Richard P (2009) The economic burden of health inequalities in the United States. Joint Center of Political and Economic Studies, Washington, DC 12. World Health Organization (1998) World report, WHO Geneva. World Health Organization. International health regulations 2005, 2nd edn. The Organization, Geneva; 2008. http:// whqlibdoc.who.int/publications/2008/9789241580410_eng.pdf 13. World Health Organization (2020) Novel Coronavirus (2019-nCoV). World Health Organization situation report-1. 21 January, 2020. Accessed 27 June 2020 14. Nash D, Mostashari F, Fine A, Miller J, O'Leary D, Murray K, Huang A, Rosenberg A, Greenberg A, Sherman M, Wong S, Layton M, 1999 West Nile Outbreak Response Working Group (2001) West Nile outbreak response working group. The outbreak of West Nile virus infection in the New York City area in 1999. N Engl J Med 344:1807–1814 15. Gubler DJ (2007) The continuing spread of West Nile virus in the western hemisphere. Emerg Infect Dis 45:1039–1046 16. Giladi M, Metzkor-Cotter E, Martin DA, Siegman-Igra Y, Korczyn AD, Rosso R, Berger SA, Campbell GL, Lanciotti RS (2001) West Nile encephalitis in Israel in 1999, the New York connection. Emerg Infect Dis 7:659–661 17. Hosseini P, Sokolow SH, Vandegrift KJ, Kilpatrick AM, Daszak P (2010) Predictive power of air travel and socio-economic data for early pandemic spread. PLoS ONE 5:e12763 18. Roehrig JT, Layton M, Smith P, Campbell GL, Nasci R, Lanciotti RS (2002) The emergence of West Nile virus in North America: ecology, epidemiology, and surveillance. Curr Top Microbiol Immunol 267:223–240 19. Askar MA, Mohr O, Eckmanns T, Krause G, Poggensee G (2012) Quantitative assessment of passenger flows in Europe and its implications for tracing contacts of infectious passengers. Euro Surveill 17:20195 20. Omatola CA, Onoja AB, Moses E, Mahmud M, Mofolorunsho CK (2020) Dengue in parts of the Guinea Savannah region of Nigeria and the risk of increased transmission. Int Health 10:1–5 21. Eisenberg JN, Cevallos W, Ponce K, Levy K, Bates SJ, Scott JC, Bates SJ, Hubbard A, Vieira N, Endara P, Espinel M, Trueba G, Riley LW, Trostle J (2006) Environmental change and infectious disease: how new roads affect the transmission of diarrheal pathogens in rural Ecuador. Proc Natl Acad Sci U S A 103:19460–19465 22. Onoja AB, Adeniji JA, Opayele AV (2016) Yellow fever vaccination in Nigeria: focus on Oyo State. Highl Med Res J 16(1):37–41 23. Onoja AB, Adeniji JA, Olaleye OD (2016) High rate of unrecognized dengue virus infection in parts of the rainforest region of Nigeria. Acta Trop 160:39–43 24. Thompson NN, Auguste AJ, Coombs D, Blitvich BJ, Carrington CVF, da Rosa APT, Wang E, Chadee DD, Drebot MA, Tesh RB, Weaver SC, Adesiyun AA (2012) Serological

2

Emerging Viral Infections in Human Population

25. 26.

27. 28. 29.

30.

31.

32.

33.

34. 35.

36.

37.

38.

39. 40. 41. 42. 43.

41

evidence of flaviviruses and alphaviruses in livestock and wildlife in Trinidad. Vector Borne Zoonotic Dis 12(11):969–978 Thornton PK (2010) Livestock production: recent trends, future prospects. Philos Trans R Soc Lond Ser B Biol Sci 365:2853–2867 Van Borm S, Thomas I, Hanquet G, Lambrecht B, Boschmans M, Dupont G, Decaestecker M, Snacken R, van den Berg T (2005) Highly pathogenic H5N1 influenza virus in smuggled Thai eagles, Belgium. Emerg Infect Dis 11:702–705 Wang LF, Eaton BT (2007) Bats, civets and the emergence of SARS. Curr Top Microbiol Immunol 315:325–344 Rosen GE, Smith KF (2010) Summarizing the evidence on the international trade in illegal wildlife. Eco Health 7:24–32 Salvato MS, Clegg JC, Buchmeier MJ, Charrel RN, Gonzalez J-P, Lukashevich IS (2005) Family Arenaviridae. In: Fauquet CM, Mayo MA, Maniloff J, Desselberger U, Ball LA (eds) Virus taxonomy: eighth report of the International Committee on Taxonomy of Viruses. Elsevier Academic Press, New York, pp 725–733 Cajimat MN, Milazzo ML, Bradley RD, Fulhorst CF (2007) Catarina virus, an arenaviral species principally associated with Neotoma micropus (southern plains woodrat) in Texas. Am J Trop Med Hyg 77:732–736 Cajimat MN, Milazzo ML, Borchert JN, Abbott KD, Bradley RD, Fulhorst CF (2008) Diversity among Tacaribe serocomplex viruses (family Arenaviridae) naturally associated with the Mexican woodrat (Neotoma mexicana). Virus Res 133:211–217 Milazzo ML, Cajimat MNB, Haynie ML, Abbott KD, Bradley RD, Fulhorst CF (2008) Diversity among Tacaribe serocomplex viruses (family Arenaviridae) naturally associated with the white-throated woodrat (Neotoma albigula) in the southwestern United States. Vector Borne Zoonotic Dis 8:523–540 Inizan CC, Cajimat MN, Milazzo ML, Barragán-Gomez A, Bradley RD, Fulhorst CF (2010) Genetic evidence for a Tacaribe serocomplex virus, Mexico. Emerg Infect Dis 16:1007– 1010 Peters CJ (2002) Human infection with arenaviruses in the Americas. Curr Top Microbiol Immunol 262:65–74 Delgado S, Erickson BR, Agudo R, Blair PJ, Vallejo E, Albariño CG, Vargas J, Comer JA, Rollin PE, Ksiazek TG, Olson JG, Nichol ST (2008) Chapare virus, a newly discovered arenavirus isolated from a fatal hemorrhagic fever case in Bolivia. PLoS Pathog 4:1000047 Musser GG, Carleton MD (2005) Family Cricetidae. In: Wilson DE, Reeder DM (eds) Mammal species of the world: a taxonomic and geographic reference, 3rd edn. The John Hopkins University Press, Baltimore, pp 955–1189 Fulhorst CF, Bowen MD, Salas RA, Duno G, Utrera A, Ksiazek TG, De Manzione NM, De Miller E, Vasquez C, Peters CJ, Tesh RB (1999) Natural rodent host associations of Guanarito and Pirital viruses (family Arenaviridae) in central Venezuela. Am J Trop Med Hyg 61:325–330 Mills JN, Ellis BA, McKee KT, Calderon GE, Maiztegui JI, Nelson GO, Ksiazek TG, Peters CJ, Childs JE (1992) A longitudinal study of Junin virus activity in the rodent reservoir of Argentine hemorrhagic fever. Am J Trop Med Hyg 47:749–763 Acuna-Soto R, Romero LC, Maguire JH (2000) Large epidemics of hemorrhagic fevers in Mexico 1545–1815. Am J Trop Med Hyg 62:733–739 Marr JS, Kiracofe JB (2000) Was the huey cocoliztli a haemorrhagic fever? Med Hist 44:341–362 Cajimat MNB, Milazzo ML, Bradley RD, Fulhorst CF (2012) Ocozocoautla de Espinosa virus and hemorrhagic fever, Mexico. Emerg Infect Dis 18:401–405 Calisher CH, Childs JE, Field HE, Holmes KV, Schountz T (2006) Bats: important reservoir hosts of emerging viruses. Clin Microbiol Rev 19:531–545 Leroy EM, Kumulungui B, Pourrut X (1995) Fruit bats as reservoirs of Ebola virus. Nature 438:575–576

42

A. B. Onoja

44. Pourrut X, Kumulungui B, Wittmann T, Moussavou G, Délicat A, Yaba P, Nkoghe D, Gonzalez J-P, Leroy EM (2005) The natural history of Ebola virus in Africa. Microbes Infect 7:1005–1014 45. Field H, de Jong C, Melville D, Smith C, Smith I, Broos A, Kung YHN, McLaughlin A, Zeddeman A (2011) Hendra virus infection dynamics in Australian fruit bats. PLoS ONE 6: e28678 46. Sobey KG, Rosatte R, Bachmann P, Buchanan T, Bruce L, Donovan D, Brown L, Davies JC, Fehlner-Gardiner C, Wandeler A (2010) Field evaluation of an inactivated vaccine to control raccoon rabies in Ontario, Canada. J Wildl Dis 46:818–831 47. Rosatte R, MacDonald E, Sobey K, Donovan D, Bruce L, Allan M, Silver A, Bennett K, Lucy Brown L, Macdonald K, Gibson M, Buchanan T, Stevenson B, Davies C, Wandeler A, Muldoon F (2007) The elimination of raccoon rabies From Wolfe Island, Ontario: animal density and movements. J Wildl Dis 43:242–250 48. Rosatte R, Sobey K, Donovan D, Allan M, Bruce L, Buchanan T, Stevenson B, Davies C, Wandeler A, Muldoon F (2007) Raccoon density and movements after population reduction to control rabies. J Wildl Manag 71:2373–2378 49. Rosatte R, Tinline R, Johnston D (2007) Rabies control in wild carnivores. In: Jackson A (ed) Rabies, 2nd edn. Elsevier, London, pp 595–634 50. Rosatte RC, Donovan D, Allan M, Bruce L, Buchanan T, Sobey K, Stevenson B, Gibson M, MacDonald T, Whalen M, Davies JC, Muldoon F, Wandeler A (2009) The control of raccoon rabies in Ontario Canada: proactive and reactive tactics, 1994–2007. J Wildl Dis 45:772–784 51. Weaver SC, Reisen WK (2010) Present and future arboviral threats. Antiviral Res 85:328– 345 52. Kilpatrick AM, Chmura AA, Gibbons DW, Fleischer RC, Marra PP, Daszak P (2006) Predicting the global spread of H5N1 avian influenza. Proc Natl Acad Sci U S A 103:19368– 19373 53. Webster RG, Bean WJ, Gorman OT, Chambers TM, Kawaoka Y (1992) Evolution and ecology of influenza A viruses. Microbiol Rev 56:152–179 54. Ellis TM, Bousfield RB, Bissett LA, Dyrting KC, Luk GSM, Tsim ST, Sturm-Ramirez K, Webster RG, Guan Y, Peiris JSM (2004) Investigation of outbreaks of highly pathogenic H5N1 avian influenza in waterfowl and wild birds in Hong Kong in late 2002. Avian Pathol 33:492–505 55. Chen H, Li Y, Li Z, Shi J, Shinya K, Deng G, Qi Q, Tian G, Fan S, Zhao H, Sun Y, Kawaoka Y (2006) Properties and dissemination of H5N1 viruses isolated during an influenza outbreak in migratory waterfowl in western China. J Virol 80:5976–5983 56. Rappole JH, Hubálek Z (2003) Migratory birds and West Nile virus. J Appl Microbiol 94:47S-58S 57. Peterson AT, Andersen MJ, Bodbyl-Roels S, Hosner P, Nyari A, Oliveros C, Papes M (2009) A prototype forecasting system for bird-borne disease spread in North America based on migratory bird movements. Epidemics 1:240–249 58. Butler JRA, Bingham J (2000) Demography and dog–human relationships of the dog population in Zimbabwean communal lands. Vet Rec 147:442–446 59. Baker PJ, Soulsbury CD, Iossa G, Harris S (2010) Domestic cat (Felis catus) and domestic dog(Canis familiaris). In: Gehrt SD, Riley SPD, Cypher BL (eds) Urban carnivores: ecology, conflict, and conservation. The John Hopkins University Press, Baltimore, MD, pp 157–172 60. Cabezón O, Millán J, Gomis M, Dubey JP, Ferroglio E, Almería S (2010) Kennel dogs as sentinels of Leishmania infantum, Toxoplasma gondii, and Neospora caninum in Majorca Island, Spain. Parasitol Res 107:1505–1508 61. Stenglein MD, Sanders C, Kistler AL, Ruby JG, Franco JY, Reavill DR, Dunker F, DeRisi JL (2012) Identification, characterization, and in vitro culture of highly divergent arenaviruses from boa constrictors and annulated tree boas: candidate etiological agents for snake inclusion body disease. MBio 3:e00180

2

Emerging Viral Infections in Human Population

43

62. Khodakevich L, Jezek Z, Messinger D (1988) Monkeypox virus: ecology and public health significance. Bull World Health Organ 66:747–752 63. de Souza TG, da Fonseca FG, Marques JT, Nogueira ML, Mendes LCN, Borges AS, Peiró JR, Pituco EM, Bonjardim CA, Ferreira PCP, Kroon EG (2003) Aracatuba virus: a vaccinia-like virus associated with infection in humans and cattle. Emerg Infect Dis 9:155– 160 64. Yuill TM, Woodall JP, Baekeland S (2013) Yellow fever outbreak—Darfur Sudan and Chad. Int J Infect Dis 17:476–478 65. Institute of Medicine (2012) For the Public’s health: investing in a healthier future. The National Academies Press, Washington, DC. http://www.iom.edu/reports/2012/for-thepunlics-health-investing-in-a-healthier-future.aspx 66. Ajala OA, Sanni L, Adeyinka SA (2005) Accessibility to health care facilities: a panacea for sustainable rural development in Osun State, southwestern Nigeria. J Hum Ecol 18:121–128 67. Hardiman MC, World Health Organization Department of Global Capacities, Alert and Response (2012) World Health Organization perspective on implementation of international health regulations. Emerg Infect Dis 18:1041–1045 68. International Civil Aviation Organization (2012) Cooperative Arrangement for The Prevention of Spread of Communicable Disease through Air Travel (CAPSCA). http:// www.capsca.org/ 69. World Tourism Organization (2012) Tourism Emergency Response Network (TERN). http://rcm.unwto.org/en/content/about-tourism-emergency-response-network-tern-0 70. Leitmeyer K (2011) European risk assessment guidance for infectious diseases transmitted on aircraft—the RAGIDA project. Eur Surveill 16:198 71. European Commission (2012) Executive agency for health and consumers. SHIPSAN TRAINET. http://www.shipsan.eu/trainet/ 72. World Health Organization (2012) Ports, airports and ground crossing network. https:// extranet.who.int/pagnet/ 73. Kuehn BM (2006) Animal-human diseases targeted to stop pandemics before they start. J Am Med Assoc 295:1987–1989 74. Dolberg F, Guerne-Bleich E, McLeod A (2005) Emergency regional support for post-avian influenza rehabilitation. FAO, Rome 75. Hitchcock P, Chamberlain A, Van Wagoner M, Inglesby TV, O’Toole T (2007) Challenges to global surveillance and response to infectious disease outbreaks of international importance. Biosecur Bioterror 5:206–227 76. Cooke FJ, Shapiro DS (2003) Global outbreak of severe acute respiratory syndrome (SARS). Int J Infect Dis 7:80–85 77. World Health Organization (2005) Strengthening health security by implementing the international health regulations, 3rd edn, p 84. SBN: 978 92 4 158049 6 78. World Health Organization (2010) Note from the permanent mission of the United States of America to the United Nations Office and other international organizations accepting the IHRs. http://www.who.int/ihr/usa.pdf 79. Medlock JM, Jameson LJ (2010) Ecological approaches to informing public health policy and risk assessments on emerging vector-borne zoonoses. Emerg Threats 3:1–23 80. Health Protection Agency (2008) The Human Animal Infections and Risk Surveillance (HAIRS) group: first report 2004–2007, pp 1–30. www.hpa.org.uk 81. Walsh AL, Morgan D (2005) Identifying hazards, assessing the risks. Vet Rec 157:684–687 82. Lindblade KA, Hamel MJ, Feikin DR, Odhiambo F, Adazu K, Williamson J, Vulule JM, Slutsker L (2007) Mortality of sick children after outpatient treatment at first-level health facilities in rural western Kenya. Trop Med Int Health 12:1258–1268 83. Beaglehole R, Bonita R (2001) Challenges for public health in the global context prevention and surveillance. Scand J Public Health 29:81–83 84. Cecchine G, Moore M (2006) Infectious disease and national security-strategic information needs. Technical report

44

A. B. Onoja

85. Biesma RG, Brugha R, Harmer A, Walsh A, Spicer N, Walt G (2009) The effects of global health initiatives on country health systems: a review of the evidence from HIV/AIDS control. Health Policy Plan 24:239–252 86. Stansfield S (2005) Structuring information and incentives to improve health. Bull World Health Organ 83:562 87. Organisation for Economic Co-Operation and Development (2005) Paris declaration on aid effectiveness: ownership, harmonization, alignment, results, and mutual accountability. http://www.oecd.org/dataoecd/11/41/34428351.pdf 88. Pfeiffer J, Johnson W, Fort M, Shakow A, Hagopian A, Gloyd S, Gimbel-Sherr K (2008) Strengthening health systems in poor countries: a code of conduct for non-governmental organizations. Am J Public Health 98:2134–2140 89. St. Louis M (2012) Global health surveillance. Supplements. Morb Mortal Wkl Rep 61:15–19 90. Reidpath DD, Allotey P (2009) Opening up public health: a strategy of information and communication technology to support population health. The Lancet 373:1050–1051 91. Vanden JL, Wolkon A, Frolov AS, Terlouw DJ, Eliades MJ, Morgah K, Takpa V, Dare A, Sodahlon YK, Doumanou Y, Hawley WA, Hightoweret AW (2007) Use of handheld computers with global positioning systems for probability sampling and data entry in household surveys. Am J Trop Med Hyg 77:393–399 92. Yu P, de Courten M, Pan E, Galea G, Pryor J (2009) The development and evaluation of a PDA-based method for public health surveillance data collection in developing countries. Int J Med Inform 78:532–542 93. Urdea M, Penny LA, Olmsted SS, Giovanni MY, Kaspar P, Shepherd A, Wilson P, Dahl CA, Buchsbaum S, Moeller G, Burgess DCH (2006) Requirements for high impact diagnostics in the developing world. Nature 444:73–79 94. Houpt ER, Guerrant RL (2008) Technology in global health: the need for essential diagnostics. The Lancet 372:873–874 95. Cohen J (2005) Public health. Gates Foundation picks winners in grand challenges in global health. Science 309:33–35 96. Horby PW, Pfeiffer D, Oshitani H (2013) Prospects for emerging infections in East and Southeast Asia 10 years after severe acute respiratory syndrome. Emerg Infect Dis 19 (6):853–860 97. Lu H, Stratton CW, Tang YW (2020) Outbreak of pneumonia of unknown etiology in Wuhan, China: the mystery and the miracle. J Med Virol 92(4):401–402 98. Wang Z, Yang B, Li Q, Wen L, Zhang R (2020) Clinical features of 69 cases with coronavirus disease 2019 in Wuhan, China. Clin Infect Dis 71(15):769–777 99. World Health Organization (2020) Coronavirus disease 2019 (COVID-19) situation report— 103. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200502 100. Li HY, Zhu GJ, Zhang YZ, Zhang LB, Hagan EA, Martinez S, Chmura AA, Francisco L, Tai H, Miller M, Daszak P (2020) A qualitative study of zoonotic risk factors among rural communities in southern China. Int Health 12(2):77–85 101. ProMED (2020) Coronavirus disease 2019 update (284): Denmark (North Jutland) animal, farmed mink, spread, dog. http://promedmail.org/post/20200617.7479510 102. CDC (2020) Confirmation of COVID-19 in two pet cats in New York. https://www.cdc.gov/ media/releases/2020/s0422-covid-19-cats-NYC.html 103. Areddy JT (2020) China rules out animal market and lab as coronavirus origin. Wall Street J. https://tinyurl.com/yb7jyb78. Accessed 26 May 2020 104. Cyranoski D (2020) The biggest mystery: what it will take to trace the coronavirus source. Nature, June 5, 2020. https://tinyurl.com/yd7ooc6a; Alexandre Hassanin, “Coronavirus origins,” The Conversation, March 24, 2020. https://tinyurl.com/rynx542 105. Holloway M (2000) Outbreak not contained. West Nile virus triggers a re-evaluation of public health surveillance. Sci Am 282:20–22

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Emerging Viral Infections in Human Population

45

106. Linthicum KJ, Anyamba A, Tucker CJ, Kelley PW, Myers MF, Peters CJ (1999) Climate and satellite indicators to forecast Rift Valley fever epidemics in Kenya. Science 285 (5426):397–400 107. Heymann DL, Barakamfitiye D, Szczeniowski M, Muyembe-Tamfum JJ, Bele O, Rodier G (1999) Ebola hemorrhagic fever: lessons from Kikwit, Democratic Republic of the Congo. J Infect Dis 179:283–286 108. Lau EH, Hsiung CA, Cowling BJ, Chen C, Ho L, Tsang T, Chang C-W, Donnelly CA, Leung GM (2010) A comparative epidemiologic analysis of SARS in Hong Kong, Beijing and Taiwan. BMC Infect Dis 10:50 109. White DO, Fenner FJ (1994) Filoviruses. Medical virology. Academic Press, California, pp 485–488 110. World Health Organization (2014) Disease outbreak update: Ebola outbreak response in West Africa, 28 July 2014 111. Baize S, Pannetier D, Oestereich L, Rieger T, Koivogui L, Magassouba N, Soropogui B, Sow MS, Keïta S, De Clerck H, Tiffany A, Dominguez G, Loua M, Traoré A, Kolié M, Malano ER, Heleze E, Bocquin A, Mély S, Raoul H, Caro V, Cadar D, Gabriel M, Pahlmann M, Tappe D, Schmidt-Chanasit J, Impouma B, Diallo AK, Formenty P, Van Herp M, Günther S (2014) Emergence of Zaire Ebola virus disease in Guinea. N Engl J Med 371:1418–1425 112. ProMED (2014) Ebola virus, Africa. ProMED ahead Digest, vol 24, issue 54 113. Gire SK, Goba A, Andersen KG, Sealfon RSG, Park AJ, Kanneh L et al (2014) Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science (New York, NY) 345:1369–1372 114. World Health Organization (2013) Statistics. www.who.int/gho/publications/world_health_ statistics/2013/en/ 115. Hoenen T, Feldmann H (2014) Ebolavirus in West Africa, and the use of experimental therapies or vaccines. BMC Biol 12:80 116. Schwabe C (1984) Veterinary medicine and human health. Williams and Wilkins, Baltimore/London 117. Forget G, Lebei J (2001) An ecosystem approach to human health. Int J Occup Environ Health 7:33–36 118. American Veterinary Medical Association (AVMA) (2008) One health: a new professional imperative. Final report. One Health Initiative Task Force

Anyebe Bernard Onoja Ph.D. is a Lecturer in the Department of Virology, University of Ibadan. He trained as a Doctor of Veterinary Medicine at the University of Ibadan and obtained a Master of Science and Doctor of Philosophy from the College of Medicine in the same Institution. His research interest is emerging viruses, arbovirology, pediatric viral infections, and molecular epidemiology of viruses. He is a foundation member of the African Society of Pediatrics Infectious Disease with headquarters at the University of Cape Town, South Africa. He is a member of the World Society of Virology as well as the African Virology Network (AVN), among other learned bodies. He is the leader of the Arbovirus and Vector Research Group in Nigeria (AVRGN), which was formed in 2012. He is a fellow of the University of Ibadan Medical Education Partnership Initiative Junior (UI-MEPI-J) project. Dr. Onoja is also a poet.

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One Health as an Integrated Approach: Perspectives from Public Services for Mitigation of Future Epidemics Sandul Yasobant, Ana Maria Perez Arredondo, Jéssica Francine Felappi, Joshua Ntajal, Juliana Minetto Gellert Paris, Krupali Patel, Merveille Koissi Savi, Dennis Schmiege, and Timo Falkenberg

For some odd reason, I had an early and extreme multidisciplinary cast of minds. I couldn’t stand reaching for a small idea in my own discipline when there was a big idea right over the fence in somebody else’s discipline. So, I just grabbed in all directions for the big ideas that would really work. Charlie Munger

Summary

This chapter explores the implications for the health of humans, animals, and the environment derived from the provision and utilization of conventional public services (e.g., healthcare and water and sanitation supply) and non-conventional services (e.g., provision of green spaces). Public services are services like education, health, and sanitation, entitled to a population, and so are important

S. Yasobant (&) Center for One Health Education, Research, and Development (COHERD), Indian Institute of Public Health Gandhinagar (IIPHG), Gandhinagar, India e-mail: [email protected] S. Yasobant
A. M. Perez Arredondo
J. F. Felappi
J. Ntajal
J. M. G. Paris
K. Patel
M. K. Savi
D. Schmiege
T. Falkenberg Center for Development Research (ZEF), University of Bonn, Bonn, Germany e-mail: [email protected] J. F. Felappi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_3

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for achieving development goals. Here, the integration of public services under the One Health (OH) perspective, which is key for promoting health and early detection of risks of future epidemics, is exemplified by the cases of food systems, land use, and land cover change. The discussion section of this chapter addresses how to reduce the risks of future epidemics by making public services enablers of health. Finally, we call for more integrative research and action under the OH approach.

J. Ntajal e-mail: [email protected] J. M. G. Paris e-mail: [email protected] K. Patel e-mail: [email protected] M. K. Savi e-mail: [email protected] D. Schmiege e-mail: [email protected] T. Falkenberg e-mail: [email protected] S. Yasobant Global Health, Institute for Hygiene & Public Health (IHPH), University Hospital Bonn, 53127 Bonn, Germany S. Yasobant School of Epidemiology & Public Health, Datta Meghe Institute of Medical Sciences (DMIMS), 442004 Wardha, India A. M. Perez Arredondo
J. F. Felappi International Centre for Development Studies (IZNE), University of Applied Sciences Bonn-Rhein-Sieg, Sankt Augustin, Germany J. Ntajal
K. Patel
D. Schmiege
T. Falkenberg Institute for Hygiene and Public Health (IHPH), GeoHealth, University Hospital Bonn, Bonn, Germany K. Patel
D. Schmiege Department of Geography, University of Bonn, Bonn, Germany K. Patel Parul Institute of Public Health, Parul University, Vadodara, India

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Graphical Abstract/Art Performance

One Health (OH)

The code of this chapter is 01100101 01110010 01100101 01110011 01101001 01100011 01010011 01110110. Keywords

Epidemics mitigation

1


One health
Public service utilization
Public services
Risk

Introduction

Public services are the set of services or facilities that entitle the population of a determined area to achieve development goals such as health and well-being, education and training, safety and social security, economic development, and environmental protection [1]. The attainment of development goals through public services depends on different factors such as:

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• The composition and distribution of the population (e.g. different ethnic groups, age groups, neighborhoods); • The needs of the population (i.e. prioritize services to achieve a desired social goal); • The service delivery system (e.g. from household to household as the water supply and sanitation, or by fixed facilities such as hospitals and parks); • The financial availability for the provision and access to services (i.e. budget, allocation); and • The involvement of civil society in government decisions (e.g. public–private partnerships). Healthcare services, drinking water supply, sanitation services, and the availability of spaces for recreation are examples of public services. The funding resources to support public services are being made available at different government levels, and its providers range from public and private enterprises to civil society, or a mix of those mentioned before [2, 3]. Public services comprise the stages of provision and utilization. On the one hand, public services provision intends to maximize the benefits to the population and minimize inequalities in-service distribution. On the other hand, the utilization of public services refers to the actual use of public services by different population groups. Therefore, collaborative provision and proper utilization of public services can help to address social challenges as well as support single authorities to overcome resource efficiency issues [4]. Food systems and land-use planning benefit from public services utilization, meaning that they would gain significantly from such collaborations, considering that their health outcomes affect not only humans but also animals and the environment. With a holistic understanding of multi-sectoral collaborations to achieve health, the provision and utilization of public services may enable within small administrative units the synergies needed to manage, mitigate, or eliminate the risk of disease in earlier phases, thus optimizing health and well-being. Under this light, the One Health (OH) approach, which warrants the health and well-being of humans, animals, and the environment, can provide a common ground for action. According to the OH Commission, OH is “a collaborative, multi-sectoral, and trans-disciplinary approach—working at local, regional, national, and global levels to achieve optimal health and well-being outcomes recognizing the interconnections between people, animals, plants and their shared environment” [5]. OH in comparative medicine has its roots in ancient times, and particular attention to diseases between humans and animals started in the sixteenth century with rinderpest management [6, 7]. Moreover, the institutionalization of joint human health and animal health studies was accelerated in the twentieth century when the concept “One Medicine” was introduced to the international research agenda [8], and more recently, the emergence of new diseases that became global pandemics in

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the early 2000s has given rise to the concept of “One Health-One World” for combating zoonotic diseases. Thus, the OH approach was advocated mostly for identifying disease transmission routes between humans and animals [9]. The OH approach is still in constant evolution. Nowadays, OH is used not only in the context of control of zoonotic and emerging infectious diseases but also in the broader context of mitigation of disease risks related to intensive food production, biodiversity loss, environmental degradation, and natural habitat loss [10, 11]. Actions to control epidemics like the Middle-East Respiratory Syndrome (MERS), Ebola, or Zika are evidence of the collaboration between the human and animal sectors for integrated research and practice on zoonotic diseases and epidemic control [12]. The OH approach also goes beyond the collaboration of the health sectors towards the integration of multiple disciplines and actors to tackle the multiple dimension of disease conditions and their potential solution(s) [13]. This chapter explores the provision and utilization of different public services and their implications for the health of humans, animals, and the environment. Moreover, the chapter presents two cases for public services integration that could benefit from the OH approach and enable multi-sectoral collaborations. This introduction follows three sections. The next section reviews definitions and explores interrelations between health and public services as single areas, such as healthcare, water and sanitation supply, provision of green spaces, but also areas that combine different services, like food system and land-use and land-cover change. Then, we will look deeper into the health implications of different single services and combined public services. Finally, we discuss how different OH actions in public services can help enable health and reduce the risk of future epidemics.

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The Interrelation of Public Services and Health and Well-Being

From the broad range of public services, this chapter selected some services, such as health services, water and sanitation supply, and the provision of green spaces, to illustrate how public services can enable health. Moreover, as services are often not used as single elements, but in combination, we present two cases of areas that combine multiple public services: food systems and land-use planning and conservation. Below, the definitions and the way services relate to health and well-being are presented.

2.1 Health Services The health system has the overall objective to provide healthcare for all and ensure equitable access to healthcare services. For humans and animals, the health system works on structural elements such as regulatory frameworks, financial resources,

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and political factors, which further depend upon specific capacities of infrastructure, information systems, technology, and human resources. Across the globe, the provision of healthcare is patient-centric, including a broad range of actors like clinicians, nurses, pharmaceutical and medical equipment providers, health centers, caregivers, and health insurance companies. In a disease outbreak, both the health impacts and the health system response depend on the health system’s capacities. A key factor in controlling epidemics is primary healthcare, concerned with disease prevention, testing and diagnosis, treatment, and care provision [14]. Disease control and prevention delivery arise parallel to the curative health service, encompassing diverse strategies such as surveillance, disease prediction, and effective control measures during health emergencies. The major historical pandemics have led to the generation of containment strategies. Examples of those strategies date back to the quarantine of Venice during the plague in the 17th-century; the investigation strategies during the cholera epidemic in London in the 19th-century; the epidemiological metrics denoting the reproduction number (i.e., the average number of infected people after the first generation of infection), and sanitation and hygiene improvements, contact tracing, isolating/quarantining of infected cases, physical distancing, public vaccination, and effective treatment in the twentieth century [15, 16]. More recently, the science for epidemic prevention has emphasized three key points: i. the use of emerging technologies for the early detection; ii. strengthening surveillance across the political borders at the regional, national, and global levels [16]; and iii. surveillance across species [17].

2.2 Water and Sanitation Services The main concern of water and sanitation services delivery is to ensure a safe, reliable, equitable, and efficient supply to support lives. The water, sanitation, and hygiene (WASH) situation around the world remains a profound challenge for households as well as in institutional settings, despite being recognized as a human right [18]. In 2017, 71% of the global population used safe drinking water services, whereas 60% had basic handwashing facilities, and only 45% used safely managed sanitation services[19]. Among health care facilities, 74% had basic water services, while insufficient data has not allowed sanitation and hygiene assessment [19]. Moreover, data from 2016 showed that around two-thirds of schools had basic drinking water and sanitation services, but only a little more than half had basic hygiene services [20]. Unsafe drinking water, poor sanitation, and the lack of hygiene services provision are ongoing threats to a safe and dignified life. Over 60% of all diarrhoeal deaths (1.4 million) and 49.8 million disability-adjusted life-years (DALYs) might be due to inadequate water and sanitation supply in 2016 in low- and middle-income countries [21].

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2.3 Provision of Green Spaces The green infrastructure contains a network of natural and semi-natural areas and elements with multiple functions that provide socioecological benefits [22]. In the urban context, it is referred to as urban green infrastructure and comprises various typologies such as green open spaces (e.g., parks, gardens, and cemeteries), street trees, green walls, and green roofs. Urban green infrastructure provision depends on the availability of green spaces within administrative limits, e.g., m2/person, or within a certain distance from residents, % of the population with green space within 300 m from home [23]. There is no general recommendation on minimum standards of green space availability, being a decision of countries and cities to establish their targets. Consequently, standards vary considerably depending on local urban development patterns and environmental and climate conditions [24]. Urban green elements provide several ecosystem services that improve the environment’s quality, such as microclimate regulation, noise reduction, and air and water purification [25–27]. Green infrastructure improves the environmental quality of urban areas and positively impacts several other spheres, such as human and animal health.

2.4 The Food System, Land-Use Planning, and Conservation The main goal of the food systems is to satisfy the nutritional requirements of the population [28]. The food supply chain consists of agricultural production, aggregation and processing, distribution and retailing, preparation, consumption, and disposal [29, 30]. Each step individually integrates services like water, sanitation, waste collection, energy, human and animal healthcare, and infrastructures (information systems, markets, and transport). Land use and land cover changes are dynamic processes that influence the functioning, quality, and provision of health-promoting ecosystem services to humans, animals, and the environment. Due to the immense pressure on land and its resources to meet the demand for transport infrastructure, education, healthcare, and the production of goods and services, there is a pressing need to integrate land use planning in OH strategies to develop sustainable solutions to promote human, animal, environmental health.

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Health Implications of Different Public Services for the Health of Humans, Animals, and the Environment

3.1 Health Services The provision and access to healthcare services vary between and within the human and animal health systems. Generally, the health of humans and animals is being

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defined and evaluated in different ways based on ethical, moral, and legal issues [31, 32], which has led to immense differences in the provision and access to healthcare. The provision of human health services comprises health promotion, disease prevention, diagnosis, treatment, and rehabilitation. Besides being highly specialized by levels of care and health conditions, its quality relates to parameters of accessibility, accountability, comprehensiveness, continuity, coordination, coverage, efficiency, quality, and person-centeredness [33]. By contrast, animal health systems worldwide usually aim to utilize domestic animals for human needs effectively—food production, leisure, company, among others—including all the actors involved in livestock production and pet care [34, 35]. The efforts on animal health are in place on increasing productivity for food production, reducing losses due to animal morbidity and mortality, livestock identification and traceability, adopting animal welfare standards, and protecting humans from zoonotic diseases [36]. Moreover, the standards of care for animals are different based on the cultural and economic values, the expected use, the species, and the geographic location [37]. For both animal and human health, accessibility to health care services is one of the main challenges, particularly in remote geographical areas. The environmental health services sphere has the most significant disadvantage, rooted similarly to the animal health case, in ethical, moral, and legal principles that have not yet been able to address the conflicting issues of the improvements on health and well-being for humans and animals causing environmental damages [38, 39].

3.2 Water and Sanitation Services While water and sanitation supply for humans have been priorities for decades, the impact of inadequate sanitation and hygiene of animals is far less evaluated and still largely neglected [40]. Studies investigating the health effects of animals concerning improvements in the supply of water and sanitation are scarce [41]. If at all, animals are considered as a source of potential contamination, as inadequate sanitation management of animals can impact the natural environment, mainly through microbiological contamination of water and soils. Integrated water and sanitation services for supporting the health of humans, animals, and environmental health is still challenging. While interventions in the human domain of OH are commonly related to drinking water and are less focused on sanitation and hygiene [42], the veterinary public health and environmental domains are often not involved in WASH interventions [43]. Improving water and sanitation is a crucial strategy for the prevention and control of diseases, particularly neglected zoonotic diseases and antimicrobial resistance (AMR). The increasing global burden of AMR is putting at stake the health of humans, animals, and the environment. Among the different AMR characteristics, antibiotic resistance (ABR) is one of the greatest public health challenges of our times [44, 45]. The lack of hygiene in human and animal

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healthcare facilities at the community and the household levels [46] are important reasons for the increased burden of AMR. Moreover, environmental aspects, such as waste disposal and water compartments [47, 48] that allow for ABR elements to further spread through various interactions with the water cycle, e.g., swimming, bathing, washing, or using surface or groundwater sources as irrigation or drinking water, play a role in this context. Despite this, sanitation and hygienic measures while handling livestock differ by cultures and between geographical areas, and more significant differences can be found between developed and developing countries, specifically for handwashing, use of personal protective measures, such as gloves, boots, and masks, level of cleanliness in households and animal shelters, and manure management [49].

3.3 Provision of Green Spaces Ample evidence reports the benefits of nature to human health and well-being (see Sandifer et al. [50] for a review). Although the causality is still not fully comprehended, this effect may involve the following three main pathways: i. reduction of exposure to environmental stressors (mitigation); ii. facilitation of physical activities and social cohesion (instauration); and iii. recovery of psychological competences (restoration) [51]. Thereby, urban green spaces and street trees correlate with several health outcomes, including lower antidepressant prescriptions [52], decreased cardiovascular mortality, and reduced asthma incidence [53]. More recently, an emerging field of research has investigated the relationship between biodiversity supported by green spaces and the human microbiota—microbial diversity more precisely–, and the possible benefits in terms of risk reduction of immune-related diseases [54–56]. Besides the benefits to urban dwellers, green infrastructure also contributes to animal health and conservation. Urban green spaces can harbor native and threatened animal species [57, 58]. As a network of connected green elements, green infrastructure forms stepping stones and corridors that enables animal movement and, thereby, connectivity between habitat patches [59, 60]. It is crucial considering that several urban areas overlap regions of high biodiversity importance [61], and therefore cities can also be key areas for wildlife conservation. Additionally, green spaces are important for domestic animals’ welfare (e.g., dogs), as these are the places where people usually exercise their pets.

3.4 Food System Feeding a continually growing world population has become an extraordinary challenge. There are multiple health implications that concern food systems. The most cited are:

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i. three dimensions of malnourishment coexisting simultaneously around the globe [62], about 820 million people of the world’s population are undernourished, 2 billion people have a micronutrient deficiency, and there is no single region of the world not affected by the obesity and overweight epidemic [63, 64]; and ii. the overconsumption of high energy and low-nutrient foods are underlying the main causes of death worldwide, linked to diet-related chronic diseases, such as cardiovascular diseases, stroke, or diabetes [65–67], and also affecting mental health [68]. Foodborne diseases and the emergence and re-emergence of pathogens along the food chain pose additional major health challenges [69, 70]. Animals suffer specific species-related diseases, particularly within industrialized agriculture, not to mention the disagreements on the parameters and difficulties in evaluating animal welfare [71]. Furthermore, the capacities of Earth’s planetary boundaries are under stress concerning ongoing ecological disruptions derived from food production and consumption, more specifically, climate change, biogeochemical flows, and biosphere integrity, among others [72–75].

3.5 Land-Use Planning and Conservation The nexus between land-use change and health is complex. For example, unsustainable or unplanned urban infrastructure development may lead to reduced soil permeability, vegetation cover loss, degradation of water systems, and their associated health-promoting services to humans and animals [76–78]. The expansion of urban settlements into forest areas and wetlands posed major challenges to wildlife in many regions worldwide [79]. Estimates say that by 2050, human activities, through land-use change, are expected to have a significant impact on virus dispersal than climate change [80]. Given the potentials of green infrastructure in reducing the chances of flooding and the risk of water-borne disease transmission [81, 82], there is an increase in competition for land between housing and other grey infrastructure and green infrastructure [83, 84]. Consequently, unregulated land use can pose major disturbances to biodiversity and pathogen reservoirs, which generate negative feedback loops to health. Sustainable land-use planning and the regulation of agricultural activities can reduce the odds of soil contamination, water pollution, and the risk of infectious diseases by minimizing the application of fertilizers and the usage of untreated wastewater for crop irrigation [85, 86].

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OH Actions to Promote Public Health Services: Enabling Health and Reducing Risks

In public services, the inclusion of the animal and environment domains as areas that serve beyond the human domain faces numerous institutional challenges derived from the integration of highly specialized areas. However, a window of opportunity has opened in the last decades, as the classical top-down service provision approach has shifted towards a more collaborative vision, based on the co-production of services by users and their communities [87]. Here, OH actions are the activities implemented for improving health in the human, animal, and environmental domains, collaboratively tackling at least two of the health domains mentioned above.

4.1 Health Services Concerning healthcare, various OH actions have covered both the curative aspect— healthcare provision–, and the preventive aspect of health—with a strong focus on surveillance. Community-based schemes have emerged in response to budgetary and institutional constraints for the provision of health services. The actions of OH such as integrated basic healthcare, vaccination, de-worming, small animals’ birth control programs, education campaigns on infectious diseases, among others, can improve the general health status of a community, decrease the transmission of diseases in the present, and develop a base for healthy humans, animals, and the environment in the future. Some examples of integrated health service provision to humans and animals include combined vaccination campaigns in Africa with nomadic pastoralists [88], treatments for cattle and humans together in China to decrease parasite loads [89], and clinics where pets and humans are treated together [90]. Integrated surveillance at the human-animal-ecosystem interface (OH surveillance) is becoming a key approach across geographic regions [91]. The effort towards the OH surveillance system appears at multiple dimensions formed by decision making-scales, disciplines, public–private partnerships, and sectors. It can occur at various surveillance process steps with a variable degree of integration, going from planning to disseminating results [92]. Further, the latest global efforts to promote healthy environments have become crucial strategies for disease prevention [93]. Recent epidemics have shown that many containment strategies historically developed are no longer efficient, changing the emphasis on early detection measures to identify pathogens in the environment and reducing environment-toanimal and animal-to-human transmission [94]. OH surveillance is an opportunity to strengthen the healthcare system and the early detection of risks. The integration of expertise from disciplines such as mathematics, computer sciences, spatial statistics, data sciences (machine learning, deep learning), and data engineering

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have a great potential to scale surveillance and epidemic preparedness through data streaming and forecasting of outbreaks as planned by the Epidemic Intelligence of the European Center for Disease Prevention and Control [95, 96].

4.2 Water and Sanitation Services Sanitation and hygienic measures for handling livestock require guidelines “to put the “A” (animal) in WASH” [40] that can help to maintain food safety and quality at all levels of the food supply chain, contributing to disease control and improving animal welfare [43]. Recent empirical evidence, as in the works of Patel et al. [97] and Falkenberg et al. [98], have highlighted that the maintenance of hygiene measures, particularly handwashing, as well as manure management of livestock, are important to prevent further transmission of infectious disease pathogens, which could prevent potential future epidemics. Besides the WASH interventions, where public services can be enablers of health, wastewater surveillance has allowed, for instance, the early detection of epidemics of hepatitis A virus and norovirus in Sweden [99] and the identification of E. coli (Escherichia coli) in irrigation water in India [98]. Wastewater surveillance also seems a promising strategy for pathogen identification to evaluate the burden of AMR [100–103].

4.3 Green Spaces Provision Many of the past and current epidemics have originated from wildlife, and some of them share domestic animals as intermediary hosts, such as avian influenza, MERS, Ebola, COVID-19. Therefore, it concerns that urban green spaces may attract and harbor various wildlife species, such as bats, monkeys, and birds, that can either migrate from adjacent natural habitats and establish their territories in urban areas or use urban green areas as stepping stones. Many of those animals are hosts or reservoirs of pathogens that can potentially spill over to humans [104]. Here, urban green infrastructure can play a role in mitigating disease outbreaks, as the urban biodiversity could be considered a part of an animal surveillance network. In terms of prevention, a surveillance system for animal health in urban green spaces can be quite useful, taking advantage of their easy accessibility. Systematic samplings of animal populations may identify either fluctuation in the prevalence of diseases or the incidence of new pathogens. It could lead to earlier detection of potential risks to human health and, consequently, better response in preventing eventual outbreaks. However, surveillance within urban areas must complement and not substitute close monitoring in wild areas, especially in transitionary zones between human and natural landscapes where the emergence of zoonotic diseases has already been documented [104].

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Green infrastructure may also reduce the magnitude of outbreaks’ outcomes. Recently, studies have suggested that higher air pollution levels could also influence an increased number of cases and mortality due to COVID-19 [105, 106]. Taking the current pandemic of COVID-19 as an example, environmental factors correlate with the severity of symptoms and increased mortality [107, 108]. It implies that urban green may be a useful public health strategy to prevent diseases since it is associated with reduced air pollution levels [109] and lower prevalence and reduced risks of comorbidities such as hypertension [83], cardiovascular diseases [84, 110], and type 2 diabetes mellitus [111]. Moreover, urban green spaces may mitigate the mental health burden of restrictive measures and quarantines, being places for mental restoration and exercises while enabling social distancing.

4.4 Food Systems The OH approach can guide initiatives that prevent future outbreaks of novel pathogens altogether, reduce the environmental impact, and promote a reduction in animal-based foods to improve human health [72, 112–114]. First, considering that in sub-therapeutic doses, antibiotics offer growth-promoting effects in many intensive farming facilities [115, 116], combined with crowded and unsanitary conditions and numerous occupational health risks in the food chain [117], it is no surprise that antibiotic-resistant bacteria have been found on meat products [118, 119] and animal sewage [120–122]. To tackle ABR (antibiotic resistance) exposure risks, interventions, such as the act of livestock sector to ban the use of antibiotics as growth promoters [123] and to improve livestock keeping conditions [124], could lead to the reduction of antibiotics use [125]. Second, the environmental impacts linked to food production and consumption, including climate change, biodiversity loss, water and soil pollution, eutrophication, and alteration of the biochemical nutrient cycles, together with the health effects linked to the food chain, call for changes in the food systems that can potentially address both health and sustainability [72, 126, 127]. Moreover, OH research can also comprehensively assess the sustainability aspects of dietary patterns, including nutritional health outcomes, environmental impacts of food production [128, 129], and animal welfare outcomes [130, 131]. As the majority of the forms of human development to meet the needs of an increasing human population lie in the intensification of natural resources base, challenges to environmental health can emerge [132, 133]. Before the COVID-19 pandemic, aspects linked to food safety, habitat encroachment, animal welfare, and wildlife trade were neglected [134]. The critical point of the spread of the SARS-COV-2 virus to humans is a lack of proper safety measures, i.e., hygiene, knowledge of disease transmission, and surveillance, at local markets that created the preconditions for zoonotic spillover from animals and humans [135].

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4.5 Land Use Planning and Conservation Human-animal-environment interactions take place mainly through land-use dynamics. Moreover, urban population growth and urbanization have increased the demand for housing, food, roads, energy, manufactured goods, and tourism, with varying effects on ecosystem services [136, 137]. Therefore, coordinated and participatory land-use planning can promote urban ecosystem conservation and their services and contribute significantly to flood mitigation, erosion control, climate regulations, and clean water systems. The integration of land use planning into OH can address the complex interactions between land-use change and the emergence of diseases [138–140]. Further, land use planning and the protection of forestlands can reduce the prevalence of zoonotic diseases through reduced human contact with wildlife [10, 141]. Besides, sustainable land use will not only contribute to the generation of services that promote human, animal, and environmental health but also regulate the microclimate and its associated health outcomes. Integrated land-use planning is a sustainable strategy for both the achievement of targets of sustainability and for the mitigation of future epidemics.

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Conclusion

A holistic approach like the OH is necessary to address the interlinkages between health and public services, either from a single service perspective or in a combined way. Such has health implications for humans, animals, and the environment. Various aspects of value in aiding OH actions include integrated health services, WASH interventions, wastewater surveillance, green infrastructure provision, regulation of food systems dynamics, and coordinated and participatory land-use planning. This chapter concluded that collaborative provision and proper utilization of services, coupled with a holistic understanding of multi-sectoral collaborations promoted by OH, can provide a common ground for health action. However, lack of collaboration strategies and the attitude and practice towards maintaining the health of humans and neglecting the health of other species have created significant barriers for collaborations. Through integrated science and action, sustainable and efficient strategies to prevent future epidemics are possible. Here, we took a first step towards showing the direct and indirect health implications of public services, information that can advise the actors involved in public services to actively promote health, respond to health risks, and improve the capability to cope with a crisis during health emergencies (Tables 1 and 2).

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Table 1 Some integrated initiatives for public service provisions Healthcare services

Water supply and sanitation services

Urban green infrastructure

Campaigns for joint human and animal vaccination to nomadic pastoralist communities in Chad Combined One Health clinical services for human and animals established for the underserved communities in the United States OH surveillance for disease prevention and early detection Methicillin-resistant Staphylococcus Aureus (MRSA) in human with or without animal contact and the surroundings (milk as well), highlighting the importance of environmental contamination with resistant bacteria High level of E-Coli in the wastewater, which is used by urban farmers for irrigation purposes The emergence of green prescription as a nature-based health intervention In Mexico, even vacant lots could be important reservoirs of bird diversity in urban areas Importance of green space use during COVID pandemic

Schelling et al. [88] Sweeney et al. [90] Bordier et al. [92] Patel et al. [97]

Falkenberg [98] Robinson et al. [142] Palacios et al. [143] Slater et al. [144]

Table 2 Integrated public service utilization and health implications Food systems

Animal-based products and environmental impacts from diets Animal-based food systems and bridging of zoonotic pathogens in the ongoing COVID-19 pandemic Sustainability of diets and health outcomes Food system and animal welfare outcomes

Land use planning

Poor land use and indiscriminate wastes disposal contributes to the choking of drainage systems, leading to flash floods and the outbreak of cholera, diarrhea, malaria, and typhoid fever, weeks after flood disasters In Ghana, 80% of the annual cases of schistosomiasis are reported in areas with dams, ponds, and irrigation canals Uncontrolled urban expansion reduces the physical distance between humans and wild animals and increase exposure to zoonotic diseases

Westhoek et al. [145], Roos et al. [146] Kock et al. [147] Heller et al. [128], Stylianou et al. [129] Scherer et al. [130], Weele et al. [131] Karikari et al. [148]

Kulinkina et al. [149]

Burkett-Cadena and Vittor [150], Pizzitutti et al. [151]

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Core Messages

• The attainment of development goals through public services is dependent on various factors. • A holistic understanding of multi-sectoral collaborations within small administrative units is necessary. • Integrated health services provision/interventions, coordinated actions, and regulations are needed. • The shift from classical top-down service provision approach towards a more collaborative vision is recommended.

References 1. Jones BD, Kaufman C (1974) The distribution of urban public services. Adm Soc 6:337– 360. https://doi.org/10.1177/009539977400600304 2. Sandfort J, Milward HB (2008) Collaborative service provision in the public sector. Oxford University Press 3. Brandsen T, Pestoff V (2006) Co-production, the third sector and the delivery of public services. Public Manag Rev 8:493–501. https://doi.org/10.1080/14719030601022874 4. Karlsson PS (2015) Risk governance in collaborative public service provision: comparative study between Scotland, Finland, and Sweden. Turku 5. One Health Commission (2020) What is one health?—One Health Commission. In: One Health Commission 6. Bruchhausen W (2019) Emerging global health approaches at the human-animal interface: conceptual and historical issues of one health. In: Yasobant S, Saxena D (eds) Global applications of One Health practice and care. IGI Global, pp 1–32 7. Gibbs EPJ (2014) The evolution of one health: a decade of progress and challenges for the future. Vet Rec 174:85–91. https://doi.org/10.1136/vr.g143 8. Zinsstag J, Schelling E, Waltner-Toews D, Tanner M (2011) From “one medicine” to “one health” and systemic approaches to health and well-being. Prev Vet Med 101:148–156. https://doi.org/10.1016/j.prevetmed 9. Centers for Disease Control and Prevention (2019) One health basics. U.S. Dep. Heal. Hum. Serv. https://www.cdc.gov/onehealth/basics/index.html. Accessed 14 Jul 2020 10. Tarazona AM, Ceballos MC, Broom DM (2019) Human relationships with domestic and other animals: one health, one welfare, one biology. Animals 10:43. https://doi.org/10.3390/ ani10010043 11. Degeling C, Johnson J, Kerridge I, Wilson A, Ward M, Stewart C, Gilbert G (2015) Implementing a one health approach to emerging infectious disease: reflections on the socio-political, ethical and legal dimensions. BMC Public Health 15:1307. https://doi.org/10. 1186/s12889-015-2617-1 12. Schmiege D, Perez A, Ntajal J, Paris J, Savi K, Patel K, Yasobant S, Falkenberg T (2020) One health in the context of coronavirus outbreaks: a systematic literature review. One Health 10 13. Lebov J, Grieger K, Womack D, Zaccaro D, Whitehead N, Kowalcyk B, MacDonald PDM (2017) A framework for one health research. One Health (Amsterdam, Netherlands) 3:44– 50. https://doi.org/10.1016/j.onehlt.2017.03.004

3

One Health as an Integrated Approach: Perspectives …

63

14. World Health Organization (2018) Primary health care as an enabler for “ending the epidemics” of high-impact communicable diseases. Geneva 15. Tognotti E (2013) Lessons from the history of quarantine, from plague to influenza A. Emerg Infect Dis 19:254–259. https://doi.org/10.3201/eid1902.120312 16. Bedford J, Farrar J, Ihekweazu C, Kang G, Koopmans M, Nkengasong J (2019) A new twenty-first century science for effective epidemic response. Nature 575:130–136. https:// doi.org/10.1038/s41586-019-1717-y 17. Unger J-P, De Paepe P, Green A (2003) A code of best practice for disease control programmes to avoid damaging health care services in developing countries. Int J Health Plann Manag 18:S27–S39. https://doi.org/10.1002/hpm.723 18. United Nations General Assembly (2010) Resolution adopted by the General Assembly on 28 July 2010. 64/292. The human right to water and sanitation, New York 19. United Nations Children’s Fund (UNICEF), World Health Organization (WHO) (2019) Progress on household drinking water, sanitation and hygiene 2000–2017. Special focus on inequalities, New York 20. UNICEF & WHO (2018) Drinking water, sanitation and hygiene in schools: global baseline report 2018. New York 21. Prüss-Ustün A, Wolf J, Bartram J, Clasen T, Cumming O, Freeman MC, Gordon B, Hunter PR, Medlicott K, Johnston R (2019) Burden of disease from inadequate water, sanitation and hygiene for selected adverse health outcomes: an updated analysis with a focus on low- and middle-income countries. Int J Hyg Environ Health 222:765–777. https:// doi.org/10.1016/j.ijheh.2019.05.004 22. Bartesaghi Koc C, Osmond P, Peters A (2017) Towards a comprehensive green infrastructure typology: a systematic review of approaches, methods and typologies. Urban Ecosyst 20:15–35. https://doi.org/10.1007/s11252-016-0578-5 23. Boulton C, Dedekorkut-Howes A, Byrne J (2018) Factors shaping urban greenspace provision: a systematic review of the literature. Landsc Urban Plan 178:82–101. https://doi. org/10.1016/j.landurbplan.2018.05.029 24. Kabisch N, Strohbach M, Haase D, Kronenberg J (2016) Urban green space availability in European cities. Ecol Indic 70:586–596. https://doi.org/10.1016/j.ecolind.2016.02.029 25. Bowler DE, Buyung-Ali L, Knight TM, Pullin AS (2010) Urban greening to cool towns and cities: a systematic review of the empirical evidence. Landsc Urban Plan 97:147–155. https://doi.org/10.1016/j.landurbplan.2010.05.006 26. Cohen P, Potchter O, Schnell I (2014) The impact of an urban park on air pollution and noise levels in the Mediterranean city of Tel-Aviv, Israel. Environ Pollut 195:73–83. https://doi. org/10.1016/j.envpol.2014.08.015 27. Klemm W, Heusinkveld BG, Lenzholzer S, Jacobs MH, Van Hove B (2015) Psychological and physical impact of urban green spaces on outdoor thermal comfort during summertime in The Netherlands. Build Environ 83:120–128. https://doi.org/10.1016/j.buildenv.2014.05. 013 28. Pinstrup-Andersen P, Fan S, Pandya-Lorch R (2012) The food system and its interaction with human health and nutrition. Reshap Agric Nutr Heal IFPRI 2020 B 21–29 29. Gomez MI, Barrett CB, Buck LE, De Groote H, Ferris S, Gao HO, McCullough E, Miller DD, Outhred H, Pell AN, Reardon T, Retnanestri M, Ruben R, Struebi P, Swinnen J, Touesnard MA, Weinberger K, Keatinge JDH, Milstein MB, Yang RY (2011) Research principles for developing country food value chains. Science (80–) 332:1154–1155. https:// doi.org/10.1126/science.1202543 30. Food and Agriculture Organization of the United Nations (FAO) (2018) Sustainable food systems. Concept and framework 31. Nunalee MMM, Weedon GR (2004) Modern trends in veterinary malpractice: how our evolving attitudes toward non-human animals will change veterinary medicine. Anim Law 125–161

64

S. Yasobant et al.

32. Leonard DK (1993) Structural reform of the veterinary profession in Africa and the new institutional economics. Dev Change 24:227–267. https://doi.org/10.1111/j.1467-7660. 1993.tb00485.x 33. World Health Organization (2010) Health service delivery. Geneva 34. Perry BD, Robinson TP, Grace DC (2018) Review: animal health and sustainable global livestock systems. Animal 12:1699–1708. https://doi.org/10.1017/S1751731118000630 35. Rabinowitz P, Conti L (2013) Links among human health, animal health, and ecosystem health. Annu Rev Public Health 34:189–204. https://doi.org/10.1146/annurev-publhealth031912-114426 36. Food and Agriculture Organization of the United Nations (1991) Guidelines for strengthening animal health services in developing countries. Rome 37. Hankin SJ (2009) Making decisions about our animals’ health care: does it matter whether we are owners or guardians? Stanford J Anim Law Policy 2:1–51 38. Food and Agriculture Organization of the United Nations (2014) Livestock and animal production. In: Sustain. Dev. Goals. http://www.fao.org/animal-production/en/ 39. Watts N, Amann M, Arnell N, Ayeb-Karlsson S, Belesova K, Boykoff M, Byass P, Cai W, Campbell-Lendrum D, Capstick S, Chambers J, Dalin C, Daly M, Dasandi N, Davies M, Drummond P, Dubrow R, Ebi KL, Eckelman M, Ekins P, Escobar LE, Fernandez Montoya L, Georgeson L, Graham H, Haggar P, Hamilton I, Hartinger S, Hess J, Kelman I, Kiesewetter G, Kjellstrom T, Kniveton D, Lemke B, Liu Y, Lott M, Lowe R, Sewe MO, Martinez-Urtaza J, Maslin M, McAllister L, McGushin A, Jankin Mikhaylov S, Milner J, Moradi-Lakeh M, Morrissey K, Murray K, Munzert S, Nilsson M, Neville T, Oreszczyn T, Owfi F, Pearman O, Pencheon D, Phung D, Pye S, Quinn R, Rabbaniha M, Robinson E, Rocklöv J, Semenza JC, Sherman J, Shumake-Guillemot J, Tabatabaei M, Taylor J, Trinanes J, Wilkinson P, Costello A, Gong P, Montgomery H (2019) The 2019 report of The Lancet Countdown on health and climate change: ensuring that the health of a child born today is not defined by a changing climate. Lancet 394:1836–1878. https://doi.org/10.1016/ S0140-6736(19)32596-6 40. Prendergast AJ, Gharpure R, Mor S, Viney M, Dube K, Lello J, Berger C, Siwila J, Joyeux M, Hodobo T, Hurt L, Brown T, Hoto P, Tavengwa N, Mutasa K, Craddock S, Chasekwa B, Robertson RC, Evans C, Chidhanguro D, Mutasa B, Majo F, Smith LE, Hirai M, Ntozini R, Humphrey JH, Berendes D (2019) Putting the “A” into WaSH: a call for integrated management of water, animals, sanitation, and hygiene. Lancet Planet Health 3: e336–e337. https://doi.org/10.1016/S2542-5196(19)30129-9 41. Elahi E, Abid M, Zhang L, Alugongo GM (2017) The use of wastewater in livestock production and its socioeconomic and welfare implications. Environ Sci Pollut Res 24:17255–17266. https://doi.org/10.1007/s11356-017-9263-3 42. Yates C, Allen T, Joseph J, Lantagne M (2017) WASH interventions in disease outbreak response 43. Matilla F, Velleman Y, Harrison W, Nevel M (2018) Animal influence on water, sanitation and hygiene measures for zoonosis control at the household level: a systematic literature review. PLoS Negl Trop Dis 12:e0006619. https://doi.org/10.1371/journal.pntd.0006619 44. Laxminarayan R, Duse A, Wattal C, Zaidi AKM, Wertheim HFL, Sumpradit N, Vlieghe E, Hara GL, Gould IM, Goossens H, Greko C, So AD, Bigdeli M, Tomson G, Woodhouse W, Ombaka E, Peralta AQ, Qamar FN, Mir F, Kariuki S, Bhutta ZA, Coates A, Bergstrom R, Wright GD, Brown ED, Cars O (2013) Antibiotic resistance-the need for global solutions. Lancet Infect Dis 13:1057–1098. https://doi.org/10.1016/S1473-3099(13)70318-9 45. Ben Y, Fu C, Hu M, Liu L, Wong MH, Zheng C (2019) Human health risk assessment of antibiotic resistance associated with antibiotic residues in the environment: a review. Environ Res 169:483–493. https://doi.org/10.1016/j.envres.2018.11.040 46. Collignon P, Beggs JJ (2019) Socioeconomic enablers for contagion: factors impelling the antimicrobial resistance epidemic. Antibiotics 8:86. https://doi.org/10.3390/ antibiotics8030086

3

One Health as an Integrated Approach: Perspectives …

65

47. Andremont A, Walsh TR (2015) The role of sanitation in the development and spread of antimicrobial resistance 48. Pruden A, Joakim Larsson DG, Amézquita A, Collignon P, Brandt KK, Graham DW, Lazorchak JM, Suzuki S, Silley P, Snape JR, Topp E, Zhang T, Zhu YG (2013) Management options for reducing the release of antibiotics and antibiotic resistance genes to the environment. Environ Health Perspect 121:878–885. https://doi.org/10.1289/ehp. 1206446 49. Odo NU, Raynor PC, Beaudoin A, Somrongthong R, Scheftel JM, Donahue JG, Bender JB (2015) Personal protective equipment use and handwashing among animal farmers: a multi-site assessment. J Occup Environ Hyg 12:363–368. https://doi.org/10.1080/15459624. 2015.1006635 50. Sandifer PA, Sutton-Grier AE, Ward BP (2015) Exploring connections among nature, biodiversity, ecosystem services, and human health and well-being: opportunities to enhance health and biodiversity conservation. Ecosyst Serv 12:1–15. https://doi.org/10.1016/j.ecoser. 2014.12.007 51. Markevych I, Schoierer J, Hartig T, Chudnovsky A, Hystad P, Dzhambov AM, de Vries S, Triguero-Mas M, Brauer M, Nieuwenhuijsen MJ, Lupp G, Richardson EA, Astell-Burt T, Dimitrova D, Feng X, Sadeh M, Standl M, Heinrich J, Fuertes E (2017) Exploring pathways linking greenspace to health: theoretical and methodological guidance. Environ Res 158:301–317. https://doi.org/10.1016/j.envres.2017.06.028 52. Taylor MS, Wheeler BW, White MP, Economou T, Osborne NJ (2015) Research note: urban street tree density and antidepressant prescription rates—a cross-sectional study in London, UK. Landsc Urban Plan 136:174–179. https://doi.org/10.1016/j.landurbplan.2014. 12.005 53. Twohig-Bennett C, Jones A (2018) The health benefits of the great outdoors: a systematic review and meta-analysis of greenspace exposure and health outcomes. Environ Res 166:628–637. https://doi.org/10.1016/j.envres.2018.06.030 54. Flies EJ, Skelly C, Negi SS, Prabhakaran P, Liu Q, Liu K, Goldizen FC, Lease C, Weinstein P (2017) Biodiverse green spaces: a prescription for global urban health. Front Ecol Environ 15:510–516. https://doi.org/10.1002/fee.1630 55. Liddicoat C, Bi P, Waycott M, Glover J, Lowe AJ, Weinstein P (2018) Landscape biodiversity correlates with respiratory health in Australia. J Environ Manag 206:113–122. https://doi.org/10.1016/j.jenvman.2017.10.007 56. Liddicoat C, Waycott M, Weinstein P (2016) Environmental change and human health: can environmental proxies inform the biodiversity hypothesis for protective microbial-human contact? Bioscience 66:1023–1034. https://doi.org/10.1093/biosci/biw127 57. Aronson MF, Lepczyk CA, Evans KL, Goddard MA, Lerman SB, MacIvor JS, Nilon CH, Vargo T (2017) Biodiversity in the city: key challenges for urban green space management. Front Ecol Environ 15:189–196. https://doi.org/10.1002/fee.1480 58. Ives CD, Lentini PE, Threlfall CG, Ikin K, Shanahan DF, Garrard GE, Bekessy SA, Fuller RA, Mumaw L, Rayner L, Rowe R, Valentine LE, Kendal D (2016) Cities are hotspots for threatened species. Glob Ecol Biogeogr 25:117–126. https://doi.org/10.1111/ geb.12404 59. Dearborn DC, Kark S (2010) Motivations for conserving urban biodiversity. Conserv Biol 24:432–440. https://doi.org/10.1111/j.1523-1739.2009.01328.x 60. Garmendia E, Apostolopoulou E, Adams WM, Bormpoudakis D (2016) Biodiversity and green infrastructure in Europe: boundary object or ecological trap? Land Use Policy 56:315– 319. https://doi.org/10.1016/j.landusepol.2016.04.003 61. McDonald RI, Güneralp B, Huang C-W, Seto KC, You M (2018) Conservation priorities to protect vertebrate endemics from global urban expansion. Biol Conserv 224:290–299. https://doi.org/10.1016/j.biocon.2018.06.010

66

S. Yasobant et al.

62. Gómez MI, Barrett CB, Raney T, Pinstrup-Andersen P, Meerman J, Croppenstedt A, Carisma B, Thompson B (2013) Post-green revolution food systems and the triple burden of malnutrition. Food Policy 42:129–138. https://doi.org/10.1016/j.foodpol.2013.06.009 63. FAO, Ifad, UNICEF, WFP, WHO (2019) The state of food security and nutrition in the world 2019. Safeguarding against economic slowdowns and downturns, Rome 64. WHO (2020) Micronutrient deficiencies. World Health Organisation. https://www.who.int/ nutrition/topics/ida/en/ 65. The GBD (2015) Obesity Collaborators (2017) Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med 377:13–27. https://doi.org/10.1056/ NEJMoa1614362 66. Fiolet T, Srour B, Sellem L, Kesse-Guyot E, Allès B, Méjean C, Deschasaux M, Fassier P, Latino-Martel P, Beslay M, Hercberg S, Lavalette C, Monteiro CA, Julia C, Touvier M (2018) Consumption of ultra-processed foods and cancer risk: results from NutriNet-Santé prospective cohort. BMJ 360. https://doi.org/10.1136/bmj.k322 67. Siegel KR, McKeever Bullard K, Imperatore G, Kahn HS, Stein AD, Ali MK, Narayan KM (2016) Association of higher consumption of foods derived from subsidized commodities with adverse cardiometabolic risk among US adults. JAMA Intern Med 176:1124–1132. https://doi.org/10.1001/jamainternmed.2016.2410 68. Zahedi H, Kelishadi R, Heshmat R, Motlagh ME, Ranjbar SH, Ardalan G, Payab M, Chinian M, Asayesh H, Larijani B, Qorbani M (2014) Association between junk food consumption and mental health in a national sample of Iranian children and adolescents: the CASPIAN-IV study. Nutrition 30:1391–1397. https://doi.org/10.1016/j.nut.2014.04.014 69. AL-Mamun M, Chowdhury T, Biswas B, Absar N, (2018) Food poisoning and intoxication: a global leading concern for human health. Elsevier Inc 70. Pradyumna A, Egal F, Utzinger J (2019) Sustainable food systems, health and infectious diseases: concerns and opportunities. Acta Trop 191:172–177. https://doi.org/10.1016/j. actatropica.2018.12.042 71. OIE (2019) Terrestrial animal health code-introduction to the recommendations for animal welfare 72. Willett W, Rockström J, Loken B, Springmann M, Lang T, Vermeulen S, Garnett T, Tilman D, DeClerck F, Wood A, Jonell M, Clark M, Gordon LJ, Fanzo J, Hawkes C, Zurayk R, Rivera JA, De Vries W, Majele Sibanda L, Afshin A, Chaudhary A, Herrero M, Agustina R, Branca F, Lartey A, Fan S, Crona B, Fox E, Bignet V, Troell M, Lindahl T, Singh S, Cornell SE, Srinath Reddy K, Narain S, Nishtar S, Murray CJLL (2019) Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393:447–492. https://doi.org/10.1016/S0140-6736(18)31788-4 73. Rockström J, Steffen W, Noone K, Persson Å, Chapin FS, Lambin EF, Lenton TM, Scheffer M, Folke C, Schellnhuber HJ, Nykvist B, De Wit CA, Hughes T, Van Der Leeuw S, Rodhe H, Sörlin S, Snyder PK, Costanza R, Svedin U, Falkenmark M, Karlberg L, Corell RW, Fabry VJ, Hansen J, Walker B, Liverman D, Richardson K, Crutzen P, Foley JA (2009) A safe operating space for humanity. Nature 461:472–475. https://doi.org/10.1038/ 461472a 74. Steffen W, Richardson K, Rockström J, Cornell SE, Fetzer I, Bennett EM, Biggs R, Carpenter SR, De Vries W, De Wit CA, Folke C, Gerten D, Heinke J, Mace GM, Persson LM, Ramanathan V, Reyers B, Sörlin S (2015) Planetary boundaries: guiding human development on a changing planet. Science (80–) 347. https://doi.org/10.1126/ science.1259855 75. Hribar C (2010) Understanding concentrated animal feeding operations and their impact on communities. Ohio 76. Deilami K, Hayes JF, McGree J, Goonetilleke A (2017) Application of landscape epidemiology to assess potential public health risk due to poor sanitation. J Environ Manag 192:124–133. https://doi.org/10.1016/j.jenvman.2017.01.051

3

One Health as an Integrated Approach: Perspectives …

67

77. Cui H, Zhou X, Guo M, Wu W (2016) Land use change and its effects on water quality in typical inland lake of arid area in China. J Environ Biol 37:603–609 78. Wilson CO (2015) Land use/land cover water quality nexus: quantifying anthropogenic influences on surface water quality. Environ Monit Assess 187. https://doi.org/10.1007/ s10661-015-4666-4 79. Kramer R, Herrera J, Fitzgerald R, Moody J, Owens A, Pender M, Ravelomanantsoa NAF, Regula L, Tortosa P, Nunn C (2019) Biodiversity, land use change, and human health in northeastern Madagascar: an interdisciplinary study. Lancet Planet Heal 3:S7. https://doi. org/10.1016/s2542-5196(19)30150-0 80. Brattig NW, Tanner M, Bergquist R, Utzinger J (2019) Impact of environmental changes on infectious diseases: key findings from an international conference in Trieste, Italy in May 2017. Acta Trop 105165 81. Black PF, Butler CD (2014) One health in a world with climate change. Rev Sci Tech 33:465–473. https://doi.org/10.20506/rst.33.2.2293 82. Thomson MC, Muñoz ÁG, Cousin R, Shumake-Guillemot J (2018) Climate drivers of vector-borne diseases in Africa and their relevance to control programmes. Infect Dis Poverty 7:1–22. https://doi.org/10.1186/s40249-018-0460-1 83. Moreira TCL, Polizel JL, de Santos IS, Silva Filho DF, Bensenor I, Lotufo PA, Mauad T (2020) Green spaces, land cover, street trees and hypertension in the megacity of São Paulo. Int J Environ Res Public Health 17:725. https://doi.org/10.3390/ijerph17030725 84. Seo S, Choi S, Kim K, Kim SM, Park SM (2019) Association between urban green space and the risk of cardiovascular disease: a longitudinal study in seven Korean metropolitan areas. Environ Int 125:51–57. https://doi.org/10.1016/j.envint.2019.01.038 85. Green J, Brunton C (2014) Public health implications of land use change and agricultural intensification with respect to the Canterbury plains. Canterbury Dist Heal Board 1–67 86. Åsjö B, Kruse H (2006) Zoonoses in the emergence of human viral diseases. Perspect Med Virol 16:15–41. https://doi.org/10.1016/S0168-7069(06)16003-6 87. Bovaird T (2007) Beyond engagement and participation: user and community coproduction of public services. Public Adm Rev 67:846–860. https://doi.org/10.1111/j.1540-6210.2007. 00773.x 88. Schelling E, Bechir M, Ahmed MA, Wyss K, Randolph TF, Zinsstag J (2007) Human and animal vaccination delivery to remote nomadic families, Chad. Emerg Infect Dis 13:373– 379. https://doi.org/10.3201/eid1303.060391 89. Gray DJ, Williams GM, Li Y, Chen H, Forsyth SJ, Li RS, Barnett AG, Guo J, Ross AG, Feng Z, McManus DP (2009) A cluster-randomised intervention trial against Schistosoma japonicum in the Peoples’ Republic of China: bovine and human transmission. PLoS ONE 4:e5900. https://doi.org/10.1371/journal.pone.0005900 90. Sweeney JM, Crook PZ, Deeb-Sossa N, Tu B, Dear JD, Mazet JAK (2018) Clinical one health: a novel healthcare solution for underserved communities. One Heal 6:34. https://doi. org/10.1016/J.ONEHLT.2018.10.003 91. Bordier M, Delavenne C, Nguyen DTT, Goutard FL, Hendrikx P (2019) One health surveillance: a matrix to evaluate multisectoral collaboration. Front Vet Sci 6:109. https:// doi.org/10.3389/fvets.2019.00109 92. Bordier M, Uea-Anuwong T, Binot A, Hendrikx P, Goutard FL (2018) Characteristics of one health surveillance systems: a systematic literature review. Prev Vet Med. https://doi. org/10.1016/j.prevetmed.2018.10.005 93. Prüss-Ustün A, Wolf J, Corvalán C, Bos R, Neira MP (2016) Preventing disease through healthy environments: a global assessment of the burden of disease from environmental risks. Geneva 94. World Health Organization (2018) Key facts about major deadly diseases managing epidemics. Geneva

68

S. Yasobant et al.

95. Paquet C, Coulombier D, Kaiser R, Ciotti M (2006) Epidemic intelligence: a new framework for strengthening disease surveillance in Europe. Eurosurveillance 11:5–6. https://doi.org/10. 2807/esm.11.12.00665-en 96. Desai AN, Kraemer MUG, Bhatia S, Cori A, Nouvellet P, Herringer M, Cohn EL, Carrion M, Brownstein JS, Madoff LC, Lassmann B (2019) Real-time epidemic forecasting: challenges and opportunities. Heal Secur 17:268–275. https://doi.org/10.1089/hs.2019.0022 97. Patel KB, Saxena DB (2018) Self-reported selected zoonotic diseases among animal handlers in Ahmedabad city. Online J Public Health Inform 10. https://doi.org/10.5210/ ojphi.v10i1.8961 98. Falkenberg T (2016) Wastewater-irrigated urban agriculture in the context of WASH in Ahmedabad. University of Bonn, India 99. Schmidt C (2020) Watcher in the wastewater. Nat Biotechnol 38:917–920. https://doi.org/ 10.1038/s41587-020-0620-2 100. Aarestrup FM, Woolhouse MEJ (2020) Using sewage for surveillance of antimicrobial resistance. Science (80–) 367:630–632. https://doi.org/10.1126/science.aba3432 101. Hendriksen RS, Munk P, Njage P, van Bunnik B, McNally L, Lukjancenko O, Röder T, Nieuwenhuijse D, Pedersen SK, Kjeldgaard J, Kaas RS, Clausen PTLC, Vogt JK, Leekitcharoenphon P, van de Schans MGM, Zuidema T, de Roda Husman AM, Rasmussen S, Petersen B, Bego A, Rees C, Cassar S, Coventry K, Collignon P, Allerberger F, Rahube TO, Oliveira G, Ivanov I, Vuthy Y, Sopheak T, Yost CK, Ke C, Zheng H, Baisheng L, Jiao X, Donado-Godoy P, Coulibaly KJ, Jergović M, Hrenovic J, Karpíšková R, Villacis JE, Legesse M, Eguale T, Heikinheimo A, Malania L, Nitsche A, Brinkmann A, Saba CKS, Kocsis B, Solymosi N, Thorsteinsdottir TR, Hatha AM, Alebouyeh M, Morris D, Cormican M, O’Connor L, Moran-Gilad J, Alba P, Battisti A, Shakenova Z, Kiiyukia C, Ng’eno E, Raka L, Avsejenko J, Bērziņš A, Bartkevics V, Penny C, Rajandas H, Parimannan S, Haber MV, Pal P, Jeunen GJ, Gemmell N, Fashae K, Holmstad R, Hasan R, Shakoor S, Rojas MLZ, Wasyl D, Bosevska G, Kochubovski M, Radu C, Gassama A, Radosavljevic V, Wuertz S, Zuniga-Montanez R, Tay MYF, Gavačová D, Pastuchova K, Truska P, Trkov M, Esterhuyse K, Keddy K, Cerdà-Cuéllar M, Pathirage S, Norrgren L, Örn S, Larsson DGJ, Heijden T Van der, Kumburu HH, Sanneh B, Bidjada P, Njanpop-Lafourcade BM, Nikiema-Pessinaba SC, Levent B, Meschke JS, Beck NK, Van CD, Phuc N Do, Tran DMN, Kwenda G, Tabo D adjim, Wester AL, Cuadros-Orellana S, Amid C, Cochrane G, Sicheritz-Ponten T, Schmitt H, Alvarez JRM, Aidara-Kane A, Pamp SJ, Lund O, Hald T, Woolhouse M, Koopmans MP, Vigre H, Petersen TN, Aarestrup FM (2019) Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage. Nat Commun 10:1–12. https://doi.org/10.1038/ s41467-019-08853-3 102. Farkas K, Hillary LS, Malham SK, McDonald JE, Jones DL (2020) Wastewater and public health: the potential of wastewater surveillance for monitoring COVID-19. Curr Opin Environ Sci Heal 17:14–20. https://doi.org/10.1016/j.coesh.2020.06.001 103. La Rosa G, Della Libera S, Iaconelli M, Ciccaglione AR, Bruni R, Taffon S, Equestre M, Alfonsi V, Rizzo C, Tosti ME, Chironna M, Romanò L, Zanetti AR, Muscillo M (2014) Surveillance of hepatitis A virus in urban sewages and comparison with cases notified in the course of an outbreak, Italy 2013. BMC Infect Dis 14:419. https://doi.org/10.1186/14712334-14-419 104. Hassell JM, Begon M, Ward MJ, Fèvre EM (2017) Urbanization and disease emergence: dynamics at the wildlife–livestock–human interface. Trends Ecol Evol 32:55–67. https://doi. org/10.1016/j.tree.2016.09.012 105. Fattorini D, Regoli F (2020) Role of the chronic air pollution levels in the Covid-19 outbreak risk in Italy. Environ Pollut 264:114732. https://doi.org/10.1016/j.envpol.2020.114732 106. Persico C, Johnson KR (2020) Deregulation in a time of pandemic: does pollution increase coronavirus cases or deaths?

3

One Health as an Integrated Approach: Perspectives …

69

107. Sohrabi C, Alsafi Z, O’Neill N, Khan M, Kerwan A, Al-Jabir A, Iosifidis C, Agha R (2020) World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int J Surg 76:71–76. https://doi.org/10.1016/j.ijsu.2020.02.034 108. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, Wang B, Xiang H, Cheng Z, Xiong Y, Zhao Y, Li Y, Wang X, Peng Z (2020) Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA J Am Med Assoc 323:1061–1069. https://doi.org/10.1001/jama.2020.1585 109. Pugh TAM, MacKenzie AR, Whyatt JD, Hewitt CN (2012) Effectiveness of green infrastructure for improvement of air quality in urban street canyons. Environ Sci Technol 46:7692–7699. https://doi.org/10.1021/es300826w 110. Tamosiunas A, Grazuleviciene R, Luksiene D, Dedele A, Reklaitiene R, Baceviciene M, Vencloviene J, Bernotiene G, Radisauskas R, Malinauskiene V, Milinaviciene E, Bobak M, Peasey A, Nieuwenhuijsen MJ (2014) Accessibility and use of urban green spaces, and cardiovascular health: findings from a Kaunas cohort study. Environ Health 13:20. https:// doi.org/10.1186/1476-069X-13-20 111. Astell-Burt T, Feng X, Kolt GS (2014) Is neighborhood green space associated with a lower risk of type 2 diabetes? Evidence from 267,072 Australians. Diabetes Care 37:197–201. https://doi.org/10.2337/dc13-1325 112. Aboussaleh Y, Capone R, El BH (2017) Mediterranean food consumption patterns: low environmental impacts and significant health-nutrition benefits. Proc Nutr Soc 76:543–548. https://doi.org/10.1017/S0029665117001033 113. Donini LM, Dernini S, Lairon D, Serra-Majem L, Amiot M-J, del Balzo V, Giusti A-M, Burlingame B, Belahsen R, Maiani G, Polito A, Turrini A, Intorre F, Trichopoulou A, Berry ME (2016) A consensus proposal for nutritional indicators to assess the sustainability of a healthy diet: the Mediterranean diet as a case study. Front Nutr 3:37. https://doi.org/10. 3389/fnut.2016.00037 114. Kock RA, Karesh WB, Veas F, Velavan TP, Simons D, Mboera LEG, Dar O, Arruda LB, Zumla A (2020) 2019-nCoV in context: lessons learned? Lancet Planet Health 4:e87–e88. https://doi.org/10.1016/S2542-5196(20)30035-8 115. Dibner JJ, Richards JD (2005) Antibiotic growth promoters in agriculture: history and mode of action. Poult Sci 84:634–643. https://doi.org/10.1093/ps/84.4.634 116. Van Boeckel TP, Pires J, Silvester R, Zhao C, Song J, Criscuolo NG, Gilbert M, Bonhoeffer S, Laxminarayan R (2019) Global trends in antimicrobial resistance in animals in low- and middle-income countries. Science (80–) 365. https://doi.org/10.1126/science. aaw1944 117. Neyra RC, Vegosen L, Davis MF, Price L, Silbergeld EK (2012) Antimicrobial-resistant bacteria: an unrecognized work-related risk in food animal production. Saf Health Work 3:85–91. https://doi.org/10.5491/SHAW.2012.3.2.85 118. Davis GS, Waits K, Nordstrom L, Grande H, Weaver B, Papp K, Horwinski J, Koch B, Hungate BA, Liu CM, Price LB (2018) Antibiotic-resistant Escherichia coli from retail poultry meat with different antibiotic use claims. BMC Microbiol 18:1–7. https://doi.org/10. 1186/s12866-018-1322-5 119. Benning R (2019) Germanwatch analysis of chicken meat for antibiotic resistant pathogens 120. McEachran AD, Blackwell BR, Hanson JD, Wooten KJ, Mayer GD, Cox SB, Smith PN (2015) Antibiotics, bacteria, and antibiotic resistance genes: aerial transport from cattle feed yards via particulate matter. Environ Health Perspect 123:337–343. https://doi.org/10.1289/ ehp.1408555 121. Schulz J, Ruddat I, Hartung J, Hamscher G, Kemper N, Ewers C (2016) Antimicrobial-resistant Escherichia coli survived in dust samples for more than 20 years. Front Microbiol 7:1–10. https://doi.org/10.3389/fmicb.2016.00866 122. Savin M, Bierbaum G, Hammerl JA, Heinemann C, Parcina M, Sib E, Voigt A, Kreyenschmidt J (2020) Antibiotic-resistant bacteria and antimicrobial residues in wastewater and process water from German pig slaughterhouses and their receiving municipal

70

123. 124. 125.

126.

127.

128.

129.

130.

131.

132. 133. 134. 135.

136.

137.

S. Yasobant et al. wastewater treatment plants. Sci Total Environ 727:138788. https://doi.org/10.1016/j. scitotenv.2020.138788 Marshall BM, Levy SB (2011) Food animals and antimicrobials: impacts on human health. Clin Microbiol Rev 24:718–733. https://doi.org/10.1128/CMR.00002-11 Animal Welfare Institute (2018) The critical relationship between farm animal health and welfare. Washington, DC He Y, Yuan Q, Mathieu J, Stadler L, Senehi N, Sun R, Alvarez PJJ (2020) Antibiotic resistance genes from livestock waste: occurrence, dissemination, and treatment. npj Clean Water 3:1–11. https://doi.org/10.1038/s41545-020-0051-0 Dernini S, Berry E, Serra-Majem L, La Vecchia C, Capone R, Medina F, Aranceta-Bartrina J, Belahsen R, Burlingame B, Calabrese G, Corella D, Donini L, Lairon D, Meybeck A, Pekcan A, Piscopo S, Yngve A, Trichopoulou A (2017) Med Diet 4.0: the Mediterranean diet with four sustainable benefits. Public Health Nutr 20:1322–1330. https://doi.org/10. 1017/S1368980016003177 Bakalis S, Valdramidis V, Argyropoulos D, Ahrne L, Chen J, Cullen PJ, Cummins E, Datta AK, Emmanouilidis C, Foster T, Fryer P, Gouseti O, Hospido A, Knoerzer K, LeBail A, Marangoni A, Rao P, Schlüter O, Taoukis P, Xanathakis E, Van Impe J (2020) How COVID-19 changed our food systems and food security paradigms. Curr Res Food Sci 3:166–172. https://doi.org/10.1016/j.crfs.2020.05.003 Heller MC, Keoleian GA, Willett WC (2013) Toward a life cycle-based, diet-level framework for food environmental impact and nutritional quality assessment: a critical review. Environ Sci Technol 47:12632–12647. https://doi.org/10.1021/es4025113 Stylianou KS, Heller MC, Fulgoni VL, Ernstoff AS, Keoleian GA, Jolliet O (2016) A life cycle assessment framework combining nutritional and environmental health impacts of diet: a case study on milk. Int J Life Cycle Assess 21:734–746. https://doi.org/10.1007/s11367015-0961-0 Scherer L, Tomasik B, Rueda O, Pfister S (2018) Framework for integrating animal welfare into life cycle sustainability assessment. Int J Life Cycle Assess 23:1476–1490. https://doi. org/10.1007/s11367-017-1420-x van der Weele C, Feindt P, Jan van der Goot A, van Mierlo B, van Boekel M (2019) Meat alternatives: an integrative comparison. Trends Food Sci Technol 88:505–512. https://doi. org/10.1016/J.TIFS.2019.04.018 Barton H (2009) Land use planning and health and well-being. Land Use Policy 26:115– 123. https://doi.org/10.1016/j.landusepol.2009.09.008 Das ND (2017) Marginalization in globalizing Delhi: issues of land, livelihoods and health. 21–42. https://doi.org/10.1007/978-81-322-3583-5 Wei G (2020) Food safety issues related to wildlife have not been taken seriously from SARS to COVID-19. Environ Res 186. https://doi.org/10.1016/j.envres.2020.109605 El Zowalaty ME, Järhult JD (2020) From SARS to COVID-19: a previously unknown SARS-related coronavirus (SARS-CoV-2) of pandemic potential infecting humans—call for a one health approach. One Health 9. https://doi.org/10.1016/j.onehlt.2020.100124 Karesh WB, Dobson A, Lloyd-Smith JO, Lubroth J, Dixon MA, Bennett M, Aldrich S, Harrington T, Formenty P, Loh EH, MacHalaba CC, Thomas MJ, Heymann DL (2012) Ecology of zoonoses: natural and unnatural histories. Lancet 380:1936–1945. https://doi.org/ 10.1016/S0140-6736(12)61678-X Wood JLN, Leach M, Waldman L, MacGregor H, Fooks AR, Jones KE, Restif O, Dechmann D, Hayman DTS, Baker KS, Peel AJ, Kamins AO, Fahr J, Ntiamoa-Baidu Y, Suu-Ire R, Breiman RF, Epstein JH, Field HE, Cunningham AA (2012) A framework for the study of zoonotic disease emergence and its drivers: spillover of bat pathogens as a case study. Philos Trans R Soc B Biol Sci 367:2881–2892. https://doi.org/10.1098/rstb.2012. 0228

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One Health as an Integrated Approach: Perspectives …

71

138. Ntajal J, Falkenberg T, Kistemann T, Evers M (2020) Influences of land-use dynamics and surface water systems interactions on water-related infectious diseases—a systematic review. Water 12:631. https://doi.org/10.3390/w12030631 139. Hristovski M, Cvetkovik A, Cvetkovik I, Dukoska V (2010) Concept of one Health—a new professional imperative. Maced J Med Sci 3:229–232. https://doi.org/10.3889/MJMS.18575773.2010.0131 140. Aguirre AA, Basu N, Kahn LH, Morin XK, Echaubard P, Wilcox BA, Beasley VR (2019) Transdisciplinary and social-ecological health frameworks—novel approaches to emerging parasitic and vector-borne diseases. Parasite Epidemiol Control 3:e00084. https://doi.org/10. 1016/j.parepi.2019.e00084 141. Miller RS, Farnsworth ML, Malmberg JL (2013) Diseases at the livestock-wildlife interface: Status, challenges, and opportunities in the United States. Prev Vet Med 110:119–132. https://doi.org/10.1016/j.prevetmed.2012.11.021 142. Robinson J, Breed M (2019) Green prescriptions and their co-benefits: Integrative strategies for public and environmental health. Chall 10:1–14 . https://doi.org/10.3390/challe10010009 143. Zuñiga-Palacios J, Zuria I, Moreno CE, Almazán-Núñez RC, González-Ledesma M (2020) Can small vacant lots become important reservoirs for birds in urban areas? A case study for a Latin American city. Urban For Urban Green 47:126551. https://doi.org/10.1016/j.ufug. 2019.126551 144. Slater SJ, Christiana RW, Gustat J (2020) Recommendations for Keeping Parks and Green Space Accessible for Mental and Physical Health During COVID-19 and Other Pandemics. Prev Chronic Dis 17:200204. https://doi.org/10.5888/pcd17.200204 145. Westhoek H, Lesschen JP, Rood T, Wagner S, De Marco A, Murphy-Bokern D, Leip A, van Grinsven H, Sutton MA, Oenema O (2014) Food choices, health and environment: Effects of cutting Europe’s meat and dairy intake. Glob Environ Chang 26:196–205. https://doi.org/10. 1016/j.gloenvcha.2014.02.004 146. Röös E, Patel M, Spångberg J, Carlsson G, Rydhmer L (2016) Limiting livestock production to pasture and by-products in a search for sustainable diets. Food Policy 58:1–13. https://doi. org/10.1016/j.foodpol.2015.10.008 147. Kock RA, Karesh WB, Veas F, Velavan TP, Simons D, Mboera LEG, Dar O, Arruda LB, Zumla A (2020) 2019-nCoV in context: lessons learned? Lancet Planet Heal 4:e87–e88. https://doi.org/10.1016/S2542-5196(20)30035-8 148. Karikari A, Asante K, Biney C (2009) Water quality characteristics at the estuary of Korle Lagoon in Ghana. West African J Appl Ecol 10:1–12. https://doi.org/10.4314/wajae.v10i1. 45700 149. Kulinkina A V, Kosinski KC, Adjei MN, Osabutey D, Gyamfi BO, Biritwum N, Bosompem KM, Naumova EN (2019) Contextualizing Schistosoma haematobium transmission in Ghana : Assessment of diagnostic techniques and individual and community water- related risk factors. Act Trop 194:195–203. https://doi.org/10.1016/j.actatropica. 2019.03.016 150. Burkett-Cadena ND, Vittor AY (2018) Deforestation and vector-borne disease: Forest conversion favors important mosquito vectors of human pathogens. Elsevier GmbH 151. Pizzitutti F, Brewington L, Walsh SJ (2020) Human and Natural Environments, Island of Santa Cruz, Galapagos: A Model-Based Approach to Link Land Cover/Land Use Changes to Direct and Indirect Socio-Economic Drivers of Change. In: Walsh S, Riveros-Iregui D, Arce-Nazario J, Page P (eds) Land Cover and Land Use Change on Islands. Springer, Cham, pp 183–203

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S. Yasobant et al. Sandul Yasobant is a technical officer (research) at the Indian Institute of Public Health Gandhinagar (IIPHG), India, a Postdoc fellow at the Institute for Hygiene & Public Health, University Hospital Bonn, Germany, associated researcher at the the Center for Development Research (ZEF), University Bonn Germany and an adjunct faculty at the Datta Meghe Institute of Medical Sciences (DMIMS), India. Originally from Odisha (eastern India), Yasobant has completed his health education and public health training at Utkal University, Sri Ramachandra University in India, and the University of Bonn in Germany. He has completed the Doctoral Program ‘One Health & Urban Transformation’ at ZEF and obtained a public health doctoral degree from the medical faculty, University of Bonn, Germany in 2020. Yasobant’s research incorporates health policy and system research, One Health and disease prevention, environment and occupational health. Yasobant has conducted extensive research in India and south east Asia and is currently engaged in research activities in India and Ghana. Timo Falkenberg is a senior scientist at the GeoHealth Center, Institute of Hygiene and Public Health, University Clinic Bonn and associated researcher at the Center for Development Research (ZEF), University Bonn (Germany). Currently he is the scientific coordinator of the German West African Center for Global Health and Pandemic Prevention (G-WAC). He holds a BSc (hon) in Public Health from the University of East London and an MSc in Development Administration and Planning from the University College London (UCL). He has completed the Doctoral Program ‘Bonn International Graduate School for Development Studies’ at the Center for Development Research and obtained a medical geography doctoral degree from the natural science faculty in 2016. His research interest lies in infectious diseases, transmission pathways, WASH nexus, and wastewater irrigation. Timo has conducted extensive research in India and is currently engaged in research activities in India, Ghana, Brazil, and Germany.

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A Multipronged Approach to Combat COVID-19: Lessons from Previous Pandemics for the Future Barbara W. K. Son

Health is a state of complete mental, social and physical well-being, not merely the absence of disease or infirmity. The World Health Organization, 1948

Summary

Since China confirmed the first novel Coronavirus case of the 21st Century in December 2019, COVID-19, the highly contagious disease caused by SARS-CoV-2, has spread quickly to 213 countries and territories. Most similar to the 20th-century influenza pandemic in 1918–1919, the present COVID-19 pandemic has caused a major ongoing worldwide public health crisis. Countries have adopted various stringent control measures to slow the virus’s rapid spread, such as border closures, lockdowns, and travel restrictions. Unprecedented global efforts have generated over 150 different COVID-19 vaccine candidates [1]. However, designing vaccine efficacy trials is a very complicated process by given the shifting distribution of new COVID-19 cases. Vaccine development also faces many other challenges, such as SARS-CoV-2 mutation, potential side effects, and public acceptance of the vaccines ultimately produced. Since the 1918–1919 flu pandemic, scientists have learned much about pandemics, mainly from advances in epidemiological models for influenza and progress in influenza pandemics models. To prevail against COVID-19, the World Health Organization (WHO) is calling for early detection, virus testing, and isolation measures [2]. What is the best strategy to combat the highly contagious, often debilitating, or deadly COVID-19 virus? A review of past pandemics lessons reveals multiple B. W. K. Son (&) Akio Morita School of Business, Anaheim University, 1240 South State College Blvd, Anaheim, CA 92806, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_4

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determinants that influence pandemic outcomes: virus detection, diagnostic testing, contact tracing, social distancing, isolation, therapeutics, vaccines, transparent communication, healthcare authorities, and pandemic decision-making. This Chapter proposes a multipronged approach that first combines these determinants and then examines how we can take a multipronged approach against the COVID-19. Graphical Abstract/Art Performance

A multipronged approach to combat COVID-19.

The code of this chapter is 01100001 01,101,001 01,101,101 01,100,011 01,101,110 01,100,100 01,110,011 01,010,000 01,100,101. Keywords










COVID-19 Epidemic surveillance Healthcare policy Healthcare technology Human behavior Influenza pandemic models Pandemic decision-making SARS-CoV-2


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Introduction

The unprecedented threat of COVID-19 has been affecting all segments of populations worldwide. The highly contagious disease caused by the SARS-CoV-2 virus mutation has spread rapidly to 213 countries and territories, dealing devastating blows to healthcare, economic, and environmental sectors of societies and severely disrupting usual social behavior patterns. According to economic estimates, the pandemic can trigger deep recessions with a 5.2% contraction in global GDP in

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2020 [3]. The health and human toll COVID-19 threatens could be viewed as a chilling crisis of planetary health, ravaging entire populations. Growing human mobility, high-density urban living, and an increasingly interconnected world multiply exposures to pathogens, greatly facilitating virus transmission. COVID-19 is but the latest in successive waves of new, often deadly infectious diseases in recent years. As countries race to contain this latest pandemic, governmental and scientific decision-makers should consider the socio-ecological determinants of health [4]. Both to treat COVID-19’s victims and to curve the virus’s further spread, governments and healthcare providers have been scrambling for supplies and adopting various stringent virus control measures. To control an outbreak, however, governments also need to prepare healthcare infrastructure, assemble alert plans for early detection, and intensify response activities during the outbreak. Better preparedness and early detection have helped some countries mitigate a Coronavirus disaster. In 2015, despite an initial two months of explosive outbreaks of MERS (Middle East Respiratory Syndrome), experience with those outbreaks imparted lasting lessons for South Korea’s government, helping that nation “bend the curve” of daily COVID-19 cases roughly five years later, utilizing fast testing, extensive screening, contact tracing, isolation, and transparency [5]. In response to MERS, South Korea passed the Infectious Disease Control and Prevention Act, equipping the South Korean health minister with greater authority to implement infection-control measures [6]. These measures enabled rapid epidemic surveillance and contact tracing, which combined, curbed transmission of that virus [5]. Previously, the 2003 SARS Coronavirus pandemic ushered in a new era of international law on infectious disease control as the World Health Assembly granted the WHO greater authority to fight international infectious disease outbreaks [7]. Since the beginning of the 2020 calendar year, the WHO has led global efforts to tackle the current unprecedented pandemic [8]. Reflecting on major pandemics over the past century, what lessons can the governments learn? What is the best strategy to combat the highly contagious COVID-19 caused by SARS-CoV-2? Lessons learned from past influenza and Coronavirus pandemics can help to answer these questions. We will explore lessons from the pandemics of 1918–1919 H1N1, 1957– 1958 H2N2, 1968–1969 H3N2, 2003 SARS, and 2009–2010 H1N1. In particular, we will address multi-dimensional issues surrounding the current pandemic, which calls for a combination of several varying approaches. Accordingly, we will present a multipronged approach to combat the COVID-19 pandemic. We will conclude with the practical implications and future potential of this multipronged approach in battling future outbreaks.

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Lessons from Previous Pandemics for Future

We have seen progress in surveillance of circulating influenza viruses and viral genomic sequencing, and mathematical and computational models have continued to contribute to the implementation of pharmaceutical and non-pharmaceutical interventions to contain viruses [9]. Despite these advances, governments and healthcare professionals worldwide have been struggling with the raging COVID-19 pandemic of 2020. Although efforts to develop vaccines against higher virulence Coronaviruses have been ongoing for years, as of this writing, there still are no commercial vaccines for SARS or MERS, and COVID-19 vaccine development has been fast-tracked [9]. Until we have a safe and effective COVID-19 vaccine, rapid diagnostic testing for SARS-CoV-2 viral RNA combined with transmission prevention is essential in containing this pandemic. Serological testing is in increasing demand because it identifies potential convalescent plasma donors, monitors people’s immune status, and enhances contact tracing [10]. Diagnostics manufacturers worldwide are racing to develop ample supplies of test kits, as the global test kit market has grown exponentially and rapidly. In June 2020, the U.S. Food and Drug Administration (FDA) took further steps to facilitate the production of molecular diagnostic tests for obtaining patient samples and as a screen for patients with mild disease and asymptomatic individuals [11]. Meanwhile, South Korea has opened drive-through testing centers for fast, free COVID-19 testing; South Korea’s aggressive testing helped it curve COVID-19 infection and death rates downward, without a draconian lockdown approach [5]. Other countries now have followed South Korea’s lead, adopting this drive-through approach to testing. Although the cause of both COVID-19 and SARS belongs to Coronaviruses, COVID-19 appears to linger for a more extended period. Containing COVID-19 is more challenging than containing SARS, as COVID-19 appears to spread easily and spread through asymptomatic carriers [10]. Further, past pandemics’ history reveals that COVID-19 may become a seasonal version of the Coronavirus in our communities. There is precedent for this disturbing thought: H1N1 and H3N2 flu viruses in the previous pandemics never went away. A second ferocious wave of Coronavirus–either COVID-19 or another novel Coronavirus variant–may come later this year, as happened in previous pandemics. In the 1968 H3N2 pandemic, the second wave of H3N2 was far deadlier than the first wave [12]. In this next section, we consider previous pandemics lessons in greater detail and discuss how they may provide insights into the current pandemic’s future.

2.1 Lessons from 1918–1919 H1N1 “Spanish Flu” Pandemic The 1918–1919 influenza A (H1N1) pandemic, also known as the “Spanish Flu” pandemic, infected about 500 million people and killed at least 50 million people in three successive waves during 1918 and 1919 [12]. The “Spanish Flu” was the world’s deadliest pandemic in recorded history, infecting about one-third of the

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global population. In fall 1918, the second wave of the virus was especially fatal, probably attributable to a virulent mutation of the virus, spread by the massive movement of soldiers during World War I [13]. The average basic reproduction number (R0) for the 1918–1919 H1N1 virus was roughly 2, indicating that each infected person would infect approximately two other people on average in sufficient contact [14]. In 45 U.S. cities, the R0 was even higher, ranging from 2 to 3 [15]. A unique feature of this highly pathogenic, highly transmissible virus was the greatly elevated mortality rate in healthy males in the 20–40-year age group during the second wave [15]. Although the same strain of the virus probably caused both waves of this pandemic, the higher mortality rates among young males later in the pandemic were attributed to immunological memory dysregulating the immune response to the virus. Also, many males with tuberculosis died in 1918 from influenza, possibly linked to an interaction between the two diseases [13]. The enormous loss of prime working-age employees, who were also breadwinners in their families, led to socio-economic devastation in many communities. While there were no therapeutics to treat a viral infection and no vaccines, the process to implement non-pharmaceutical interventions such as facial masks, good hygiene, and social distancing was successful. The public health authority closed schools, churches, and theaters to prohibit public gatherings. However, in the absence of early federal response, states and cities in the U.S. applied these control measures unevenly, leading to marked differences in mortality rates during 1918 [13]. Table 1 highlights lessons learned from contrasting interventions during the 1918–1919 pandemic in two cities in the U.S., i.e., St. Louis and Philadelphia. The health commissioner of St. Louis, Missouri, gave the public an early warning and quickly implemented extreme and comprehensive social measures during the first eight weeks of the epidemic. In addition to closing schools, theaters, libraries, courthouses, and churches, the city staggered work shifts, curtailed transit, shuttered public buildings, and banned gathering in groups of more than 20 people–all to limit virus transmission [15]. The city’s social distancing policy had a positive effect, dramatically lowering the death rate in St. Louis, as contrasted with the death rates in other cities, including Philadelphia. In St. Louis, the estimated death rate per 100,000 population was 358, less than half the rate of 748 in Philadelphia [16]. While St. Louis was applying strict social distancing measures, Philadelphia took a different approach to the virus outbreak. Amid the pandemic, Philadelphia ignored virus infection among soldiers, and on September 28, 1918, the city hosted a parade to support the World War I effort, attended by 200,000 people [16]. The 1918–1919 pandemic revealed how early, full implementation of social distancing is critical in limiting the death toll from a highly transmissible virus. Community behavior is a key parameter in influenza pandemic models, as social distancing measures may control a flu pandemic far better than vaccines would [17]. Pandemic modeling shows that social distancing measures lower mortality by 10 to 30% in U.S. cities [15]. Flattening the curve is crucial, relieving pressure on overwhelmed healthcare systems while giving time to develop vaccines and anti-viral therapies [13]. As demonstrated by St. Louis’s example, multiple public

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Table 1 The 1918–1919 H1N1 “Spanish Flu” pandemic lessons 1918–1919 H1N1 pandemic

St. Louis

Philadelphia

–Ignoring social distancing measures: On –Early and quick Social distancing September 28, 1918, 200,000 people and limiting contact implementation of (about one-ninth of the city’s comprehensive closures measures –A near-total lockdown within population) packed the Downtown area two days of its first “Spanish for the Liberty Loans parade Flu” case –Immediate warning of the city’s health commissioner –Gradual reopening of the city –Re-imposition of restrictions during the second wave Outcomes –St. Louis “flattened the curve” better than other cities, with much lower peak death rates –Example: Estimated death rate per 100,000 population for “Spanish Flu” in St. Louis—358 versus Philadelphia—748 –Global excess all-cause mortality: 598 deaths per 100,000 people per year Lessons learned –Early, fast implementation of social distancing was highly effective against virus transmission –The timing of interventions had a significant impact on the magnitude of the second wave –Multiple interventions implemented at the same time were more effective than multiple interventions started at different times –Community behavior is a key parameter in influenza pandemic models –Pandemic decision-makers should take a stronger role in infectious disease prevention to avoid fateful results

health interventions implemented simultaneously are far more effective than multiple interventions at different times. St. Louis had flattened the curve through social distancing and isolation. However, when city officials lifted restrictions too early, St. Louis suffered from a deadly second influenza wave. Hence, pandemic decision-makers should learn from these events, taking faster, stronger precautions to avoid dire human and economic consequences [13].

2.2 Lessons from 1957–1958 H2N2 “Asian Flu” Pandemic Recognized as a severe pandemic in modern virology, the H2N2 pandemic of the H1N1 family emerged in China in February 1957. The extremely contagious H2N2 strain, called the Asian Flu, spread to more than 20 countries by June 1957 and killed between one million and two million people worldwide during 1957 and 1958 [18]. The estimated average excess respiratory mortality rate in this H2N2 pandemic was 1.9 per 10,000 people. In September 1957, the widespread pandemic

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began in the U.S. and caused about 60,000 excess deaths in the U.S. [19]. However, the 1957–1958 pandemic was mild relative compared to the 1918–1919 pandemic, with an estimated maximum case fatality rate of about 0.67%. Estimates of the R0 for this H2N2 pandemic are between 1.08 and 1.65. It is less than the R0 of the 1918–1919 pandemic, which was about 2 [20]. As with the 1918–1919 pandemic, a unique feature of the highly contagious H2N2 virus was the high mortality rates in the 5–14-year age group, probably attributable to children’s lack of pre-existing immunity [21]. Unlike the 1918–1919 pandemic, the 1957–1958 pandemic had a small impact on the economy, only lowering the U.S. GDP by about 1% [12]. The 1918–1919 pandemic prompted progress in worldwide cooperation in public health matters, leading to the development of viable flu vaccines in the 1940s. By 1957, a global network of laboratories contacted the Influenza Research Center in London, and comprehensive virus surveillance systems were in place. Accordingly, healthcare professionals in many countries were better prepared when the H2N2 pandemic arrived in 1957; global researchers were able to study responses to the developed influenza vaccine and to monitor ongoing behaviors of the H2N2 strain [12]. This virus caused a briefly harsh, febrile respiratory illness, but it rarely was fatal, and there was a low demand for hospitalization [12]. As the rapidly spreading virus infected an estimated 25% of the U.S. population, U.S. health officials considered reducing transmission through non-pharmaceutical interventions. With the mild nature of the symptoms, though, these officials decided against harsher mitigation strategies, generally avoiding quarantines, travel restrictions, school and border closures, mask-wearing, and mandatory reductions in the size of gatherings. Thus, while the U.S. focused on sustainable health services, the U.K. implemented social control measures unevenly [22]. When the U.S. vaccine manufacturers were developing the 1957–1958 flu vaccine, they did not start from scratch, thanks to viable flu vaccines from the 1940s. Although the U.S. Centers for Disease Control and Prevention (CDC) had requested manufacturers to produce a vaccine quickly, the distribution of vaccines was slow, with the first vaccines not distributed in the U.S. until August 1957. Moreover, vaccine distribution was extremely limited [22]. Additionally, as described in Table 2, although efforts to prevent pandemic-related morbidity and mortality focused on vaccination campaigns, the vaccine efficacy was a mere 53%–60% [12]. Despite health officials’ best efforts to have vaccine production ramped up, sufficient supplies of the H2N2 flu vaccine were unavailable in the U.S. when needed. Then, in November 1957, as new H2N2 outbreaks largely had ceased, interest in vaccination fell sharply, and people’s lives went back almost to normal. However, when the H2N2 virus returned for a third time in February 1958, the virus’s new strain caused much higher mortality rates, particularly in the elderly population. Table 2 highlights the H2N2 pandemic lessons. One important is the inadequate coverage of vaccination, which had a limited impact on the pandemic trend, but there would have been more pandemic-related deaths without the vaccine [12]. Hence, in the current pandemic, officials should keep pressing the development/ implementation of a flu vaccine. Drawing on lessons from vaccine production in 1957, a vaccine that induces long-term and broad-based immunity is needed [19].

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Table 2 The 1957–1958 H2N2 “Asian Flu” pandemic lessons 1957–1958 H2N2 pandemic Resources –Global Surveillance Systems –Global Network of Laboratories linked to the World Influenza Research Center in London –U.S. CDC’s Influenza Surveillance Unit Interventions –Early Detection of H2N2 Strain –Extremely Limited Distribution of Vaccine –Focus on Vaccination Campaigns –Lack of Non-pharmaceutical Interventions Outcomes –Estimated Average Excess Respiratory Mortality Rate: 1.9 per 10,000 people –Estimated Maximum Case Fatality Rate of about 0.67% –Vaccine Efficacy of 53%-60% Lessons –Limited Impact on Pandemic Trends, due to Inadequate Coverage of Learned Vaccination –Impact of Public Health Interventions and Environmental Conditions on Mortality and Transmission Rates

Moreover, a recent study of 1957–1958 pandemic cases in Maricopa County, Arizona, suggested that virus transmission and mortality rates potentially were affected by environmental conditions [18]. Further study is needed to optimize virus control measures, to tailor them to local needs and conditions.

2.3 Lessons from 1968–1969 H3N2 “A2/Hong Kong Flu” Pandemic First detected in Hong Kong in July 1968, the A/H3N2 virus, dubbed the “A2/Hong Kong Flu”, spread around the world, with its transmission facilitated by approximately 160 million international air travelers [23]. The A/H3N2 pandemic was clinically mild in most cases; mortality rates were low in most countries relative to those caused by previous pandemic viruses due to a pre-existing immunity to the neuraminidase antigen in all age groups. Nevertheless, in its two waves, the A/H3N2 virus was estimated to have led to between 500,000 and two million deaths, with excess mortality of 16.9% [24]. The initial, smoldering wave of the H3N2 virus in the U.S. was more severe than in other countries; 70% of excess deaths were associated with the pandemic in the 1968–1969 season. In the 1969– 1970 season, the second wave was less severe in the U.S. than in Europe and Asia [23]. These divergent smoldering patterns likely were due to differences in prior neuraminidase immunity through antibodies to the hemagglutinin H3 gene, followed by subsequent drift in the neuraminidase antigen between waves [25]. The R0 for the 1968–1969 A/H3N2 virus was estimated at 1.8, higher than R0 at 1.08– 1.65 in the 1957–1958 pandemic [20, 23]. Unlike the 1957–1958 H2N2 pandemic, the virus infection burden shifted to younger ages, including younger adults.

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Similar to the 1957–1958 pandemic, though, most excess deaths from the A/H3N2 virus occurred among seniors (age 65 years and older) with pre-existing conditions [23, 26]. Despite the geographical differences in the major mortality timing, the 1968–1969 pandemic remained relatively mild overall, causing only small social and economic impacts in the U.S., despite school and workplace absenteeism [26]. The 1968–1969 A/H3N2 pandemic was the mildest influenza pandemic of the twentieth century, with relatively low mortality and disease severity rates, as described in Table 3. Accordingly, the U.S. avoided costly strict containment measures, such as quarantines, to combat this pandemic. Americans still kept going to work while practicing social distancing [12]. U.S. health officials focused on vaccination, hospitalization, and antibiotics. The National Influenza Center at the University of Hong Kong sent samples of the A/H3N2 virus to the World Influenza Center in London and the International Influenza Center for the Americas in Atlanta, prompting vaccine production laboratories to study the virus. Meanwhile, the National Communicable Disease Center mobilized, obtaining cooperation from health officers, epidemiologists, and laboratory directors to increase virus surveillance in the U.S. Drawing on lessons from the 1957–1958 pandemic, the U.S Advisory Committee on Immunization Practices recommended the use of a vaccine before high virus activity spread widely through the population [23]. Although the 1968–1969 smoldering pattern gave vaccine makers ample time to distribute a pandemic vaccine, the vaccine came too late for most of the population, causing the vaccine to have a limited effect in reducing the pandemic’s spread [12]. After the pandemic’s Table 3 The 1968–1969 H3N2 “A2/Hong Kong Flu” pandemic lessons 1968–1969 H3N2 pandemic Resources –Global Surveillance Systems –Global Network of Laboratories linked to the World Influenza Research Center in London –International Influenza Center for the Americas –U.S. CDC’s Influenza Surveillance Unit –National Communicable Disease Center –U.S. Advisory Committee on Immunization Practices –Reassortment, Antiviral Medications, Influenza Vaccine Options Interventions –Early Detection of H3N2 Strain –Limited Distribution of Vaccine –Focus on Vaccination, Hospitalization, and Antibiotics –Lack of Non-pharmaceutical Interventions Outcomes –Estimated Global Mortality: 500,000-two million in two waves –Excess Mortality: 16.9% –Geographical Differences in the Timing of the Major Mortality –The Efficacy of Monovalent Pandemic Vaccines: 65%-93% –Limited Effect of the Vaccine on Reducing Pandemic Spread, due to Lessons learned inadequate Coverage of Vaccination –Standard Surveillance Reporting Form for National Influenza Centers –Better Influenza Surveillance and Influenza Forecasting System

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peak in January 1969, vaccine demand waned, and vaccine makers started phasing out production. However, there was evidence that the vaccine was safe and effective, even for school-age children. The efficacy of monovalent pandemic vaccines ranged from 65 to 93% [23]. Several actions took place to combat the A/H3N2 pandemic, such as the establishment of virus reassortment and the development of vaccines along with the continued use of anti-viral agents. However, the cumulative impact of A/H3N2 virus infections on global public health was profound as extremely transmissible A/H3N2 virus had been adapting to evade host immune systems, and viral mutations ultimately had been making existing vaccines less effective against the virus. Concomitantly, the A/H3N2, a novel virus subtype, became the leading cause of seasonal influenza morbidity and mortality worldwide [27]. The epidemic model study indicated that global epidemic severity could have decreased significantly through air travel restrictions, combined with other control measures [28]. As Table 3 highlights, the WHO introduced a standard virus surveillance reporting form for the National Influenza Centers. Thus, the continued experience of worldwide transmissions of pandemic flu viruses underscores the WHO’s need to enhance global collaboration, both for improving influenza surveillance and developing a better influenza forecasting system [25].

2.4 Lessons from the 2003 SARS Pandemic Severe Acute Respiratory Syndrome (SARS) first emerged in southern China in 2002 and infected 8,437 people in 32 countries, with 813 deaths [29]. The WHO labeled this Coronavirus as SARS instead to avoid stigmatizing any particular country or region and to give more detailed, descriptive case definitions. The R0 for the SARS Coronavirus was estimated at 3, which was higher than the R0 in the previous pandemics, as the SARS variant of the Coronavirus was more infectious than previous strains had been [7]. Transmission of the highly infectious SARS virus spread rapidly through infected healthcare workers; in fact, the transmission of this particular virus occurred mainly within healthcare settings [30]. Consistent with the effects of viruses in previous pandemics, though, SARS Coronavirus hit seniors (age 65 and older) particularly hard. The estimated fatality rate of seniors was more than 50%, substantially higher than the general estimate of general case fatality–14% to 15% overall [29]. Chinese officials delayed a few months in reporting to the WHO about the SARS Coronavirus, and the WHO publicly criticized this delay. Meanwhile, an epidemiologic team in Hong Kong headed by Margaret Chan gave the WHO information about the route of virus transmission and local cluster factors. This information prompted the WHO both to issue emergency guidelines to airlines for screening departing passengers and also to recommend to international travelers that they avoid affected areas [7]. Business and leisure travel to affected areas was halted, and subsequently, the airline and tourism industries suffered terrible economic losses. Although the number of SARS cases was far smaller than the number

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Table 4 The 2003 SARS pandemic lessons 2003 SARS pandemic Resources –International Health Regulations Reform –Proactive Global Surveillance –Global Network of Laboratories linked to the World Influenza Research Center in London –International Influenza Center for the Americas –U.S. CDC’s Influenza Surveillance Unit –National Communicable Disease Center –U.S. Advisory Committee on Immunization Practices Interventions –Rapid Diagnosis –Epidemiological Investigation –Strict Control Measures –Vigorous Containment Activities –International Travel Recommendations Outcomes –Estimated Global Mortality: 813 deaths –Global Containment of SARS in Eight Months Lessons –Public Compliance with Recommended Infection Control Measures learned –Effective Containment without a Vaccine –WHO’s Guidance about Managing Risks

of cases in previous notable pandemics (e.g., in 1918–1919, 1957–1958, and 1968– 1969), rigorous disease control measures dealt a severe blow to the economy in each affected geographical area. Because the world had become reliant on international travel for inter-connectivity, with 1.6 billion air passengers in 2002, the estimated total global economic cost from these restrictions was $33 billion U.S. dollars [31]. As Table 4 highlights, SARS contagion’s rapidity caused intense fears of imminent SARS infection, fears that rippled throughout the world. The profound impact of SARS on social and consumer behavior led to community-wide support for extreme control measures, including prompt isolation and quarantine [31]. The Chinese government imposed a lockdown on most of China to minimize human-to-human transmission [32]. Toronto also effectively contained SARS thanks to the high level of public compliance with intensive containment measures [30]. SARS effectively was controlled in just eight months through epidemiological investigation, rapid diagnosis, case isolation, and international travel restrictions. This surprising result came about without the use of novel therapeutic drugs or vaccines [29]. As the SARS outbreak ended before a vaccine had been developed, vaccine makers also incurred substantial financial losses [9]. Nevertheless, the WHO vision of reform of International Health Regulations (IHRs) led to proactive international surveillance through greater international cooperation in managing SARS coronavirus infection [33].

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2.5 Lessons from 2009–2010 pH1N1/09 “Swine Flu” Pandemic The pH1N1/09 virus, a new strain of H1N1 commonly called Swine Flu, emerged in Mexico and the U.S. almost simultaneously in 2009, causing somewhere between 151,700 to 575,400 deaths worldwide, with an estimated global fatality rate of 0.001 to 0.007% [34]. Because advances in technology and trends in business and social globalization had increased the movement of people across international borders dramatically, within a matter of days, the pH1N1/09 virus had spread across 30 countries, and in six weeks, it had spread to 122 countries worldwide [35]. Although the first two waves of the pH1N1/09 virus generally were mild, in fall, 2009 the third wave was quite severe. The R0 for the 2009–2010 pH1N1/09 pandemic hovered between 1.4 and 1.6 [35]. This pandemic hit children, who were more susceptible to pH1N1/09 infection than adults, causing a shift in mortality towards younger populations due to their lack of immunity to the specific virus. In contrast, many older people who earlier had been exposed to the H1N1 virus had immunity to the pH1N1/09 virus [34]. On April 29, 2009, the WHO called for the activation of pandemic preparedness plans [34]. As Table 5 describes, many countries employed vigorous virus containment activities because of the high risk of infection to children. For example, the government of Mexico, in its response, employed drastic non-pharmaceutical interventions, including closing schools, banning public gatherings, enacting social distancing rules, and imposing quarantines [15]. The aggressive containment campaign in the U.K. included school closures, isolation, and anti-viral treatment [12]. While Canada launched the largest mass immunization program, the U.S. employed a multi-faceted response to the pandemic, including school closures, Table 5 The 2009–2010 pH1N1 “Swine Flu” pandemic lessons 2009 Swine flu pandemic Resources –Mathematical and Computational Models –Proactive Global Surveillance –U.S. CDC’s Influenza Surveillance Unit –Emergency Operations Center (EOC) –National Incident Management System Interventions –Pandemic Preparedness Plans –Epidemiological Investigation –Strict Control Measures –Vigorous Containment Activities –Vaccination Campaign Outcomes –Estimated Global Mortality: 151,700 to 575,400 deaths –Global Containment of Swine Flu in 16 Months –Need for Real-Time Data on the Progress of a Pandemic Lessons learned –Contribution of Modelling to the Interventions –Maintain Consistent Diagnostic Protocols –WHO’s Guidance about Managing Risks

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social distancing, epidemiological investigations, an Emergency Operations Center, a National Incident Management System, vaccinations, and anti-viral drug use [34]. These pharmaceutical and non-pharmaceutical interventions ultimately contained the pandemic. The WHO declared an end to the pandemic in August 2010. However, the 2009–2010 pH1N1/09 pandemic was costly, as it brought a substantial economic loss in the countries most affected by the virus, with drops in their GDP between 0.5% and 1.5% [12]. The supply of a monovalent pH1N1/09 vaccine began increasing only after the pandemic had peaked in the U.S. Nevertheless, the CDC continued its vaccination outreach efforts [9]. As the pH1N1/09 virus continues to be a modern seasonal flu virus, a pH1N1/09 vaccine ultimately has become a part of seasonal flu vaccines [34]. As shown in Table 5, mathematical and computational models were applied to the pandemic decision-making, guiding decision-makers in analyzing, selecting, and implementing various intervention modalities [32]. For example, consistent diagnostic protocols produced surveillance data essential for making real-time decisions [12]. Real-time data on the progress of the pandemic also assisted the coordination of a worldwide response [32].

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A Multipronged Approach to Combat COVID-19

In the previous section, we closely examined the stark lessons from the pandemics of 1918–1919, 1957–1958, 1968–1969, 2003, and 2009–2010. This wide-ranging examination revealed multiple determinants that influence pandemic outcomes: virus detection, diagnostic testing, contact tracing, social distancing, isolation, therapeutics, vaccines, transparent communication, actions by healthcare authorities, and pandemic decision-making. These determinants can be clustered into five main categories: fast, extensive testing, contract tracing technology, restrictive public health measures, healthcare technology and treatments, and healthcare policy and communication. The lessons learned from prior pandemics confirm that the best response to combat a global pandemic will utilize a multipronged approach, addressing each of these five categories of determinants, as depicted in Graphical Abstract. In this section, we analyze how implementing such a multipronged approach can help the world prevail against the current COVID-19 pandemic specifically.

3.1 Fast, Extensive Testing As depicted in the first component in a multipronged strategy to combat COVID-19 is fast, extensive testing during the early stages of infection. Drawing on lessons learned from the response to the 2003 SARS pandemic, early case detection was a key determinant of effective containment of the disease. Rapid, accurate testing for SARS-CoV-2 viral RNA prompted medical intervention and effective isolation,

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which helped prevent the further spread of the virus [10]. Especially considering the virus’s range of clinical manifestations and estimated case fatality rates of 3.5% to 4.2%, early diagnosis campaigns were very valuable in “flattening the curve” [36]. The diagnostic test regimen included both serological tests, which monitor an individual’s immune status, and molecular diagnostic tests as a screen for asymptomatic individuals [11]. Thus, proper diagnostic testing requires serology testing and affordable test kits, not only for molecular- and immunoassays but also for reverse transcription-polymerase chain reaction [10]. Using this dual diagnostic testing program enhanced surveillance capabilities during the 2003 SARS pandemic [10]. Although COVID-19 test kits were not available in most parts of the world in the early days of the current pandemic, South Korean firms adopted a rapid development program and churned them out to meet demand in that country. Under this accelerated plan, South Korea also pioneered drive-through COVID-19 testing centers for fast, free testing. South Korea’s aggressive testing and mass screening have helped curve COVID-19 rates without a draconian lockdown approach [5]. Recognizing South Korea’s apparent success, other countries have been adopting this extensive testing approach to combat the spread of COVID-19.

3.2 Contact Tracing Technology The second component in a multipronged strategy is contact tracing technology. In addition to early testing and detection, as discussed above, contact tracing was a key determinant in containing the SARS virus successfully in 2003. In 2020, with modern communications technology, several nations had rushed to adopt smartphone applications for COVID-19 contact tracing, including Singapore’s Trace Together, Germany’s Corona-Warn tracing, Australia’s COVIDSafe, and Israel’s The Shield. These apps are appealing because the COVID-19 virus is stealthy, presenting asymptomatic infections, with symptoms most often manifesting within 14 days [37]. In other countries, such as South Korea, India, and Iceland, government COVID-19 contact tracing programs use GPS tracking, which raises privacy concerns [38]. In fact, under South Korea’s Infectious Disease Control and Prevention Act, South Korean health authorities have access to high-tech surveillance, providing them with precise geolocation data on COVID-19 patients. Thus, in embracing this tracing technology, South Koreans have prioritized public health over privacy during the current emergency [6]. Elsewhere, to maintain privacy protections, the COVID-19 exposure notification from Apple and Google allows each user to choose to opt-in; the Bluetooth-based contact tracing system does not collect location data from the user’s device automatically [39]. In addition to the South Korean surveillance program, the Korea Centers for Disease Control and Prevention employ a new COVID-19 Epidemiological Survey Prompt Support System, expediting epidemiological investigations [40]. Additional progress in epidemiological models based on large data streams from mobile phones is vital to planning surveillance and containment interventions [4].

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3.3 Restrictive Public Health Measures Restrictive public health measures represent the third component in a multipronged strategy. Lessons from the earlier pandemics in 1918–1919 and 2003 have revealed how much early and fast implementation of restrictive public health measures–such as comprehensive closures and social distancing–is critical to limit the death toll from the highly transmissible virus [13]. Community behavior is a key parameter in influenza pandemic models. Health officials effectively controlled the 2003 SARS pandemic in just eight months, primarily by non-pharmaceutical interventions, without the use of novel drugs or vaccines [29]. Clearly, social-behavioral factors influence R0 estimates [41]. The R0 for COVID-19 is 3.28, higher than the R0 of the SARS Coronavirus. This high infectivity of the virulent and potentially deadly COVID-19 virus has triggered many governments to adopt stringent control measures, such as different forms of social distancing and lockdowns, business shutdowns, and travel restrictions. Such measures collectively have had a devastating impact on the global economy. Research shows that a somewhat greater distance–at least two meters, or just over 6–1/2 feet–from an infected individual is more protective than being closer–say, a distance of one meter, or just over 3–1/4 feet [42]. As the countries discuss the relaxation of regulations in response to COVID-19, a lesson from history is instructive since it was the second wave of the 1918–1919 H1N1 pandemic that brought about the majority of infections and deaths. Therefore, there is substantial impetus to continue rigorous restrictive public health measures as much as possible until a safe, effective vaccine can be produced and widely distributed. Although non-pharmaceutical interventions can flatten pandemic peaks, they cannot prevent infectivity from viral infection. Accordingly, if restrictive public health regulations are discontinued or significantly relaxed prematurely, the infection can come back to the normal transmission patterns [12].

3.4 Healthcare Technology and Treatments Healthcare technology and treatments represent the fourth component in a multipronged strategy. When reviewing pandemics lessons in 1957–1958, 1968–1969, and 2009–2010, the COVID-19 response needs a vaccine that induces long-term and broad-based immunity, with an adequate worldwide distribution [9]. More research is needed to develop platform technologies to mass-produce effective, safe COVID-19 vaccines rapidly, thus enabling the population to achieve herd immunity slowing the virus’s spread [43]. Despite critical knowledge gaps concerning the pathogenicity and viral characteristics of SARS-CoV-2, the virus underlying COVID-19, effective vaccine development requires viral genomic sequencing-based diagnostic testing [44]. As the COVID-19 pandemic continues to devastate socio-economic activities worldwide, unprecedented global cooperation in developing vaccines for COVID-19 has led to the creation of 21 vaccine candidates currently in Phase 1 through 3 clinical evaluation and an additional 139 vaccine candidates in preclinical evaluation [8].

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Additionally, the U.S. has launched its “Operation Warp Speed” program to attempt to fast-track the development, production, and distribution of effective COVID-19 vaccines for Americans by January 2021. Meanwhile, global research on anti-viral agents, immunotherapies, and novel pharmacological therapies with interferon-alpha and lopinavir have given rise to clinical trials of potential treatments of COVID-19 infections [45]. Regardless of therapeutics, an effective COVID-19 vaccine providing optimal immune response and a longer duration of immunity is the more certain methodology to control the outbreak [9].

3.5 Healthcare Policy and Communication Healthcare policy and communication represent the last component in a multipronged strategy. In the 1918–1919 H1N1 pandemic, poor communication and reporting between jurisdictions hampered effective responses. In contrast, in the 2003 SARS pandemic, governments were better prepared, communicated with their citizens about the risks of SARS and public protections from the virus, and marshaled community-wide support for intensive containment measures [30, 31]. To create a high level of public compliance with public health interventions, governments should build trust through open, transparent communications about the health crisis’s risks and harms and communications based on competent, committed pandemic strategies [46]. Accordingly, healthcare policies in the fight against pandemics should combine adequate preparedness, vigorous early warning systems, effective risk communications, and organized response activities grounded in data and scientific facts. Further, policies should employ comprehensive multi-level interventions flexibly in response to the changing epidemiology of the particular pandemic influenza.

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Conclusion

Because the current COVID-19 pandemic behaves as a “once-in-a-century” pandemic, we explored lessons from recent historical pandemics, from 1918–1919 H1N1 “Spanish Flu” pandemic, to the 2009–2010 pH1N1/09 “Swine Flu” pandemic, and identified multiple determinants that influence pandemic outcomes, summarized in Tables 1, 2, 3, 4, 5. We proposed a comprehensive, multipronged approach, as illustrated in Graphical Abstract, to combat COVID-19 based on these determinants. Successful Coronavirus-fighting countries have utilized a comprehensive multi-layered approach, implementing various earlier, stricter interventions simultaneously. Because later surges of outbreak cases may be on the horizon, effective control measures hinge on diligent citizens who remain vigilant, continuing their compliance with control measures recognized as effective, such as social distancing, to protect themselves and others. Individual behavior is one of the crucial determinants of epidemiological models [47].

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Further progress in mathematical and computational models based on real-time data can guide pandemic decision-makers on more precise techniques of implementing the range of pharmaceutical and non-pharmaceutical interventions in response to the changing epidemiology of the particular influenza strain. The resulting shockwaves from lockdowns and travel restrictions during the current COVID-19 pandemic have caused severe downturns in global economies and deleterious impacts on societies worldwide. The worst socio-economic impacts in influenza pandemic history underscore the importance of actions to mitigate current and future flu pandemics. After an initial destructive wave of outbreaks in China, the COVID-19 virus has swept through Europe and North America. Another wave is hitting low- and middle-income countries around the world particularly hard. This devastation has spurred a global coalition to assist 165 countries in utilizing the global access facility for the COVID-19 vaccine. This is an important action provided by the WHO, Gavi, and the Coalition for Epidemic Preparedness Innovations [48]. As human exposure to pathogens and virus transmission increases with growing global interconnectivity, mobility, pollution, and rapid urbanization, the 2020 COVID-19 pandemic likely will not be the worst or last worldwide pandemic. Accordingly, both to cope with the current pandemic and to prepare more thoroughly for future pandemics, COVID-19 public health policies should be deliberate and emphasize greater global cooperation, improved influenza surveillance, viral genomic sequencing, an improved influenza forecasting system, as well as support for lower-capacity countries [9]. Additionally, as the global healthcare policy organization, the WHO should promote greater investments by developed countries in pandemic preparedness, should monitor nations’ compliance with IHRs, should lead effective global surveillance networks, and should foster coordinated and organized responses with integrated real-time epidemiological data systems [4]. Core Messages

• Multiple determinants influence pandemic outcomes when exploring lessons from recent historical pandemics. • The lessons learned from prior pandemics confirm that the best response to combat a global pandemic will utilize a multipronged approach. • Both to cope with the current pandemic and to prepare more thoroughly for futurepandemics, we proposed COVID-19 public health policies.

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References 1. Gates B (2020) Responding to COVID-19—a once-in-a-century pandemic? N Engl J Med 382:1677–1679 2. WHO (2020a) WHO coronavirus disease (COVID-19) dashboard. https://covid19.who.int/. Accessed 22 June 2020 3. The World Bank (2020) The global economic outlook during the COVID-19 pandemic. https://www.worldbank.org/en/news/feature/2020/06/08/the-global-economic-outlookduring-the-covid-19-pandemic-a-changed-world. Accessed 13 July 2020 4. Buckee C (2020) Improving epidemic surveillance and response: big data is dead long live big data. Lancet 2(5):E218–E220 5. Moradi H, Vaezi A (2020) Lessons learned from Korea: COVID-19 pandemic. Infect Control Hosp Epidemiol 1–2. Advance online publication 6. Son B, South-Winter C (2018) Human behavior impacts on health care. J Int & Interdiscip Bus Res 5(8):138–146 7. WHO (2003a) Consensus document on the epidemiology of severe acute respiratory syndrome. Dep Commun Dis Surveill Response. https://www.who.int/csr/sars/ WHOconsensus.pdf?ua=1. Accessed 14 July 2020 8. WHO (2020b) Draft landscape of COVID-19 candidate vaccines. https://www.who.int/ publications/m/item/draft-landscape-of-covid-19-candidate-vaccines. Accessed 8 July 2020 9. Lurie N, Saville M, Hatchett R, Halton J (2020) Developing COVID-19 vaccines at pandemic speed. N Engl J Med 382:1969–1973 10. Carter L et al (2020) Assay techniques and test development for COVID-19 diagnosis. ACS Cent Sci 6(5):591–605 11. Shuren J (2020) Coronavirus (COVID-19) update. U.S. FDA. https://www.fda.gov/newsevents/press-announcements/coronavirus-covid-19-update-facilitating-diagnostic-testavailability-asymptomatic-testing-and. Accessed 12 July 2020 12. Saunders-Hastings P, Krewski D (2016) Reviewing the history of pandemic influenza: understanding patterns of emergence and transmission. Pathogens 5(4):66 13. Hatchett R, Mecher C, Lipsitch M (2007) Public health interventions and epidemic intensity during the 1918 influenza pandemic. Proc Natl Acad Sci 104(18):7582–7587 14. Morse SS (2007) Pandemic influenza: studying the lessons of history. Proc Natl Acad Sci 104 (18):7313–7314 15. Coburn B, Wagner B, Blower S (2009) Modeling influenza epidemics and pandemics: insights into the future of Swine Flu (H1N1). BMC Med 7:30 16. Markel H, et al (2007) Nonpharmaceutical Interventions Implemented by U.S. Cities during the 1918–1919 influenza pandemic. J Am Med Assoc 298(6):644–654 17. Morens D, Fauci A (2007) The 1918 influenza pandemic: insights for the 21st century. J Infect Dis 195(7):1018–1028 18. Cobos A et al (2016) Mortality and transmissibility patterns of the 1957 influenza pandemic in Maricopa County, Arizona. BMC Infect Dis 16:405 19. Henderson D, Courtney B, Inglesby T, Toner E, Nuzzo J (2009) Biosecurity and bioterrorism: biodefense strategy. Pract Sci 7(3):265–273 20. Biggerstaff M, Cauchemez S, Reed C et al (2014) Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature. BMC Infect Dis 14:480 21. Viboud C et al (2016) Global mortality impact of the 1957–1959 influenza pandemic. J Infect Dis 213(5):738–745 22. Jackson C (2009) History lessons: the Asian flu pandemic. Br J Gen Pract: J R College Gen Pract 59(565):622–623 23. Jester B, Uyeki T, Jernigan D (2020) Fifty years of influenza A(H3N2) following the pandemic of 1968. Am J Public Health 110(5):669–676

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A Multipronged Approach to Combat COVID-19: Lessons …

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24. Cockburn WC, Delon PJ, Ferreira W (1969) Origin and progress of the 1968–69 Hong Kong influenza epidemic. Bull World Health Organ 41(3):345–348 25. Ortiz J et al (2012) Pandemic influenza in Africa, lessons learned from 1968: a systematic review of the literature. Influ Other Respir Viruses 6(1):11–24 26. Tognotti E (2013) Lessons from the history of quarantine, from plague to influenza a. Emerg Infect Dis 19(2):254–259 27. Finelli L, Swerdlow D (2013) The emergence of influenza a (H3N2) virus: what we learned from the first wave. Clin Infect Dis 57(Suppl. 1):S1–S3 28. Epstein J et al (2007) Controlling pandemic flu: the value of international air travel restrictions. PLoS One 2(5):e401 29. Chowell G et al (2004) Model parameters and outbreak control for SARS. Emerg Infect Dis 10(7):1258–1263 30. Low D (2004) SARS: lessons from Toronto. In: Knobler S, Mahmoud A, Lemon S, et al (eds) SARS: preparing for the next disease outbreak: workshop summary. National Academies, Washington 31. Lee J, McKibbin W (2004) Estimating the global economic costs of SARS. In: Knobler S, Mahmoud A, Lemon S, et al (eds) SARS: preparing for the next disease outbreak: workshop summary. National Academies, Washington 32. Lee B, Haidari L, Lee M (2013) Modelling during an emergency: the 2009 H1N1 influenza pandemic. Clin Microbiol Infect 19(11):1014–1022 33. WHO (2003b) Key step forward on international health rules. http://www.who.int/ mediacentre/releases/2003/prwha7/en/. Accessed 15 July 2020 34. CDC (2010) The 2009 H1N1 pandemic: summary highlights. https://www.cdc.gov/h1n1flu/ cdcresponse.htm. Accessed 5 July 2020 35. Akin L, Gözel MG (2020) Understanding dynamics of pandemics. Turk J Med Sci 50 (SI-1):515–519 36. Wilson N, Kvalsvig A, Barnard L, Baker M (2020) Case-fatality risk estimates for COVID-19 calculated by using a lag time for fatality. Emerg Infect Dis 26(6):1339–1441 37. Sun J et al (2020) COVID-19: epidemiology, evolution, and cross-disciplinary perspectives. Trends Mol Med 26(5):P483-495 38. Servick K (2020) COVID-19 contact tracing apps are coming to a phone near you: how will we know whether they work? Science. https://www.sciencemag.org/news/2020/05/countriesaround-world-are-rolling-out-contact-tracing-apps-contain-coronavirus-how. Accessed 10 July 2020 39. Pressman A (2020) Apple and Google’s coronavirus contact tracing system gains more participants across the globe. Fortune. https://fortune.com/2020/07/08/apple-googlecoronavirus-tracing-app-technology-participants-countries/. Accessed 11 July 2020 40. Park S, Choi G, Ko H (2020) Information technology-based tracing strategy in response to COVID-19 in South Korea—privacy controversies. J Am Med Assoc 323(21):2129–2130 41. Kolifarhoo G et al (2020) Epidemiological and clinical aspects of COVID-19; a narrative review. Arch Acad Emerg Med 8(1):e41 42. Chu D et al (2020) Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis. Lancet 395(10242):1973–1987 43. Charlton H, Lua L (2017) Platform technologies for modern vaccine manufacturing. Vaccine 35(35):4480–4485 44. Furukawa N, Brooks J, Sobel J (2020) Evidence supporting transmission of severe acute respiratory syndrome coronavirus 2 while presymptomatic or asymptomatic. Emerg Infect Dis 26(7). Early Release 45. Khachfe H et al (2020) An Epidemiological study on COVID-19: a rapidly spreading disease. Cureus 12(3):e7313

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46. Tumpey A, Daigle D, Nowak G (2018) Communicating during an outbreak or public health investigation. https://www.cdc.gov/eis/field-epi-manual/chapters/Communicating-Investigation. html. Accessed 14 July 2020 47. Bauch C, D’Onofrio A, Manfredi P (2012) Behavioral epidemiology of infectious diseases: an overview. Model Interplay Hum Behav Spread Infect Dis 1–19 48. WHO (2020c) More than 150 Countries Engaged in COVID-19 Vaccine Global Access Facility. https://www.who.int/news-room/detail/15-07-2020-more-than-150-countries-engagedin-covid-19-vaccine-global-access-facility. Accessed 20 July 20 2020

Barbara Son is a professor of business and sustainable management at Anaheim University. Her current research focuses on global health, the healthcare industry, the digital economy, and online learning. She has published in peer-refereed journals, Springer and Sage, with articles on healthcare, technology and services, and online education. She was vice president and co-founder of the International Association Management Group. Barbara served as a site reviewer of business-degree and online programs for the California Bureau for Private Post-Secondary and Vocational Education.

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Response to Disease Outbreaks in Africa: A Call to Build Resilient Health Systems Juliet Nabyonga-Orem, James Avoka Asamani, and Hillary Kipruto

COVID-19 has taught us that strong health systems are a matter of national security and survival. This virus has not only affected our health, but also tested our way of living, societal norms and economies at large. In Africa, we quickly felt the impact of the pandemic due to our weak health systems coupled with the highest disease burden in the world. Abiy Ahmed, Prime Minister, Federal Democratic Republic of Ethiopia

Summary

Although resilience has been central to building health systems, conceptual clarity and consensus on its measurement are murky. Refocusing the discourse from merely disease outbreak to acute and chronic stressors is imperative. It should follow moving beyond strengthening health system building blocks to encompass software issues, including values, norms, relationships, management,

J. Nabyonga-Orem (&)
J. A. Asamani
H. Kipruto Inter-Country Support Team for Eastern & Southern Africa, Health Systems and Services Cluster, World Health Organization, P.O Box CY 348, Causeway, Harare, Zimbabwe e-mail: [email protected] J. A. Asamani e-mail: [email protected] H. Kipruto e-mail: [email protected] J. Nabyonga-Orem
J. A. Asamani Centre for Health Professions Education, Faculty of Health Sciences, North-West University, Potchefstroom Campus, Building PC-G16, Office 101,11 Hoffman St. Potchefstroom 2520, Vanderbijlpark, South Africa © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_5

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and organization. This chapter traces vulnerabilities and the history of epidemicrelated emergencies in Africa and synthesizes conceptual issues in health system resilience and measurement attempts. Africa’s health systems and capacities are abysmal on the Global Health Security Index, scoring averagely 15% (range: 0.3%–33%), while, based on inherent resilience, 22 countries scored below 50%, with scores ranging from 22 to 88%. The differences between these approaches at least partly lie in conceptual and measurement issues. However, it is conclusively clear that the road to resilience is still long in Africa, epitomized by 3.3 million people suffering 1,910 episodes of disease outbreaks with a 13% fatality rate between 1990 and 2019. The chapter provides an impetus to rethink building resilience as a continuous process of creative adaptation and transformation informed by the ever-evolving nature of context-specific issues, not merely focusing on bouncing back after acute shocks. Thus, the notion of inherent resilience to both acute and chronic shocks is critical, taking into account the need for continuous adaptation to embrace developments in global health, digital innovations, and epidemiological and demographic transitions. This chapter calls for a need for consensus on a fit-for-purpose approach and indicators on building a resilient health system.

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Graphical Abstract/Art Performance

Health emergencies in Africa: 1990–2019

The code of this chapter is 01101100 01,100,101 01,001,000 01,100,001 01,101,000 01,110,100. Keywords
















Africa Chronic stress Disasters Diseases outbreaks Hazards Resilience Resilient health systems




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Introduction

Africa has 49% of the global burden of communicable diseases. It is much more than its share of the world’s population, estimated to be about 16% [1]. Africa’s disease burden is mainly attributed to infectious diseases presented by occasional epidemics [2–4]. Additionally, weak and fragile health systems characterized by inadequate health infrastructure, essential medicines and technology; health workforce shortages; and limited financing capacity critically come to explain Africa’s suffering. In the meantime, increasing global interconnectedness is responsible for either exposing Africa to diseases that start elsewhere or spreading diseases that start from Africa to other parts of the world [5]. In the former case, a key example is the coronavirus disease (COVID-19) that began from Wuhan but could spread across Africa. We conducted a scoping search in major medical databases (PubMed, Scopus, and Google Scholar) to obtain relevant scholarly works and reports. The various combinations of the following key terms were used: “Resilient Health services” OR “Resilient Health systems” AND “low-income countries” OR “Africa.” Additionally, relevant and publicly available datasets and analyses from the world health organization (WHO) were analyzed to support the discussion.

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Historical Trends of Disease Outbreaks in Africa

Before severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged, disease outbreaks in Africa mainly were cholera, meningitis, yellow fever, malaria, dysentery, Ebola, and Marburg hemorrhagic fevers, as well as avian influenza [2]. Between 1970 and 2019, there have been at least 1,910 reported incidents of disease outbreaks in Africa: about 1779 traced by the WHO Africa regional office up to 2016 [4]; 96 reported to WHO by the Members States in 2018 [5]; and 18 and 17 reported for 2017 and 2019 respectively by the International Disaster Database [6]. Between 1990 and 2019, estimates based on publicly available data show that nearly 3.3 million people in Africa have suffered one disease or more due to an outbreak (Fig. 1 and Table 1); among them, at least 132,230 have lost their lives (overall fatality rate of 13%). However, given historical weaknesses in capacity and delays in detecting and reporting cases, we believe that the publicly available data tend to underestimate past epidemics’ accurate scale. This is especially important in epidemics that occurred earlier than 2014, when the Ebola crisis triggered an improved consciousness for epidemic monitoring and reporting. During this period (1990–2019), available data show that disease outbreaks have, on average, affected 121,534 people in Africa annually. Until the coronavirus disease (COVID-19) pandemic, which affected more than one million Africans killing at least 22,150 between March and August 2020 [7], the continent faced its worst disease outbreak in 2002 when more than 580,000 people across 18 countries suffered various forms of disease outbreaks, largely cholera, influenzas, and meningococcal diseases.

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Fig. 1 Trend of the number of persons infected by disease outbreaks between 1990 and 2019 in African countries (Prepared with data from The International Disaster Database, 2020 https:// public.emdat.be/data) Table 1 Decade-by decade summary of epidemics and the number of people infected between 1990 and 2019 in African countries (Prepared with data from the International Disaster Database, 2020 https://public.emdat.be/data) Disease

Number of people affected within periods 10-year 1990–1999 2000–2009 2010–2019

Cholera Cerebrospinal meningitis Meningococcal disease Typhus Yellow fever Influenza Lassa fever Poliomyelitis Monkeypox Typhoid fever Rift Valley fever Plague Shigellosis Acute neurological syndrome Marburg virus Ebola Measles Acute Watery Diarrhoea

635,665 31,219 24,956 23,889 2419 2000 953 620 611 568 344 335 242 211 72 15

740,222

519,924

172,902 1365 52,853 246 459 43,772 1609 1725 1719 1583 46 1013 63,661 34,849

1150 29 9 138

32,733 27,475 967 (continued)

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Table 1 (continued) Disease

Chikungunya virus disease Dengue Acute Hepatitis E Meningitis Hepatitis E Leishmaniasis Acute diarrhoeal syndrome Visceral leishmaniasis (Kala-Azar) Hepatitis A Avian Influenza H5N1 Wild Poliovirus Type 1

Number of people affected within periods 10-year 1990–1999 2000–2009 2010–2019 28,898 20,147 8173 8123 7857 1000 661 221 120 23 524

9867 108 14,584

Despite Africa suffering its worst episode of Ebola outbreak in 2015, a reduction in cholera and influenza incidence (compared to the period 1990–2019) resulted in fewer people being affected by all epidemics forms in 2015. Figure 1 summarizes the number of people affected by disease epidemics in Africa between 1990 and 2019. Since the 1970s, the Democratic Republic of Congo (DRC) has had more frequent epidemics (N = 140) than any other country, followed by Nigeria (*125), Uganda, Kenya, and Ghana, with approximately 100 epidemic events each [4]. Besides cholera, Ebola is Africa’s most frequent and arguably devasting epidemic [2], only surpassed by the COVID-19 pandemic. From 1976 when Ebola was first discovered in present-day DRC, until mid-2018, there have been at least 16 separate outbreaks of Ebola within the central part of Africa (mainly DRC, Gabon, and the Republic of Congo), with at least 1,588 confirmed cases and an estimated 1,209 fatalities (case fatality rate of 76%). The actual number of infections is, however, thought to be higher [3]. Between 2013 and 2015, the most devasting Ebola outbreak occurred in West Africa, which mostly affected Guinea, Sierra Leone, and Liberia. By the end of the outbreak, at least 28,663 people were infected with 11,324 deaths (*40% case fatality rate) with a few cases recorded outside of Africa (4 cases with one death in the United States, one case each with no deaths in the United Kingdom, Spain, and Italy) [8]. The concomitant economic cost was equally devasting, estimated at US$2.2 billion losses in the gross domestic products of Liberia, Sierra Leone, and Guinea, while the cost of the international response was said to be more than US$3.9 billion [9].

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2.1 How Long Does It Take to Detect, Respond to, and Control Epidemics in Africa The inability to detect disease outbreaks early or the health systems’ incapacity to rapidly adjust and respond to the outbreaks while maintaining essential critical services has often been discussed as the source of mortality in Africa rather than just the virulence of the organism responsible for the epidemic [2, 8, 10]. However, no quality studies have objectively estimated the level of delay in detection, reporting, and response to previous epidemics. Based on the lessons learned from the 2014– 2015 Ebola outbreak in West Africa, the WHO Regional Office for Africa (WHO/AFRO) increased support to “… rapidly detect public health events (PHE) of international concern and rapidly implement effective public health actions … through the implementation of the integrated disease surveillance and response (IDSR) … and the establishment of an epidemic intelligence unit at the WHO regional office for Africa” [5]. The strengthened vigilance led to a higher index of suspicion and relatively improved reporting. For instance, the WHO regional office received a record of 96 episodes attributable to infectious disease outbreaks from 36 countries involving about 107,167 people with 1221 fatalities [5]. Routine surveillance systems locally available could detect nearly 70% of disease outbreaks, while media (and social media) signals were capable of detecting about 24% of disease outbreaks from 17 countries [5]. It implies media are becoming an essential part of disease surveillance [11]. Despite a seemingly improved detection, delays in the rate of detection and reporting are still a challenge. For instance, in 2018, it took more than two weeks on average, and in some cases, up to three months for epidemics to either be detected or reported to WHO in line with the International Health Regulations, 2005 (median = 16 days; range: 0–184 days). The West Africa Ebola crisis between 2014 and 2015 affected multiple countries. It took more than two years to be controlled. Several factors contribute to the protracted nature of that epidemic, including the fragility of the health systems in Sierra Leone, Guinea, and Liberia, which were emerging from civil war [8]; late detection of the disease [12]; and societal mistrust of political and public health professionals leading to resistance to public health interventions [13]. In contrast, Nigeria, with a relatively more robust health system, contained the Ebola outbreak within four months (20 cases and eight deaths) [14]. Despite a relatively more rapid response from government and international partners, the 9th and 10th Ebola outbreak in the Democratic Republic of Congo has lingered between 2018 and 2020 with intermittent control and rebound, which has been primarily attributed to insecurity and political instability making access to affected areas difficult [3]. There are several lessons [15] for the COVID-19 pandemic and future disease outbreaks, which hinge on the concept of building resilient health systems capable of picking early signals, detecting, and rapidly responding to health emergencies while maintaining the provision of essential health services [10, 16]. It highlights the need for conceptual clarity regarding the term “resilient health systems” and considerations in building a resilient health system.

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Conceptual Clarity on Resilient Health System

What a resilient health system embodies is murky and different scholars provide different perspectives. WHO [17] compiled definitions from 18 scholars and institutions, which are helpful to identify common threads that may reflect the totality of what a resilient health system entails. The central constructs seem to vary in different definitions, including: • Awareness of a system that enables it to anticipate shocks, identify its vulnerabilities, and use evidence to improve continually; • The capacity of the system to absorb disturbances helps prepare the system for and respond to a crisis, withstand and bounce back, survive, and grow; • Adaptability refers to the role of actors and institutions as major players in realizing resilience; individuals, communities, families, institutions, and systems to mitigate, engage and survive; the capacity of the system to make adjustments that can adequately anchor response efforts and sustain the provision of essential services; the capacity of the community or society to ‘self-preserve.’ Preservation of the system and restoration of its functionality; • The capacity to manage continuous change reflects the magnitude to which a system can absorb disturbances and reorganize; transform when conditions require it; deal with change; and reduce chronic vulnerability; • The capacity to sustain functionality is the capacity of the system to preserve identity; maintain functional health institutions and sustain achievements; guard against compromising long term prospects; and sustain capacity and recover from shocks; and • The capacity to transform refers to the capacity of the system to generate a new system and alter the nature of a system where needed; bounce back; avoid fundamental loss of identity; transform; and maintain relevance. Only one of the several definitions made consideration for the vulnerability (reflected in continuous stress) as opposed to stability [18]; and again, only one referred to performance with limited resources [19]. What a resilient health system entails remains a subject of limited consensus or none (or maybe even confusing in some definitions). In some definitions, the emphasis has been on resilience against disease outbreaks and disasters, representing only one kind of shock. Indeed 11 out of the 18 definitions compiled by WHO [17] focussed on responding to shocks. However, the range of shocks that expose the presence or lack thereof of resilience in Africa’s health systems has been characterized as chronic stresses (long-term issues that limit health systems from performing optimally, e.g., inadequate financing) or acute shocks (on–off issues that disrupt the performance of systems for the provision of essential services) [20]. Scholars advocate for broadening the concept of “building resilient health systems” and underscore some of the aspects least emphasized in the several definitions. Among the considerations is the time horizon, as illustrated by Barasa et al.

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[21]. They perceive building resilience as a continuous process, asserting that “health system actors need to abandon the dream of ever achieving secure and responsive health systems and instead embrace vulnerability,” implying continuity in building resilient systems. It is worth noting that the building of resilient health systems is not linear given the different kinds of shocks (chronic and acute) and residual vulnerability after responding to a shock [22]. Disasters of any kind are not short-term or transient shocks to the systems [10]. Its effects wane with time but pose longer-term effects on the health system’s ability to prepare, absorb, and adapt to subsequent shocks. Literature from social-economic systems [23] attempts to debunk the resilient concept and identifies four attributes that govern the system’s dynamics, namely: i. Latitude, the maximum strain/change a system can endure (the threshold) without losing the capacity to recover; ii. Resistance, how resistant the system is to shocks; iii. Unstableness, the level of vulnerability of the system; and iv. Context, how intrinsic and extrinsic factors interact to impact a system’s resilience (e.g., political regimes, conflicts, climate change, and global markets). The institutions and actors involved play a significant role in managing the system and adapting to the different challenges/shocks. Their intended or unintended actions influence the system’s transition between the four areas and subsequently influence system resilience. Health system resilience in responding to shocks tends to imply a knee-jack reaction, a very limiting focus. Barasa et al. [21] argue that such leads to “encouraging inaction” instead of sustained efforts to build resilient systems as part of routine health system planning and management. If one says a health system must be resilient, then we ask resilient to what?: the chronic challenges (e.g., underfunding or inadequate human resources) or acute shocks (e.g., disease outbreaks, floods). It definitely must be to both (chronic and acute), and as such, the process of building resilience is a day-to-day activity. Kruk et al. [19] propose a reframing of the concept to embrace everyday resilience and not just respond to shocks and embody creative adaptation and transformation instead of focusing solely on bouncing back.

4

The Resilience of Health Systems in African Countries: What Do We Know?

Although resilience has become a central part of Africa’s health systems, there is neither a formal consensus on its measurement nor conceptual clarity. Hence, there is a dearth of evidence on the status of health systems’ resilience in Africa, with very few attempts to present a holistic picture. The recently developed Global Health Security Index (GHSI) [24], which includes a sub-index on health systems capacity to sustain the provision of services while ensuring health worker protection

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during health emergencies, ranks African countries between 0.3 (Somalia) and 33.0 (South Africa), with an African region average of 14 (highest being 100). Although the GHSI assesses 85 sub-indicators across six core areas, it appears to be more relevant for high-income countries; and that “… the validity of some indicators, the scoring system and its weighting and its [the added] value” remains questionable [25]. Building on this critique, Razavi et al. [25] caution against using the GHSI as a single estimate to compare countries. They, however, advocate for its use to track individual country’s change over time. The approach employed in this assessment is aligned to response to emergencies and assesses the parameters, including the capacity of health facilities (human resource and infrastructure); capacity to mobilize countermeasures and personnel deployment; access to health services; communication with health workers; infection prevention and control practices; and capacity to test and approve new medical countermeasures. The WHO African Region [26] employs a different approach to assess the “inherent health system resilience,” defined as the inbuilt capacity to anticipate, absorb, and transform functionality due to a shock event. The AFRO’s inherent health system resilience is measured using five core parameters: i, awareness of the system to capacities and risks; ii, diversity of services and capacities; iii, self-regulatory capacity for fast decision making; iv, capacity for local mobilization of resources; and v, capacity to learn and transform. The performance ranges from 22.1 for Mauritius to 87.6 for South Africa, where the maximum score is 100. Out of the 22 countries assessed, ten had a score of less than 50. Although the current measurement approaches are limited in scope, we can conclude that the health system’s resilience in African countries is generally low. This poor performance is corroborated by Ayanore et al. [27], who note that health systems in the Africa Region possess a low capacity for detecting and reporting significant health events and are not adaptable to changing health conditions, which present the risk of the disrupted provision of essential services during surge periods. Further exploration of the health system building blocks (hardware of the system) highlights additional weaknesses. Early detection and timely reporting of an outbreak are critical characteristics a resilient health system must possess, as this paves the way for a swift response. In this regard, health information systems (including surveillance systems) must be robust, integrated, and able to identify shocks in real-time and trigger action. Additionally, the health information system should also monitor progress in capacity strengthening through, for example, the duration required to control an outbreak [28–30]. We, however, note that information systems in Africa are far from achieving this. For example, very few countries in Africa report annual death (18/54), and only four disaggregate data to an internationally approved standard [31]. The WHO Africa Region report 2018 [32] highlighted the impact of the predominance of paper-based information systems, limited analytical capacity, inadequate standardizations, and poor coding on the quality of data collected. Health systems in Africa have suffered persistent underfunding with current health expenditure per capita in African countries as low as US$19 in the Democratic Republic of Congo. Twenty-one countries in Africa spend less than US$50

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per capita on health [33] compared to the required estimate of US$86 per capita in low-income countries to provide a meaningful package of health services [34]. The COVID-19 pandemic witnessed the mobilization of supplementary budgets and the creation of special COVID-19 funds through hurriedly passed laws and presidential pronouncements. Nineteen out of 26 countries in Africa had to establish such special funds [35]. In building resilience, how can budget legislation be flexible to enable activation of emergency spending to facilitate fast response given that the African continent battles on average of 25 outbreaks at any one time [36]? Only four out of the 26 countries drew from reserve funds [35]. Africa has only 30% of its required health workforce [37]. Health worker infections exacerbated inadequate human resources for health during the COVID-19 response, especially those on the front line. By July 23, 2020, 40 countries in the African region reported slightly over 10,000 COVID-19 infections among health workers [38]. Insufficient personal protective equipment (PPE) to protect health workers at the community and health facility level and suboptimal infection prevention and control practices have been highlighted [39, 40]. The role of effective governance has been brought to the fore, which is defined by Brinkerhoff et al. [41] as the “rules and processes that guide operations and affairs of organizations.” Factors to ensure resilience include: • empowering local actors coupled with flexibility for swift action through decentralized control as opposed to central control [42]; • careful engagement of actors and reducing fragmentation [43]; • being forward-looking and evidence-driven; implementation of result-oriented solutions; achieving value for money; • being accountable to communities and adherence to professional standards [44]; • nonlinear planning with consideration for feedback loops and continued learning; • deliberative democracy coupled with transparency (not mere voting) as opposed to representative democracy [45] and; • effective coordination between the different functions [46]. Olu et al. reported weak and highly centralized coordination capacity and decision-making among the Ebola response challenges in Sierra Leone [47]. Moreover, weak governance and leadership played a role in maternal and child survival in selected African countries [48, 49]. The capacity to provide critical care is limited. A study of a sample of hospitals in 54 African countries undertaken in 2017 showed that the availability of critical care beds per 100,000 population ranged from as low as zero (0) in Sierra Leone and Somalia to 6.0 in South Africa [50]. Further, the availability of critical care nurses is far inadequate, with over 50% of intensive care units unable to attain a nurse: patient ratio of at least 1:2[50] and in 78% of African countries, non-physician anesthetists comprise more than 50% of the anesthesia workforce [51]. However, some countries perform better, for example, the Central African Republic, Eritrea, Liberia, and Somalia (almost 100%) [52]. During the COVID-19

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pandemic, only 2,000 ventilators were available in public hospitals across 41 African countries, while ten countries had no ventilator [53]. Barasa et al. [21] draw our attention to neglected tangible and intangible software components that are important for building resilient health systems. While the former refers to management knowledge and skills, organizational systems, and procedures, the latter refers to values, norms, relationships, and power. These have not been studied much, especially in Africa. Power imbalances, especially in donor dependant countries, have negatively impacted the policy dialogues, limiting ministries of health’s capacity to focus on their priorities [54]. Mwisongo and Nabyonga-Orem noted that donor conditionalities still drove resource allocation decisions in Africa regardless of countries’ priorities [55].

5

What Will It Take to Improve Health System Resilience?

Building resilient health systems is everybody’s responsibility. Blanchet K argues that it starts with conceptual clarity and consensus on priority actions as prerequisites [56]. However, some scholars highlight context’s importance, stating that health system resilience is context-specific [57]. Therefore, actors in any given system must agree on the definition to ensure coherent action according to a common objective. Building resilient systems is a multi-sectoral issue and thus requires a multi-sectoral collaboration. Some scholars argue that the boundary between the whole of society resilience and health system resilience is arbitrary [21]. Every sector must play its role. For example, solutions to the human resource for health crises lie outside the health ministry; education, finance, and public service play significant roles. Further, addressing social determinants of health is a critical component of the epidemic response (e.g., control of cholera outbreaks in slums is a challenge) and control of communicable diseases (e.g., control of tuberculosis in poor housing conditions is a challenge). Approaches to building resilience must be tailored to health systems working in unique geographic and socio-political environments, such as rapid and poorly planned urbanization in many African cities. Cardoso et al. [58] highlight the complexity and vulnerability of urban areas, which are continuously evolving. In such settings, building resilience must be integrated with sustainable urban development. Community engagement plays a significant part in ensuring resilience before, during, and after shocks. Community members must be active participants in decision-making, planning, governance, and service delivery. Emphasis should be on self-preservation with behavioral change as the first line to outbreak response and disease control. Some argue that we need to embrace the concept of community resilience and support and leverage bottom-up community action in response to outbreaks.

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We need to refocus the dialogue on health system resilience from a predominantly diseases outbreak focus to chronic stress and emphasize the continued efforts and investments in building resilience. Perhaps inherent resilience [26, 59] needs to be pursued given the evolving nature of the context and issues faced; acute and chronic shocks, new developments in global health, digital innovations, and epidemiological and demographic transitions; which require constant adaptations to sustain health service provision efficiently. This will ensure what Kruk et al. [59] refer to as dynamism and urgency to the long-standing work of health system strengthening; guard against inaction and change the focus from predominantly a hardware focus (strengthening health system building blocks) and address software (values, norms, relationships, management, organization aspects) concerns as well. Detailing explicit interventions to be implemented in building resilient health systems has remained elusive as much of the literature has focused on the attributes. Nuzzo et al. [10] highlight the dearth of evidence to translate the attributes into contextualized interventions policy actors can implement and specific capacities to develop. The assessment and monitoring of resilience face similar challenges as previous attempts have differed in measures employed and comprehensiveness, while some of the proposed frameworks, for example, by Kruk et al., are yet to be validated [59].

6

Conclusion

There is a need for conceptual clarity and explicit priority interventions to ensure aligned action by all stakeholders in building resilient health systems. Further, we underscore the need for a broader approach to embracing the concept of day-to-day resilience instead of focusing on emergency response, a multi-sectoral approach, and focusing on both the hardware and software. Building resilient health systems is a continuous process, and, in this regard, there is a need to develop reliable indicators to assess progress. Core Messages

• What the term health systems resilience embodies, as well as interventions to be implemented, remains murky. • Approaches to assessing resilience in African’ health systems are limited. • Given the range of shocks and actions required, building resilient health systems is everybody’s responsibility. • We should build inherent resilience as a day-to-day endeavor instead of responding to shocks.

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References 1. Narayan K (2016) Envisioning a healthy future-Africa’s shifting burden of disease. Inst Secur Stud Pap 18:1–36 2. Kebede S, Duales S, Yokouide A, Alemu W (2010) Trends of major disease outbreaks in the African region, 2003–2007. East Afr J Public Health 7(1):20–29. https://doi.org/10.4314/ eajph.v7i1.64672 3. Munster VJ, Bausch DG, de Wit E, Fischer R, Kobinger G, Munoz-Fontela C, Olson SH, Seifert SN, Sprecher A, Ntoumi F, Massaquoi M, Mombouli JV (2018) Outbreaks in a rapidly changing central Africa—lessons from Ebola. N Engl J Med 379(13):1198–1201. https://doi. org/10.1056/NEJMp1807691 4. WHO Region Office for Africa (2016) Mapping the risk and distribution of epidemics in the WHO African Region. A technical report. Brazzaville Congo 5. Mboussou F, Ndumbi P, Ngom R, Kassamali Z, Ogundiran O, Beek JV, Williams G, Okot C, Hamblion EL, Impouma B (2019) Infectious disease outbreaks in the African region: overview of events reported to the World Health Organization in 2018—ERRATUM. Epidemiol Infect 147:e307. https://doi.org/10.1017/S0950268819002061 6. EM-DAT Public (2020) No title. The international disaster database. https://public.emdat.be/ data. Accessed 4 Sept 2020 7. WHO (2020) COVID-19 in the WHO Africa region—situation report 27. Brazzaville, Congo 8. Omilabu SA, Salu OB, Oke BO, James AB (2016) The West African ebola virus disease epidemic 2014–2015: a commissioned review. Niger Postgrad Med J 23(2):49–56. https://doi. org/10.4103/1117-1936.186299 9. World Bank (2016) 2014–2015 West Africa Ebola crisis: impact update. World Bank Fisc Rep 4. https://doi.org/10.1145/2330163.2330218. Washington DC 10. Nuzzo JB, Meyer D, Snyder M, Ravi SJ, Lapascu A, Souleles J, Andrada CI, Bishai D (2019) What makes health systems resilient against infectious disease outbreaks and natural hazards? Results from a scoping review. BMC Public Health 19(1):1310. https://doi.org/10.1186/ s12889-019-7707-z 11. Odlum M, Yoon S (2015) What can we learn about the Ebola outbreak from tweets? Am J Infect Control 43(6):563–571. https://doi.org/10.1016/j.ajic.2015.02.023 12. Marston BJ, Dokubo EK, van Steelandt A, Martel L, Williams D, Hersey S, Jambai A, Keita S, Nyenswah TG, Redd JT (2017) Ebola response impact on public health programs, West Africa, 2014–2017. Emerg Infect Dis 23(13). https://doi.org/10.3201/eid2313.170727 13. Cohn S, Kutalek R (2016) Historical parallels, ebola virus disease and cholera: understanding community distrust and social violence with epidemics. PLoS Curr 8 14. Kamorudeen RT, Adedokun KA, Olarinmoye AO (2020) Ebola outbreak in West Africa, 2014–2016: epidemic timeline, differential diagnoses, determining factors, and lessons for future response. J Infect Public Health 13(7):956–962. https://doi.org/10.1016/j.jiph.2020.03. 014 15. Gates B (2015) The next epidemic–lessons from Ebola. N Engl J Med 372(15):1381–1384. https://doi.org/10.1056/NEJMp1502918 16. Meyer D, Bishai D, Ravi SJ, Rashid H, Mahmood SS, Toner E, Nuzzo JB (2020) A checklist to improve health system resilience to infectious disease outbreaks and natural hazards. BMJ Glob Health 5(8):e002429 17. World Health Organisation (2017) Taking forward an integrated approach to health systems & health security: exploring resilience. Geneva 18. Nemeth C, Wears R, Woods D, et al (200) Minding the gaps: creating resilience in health care. In: Henriksen K, Battles JB, Keyes MA, et al (eds) Advances in patient safety: new directions and alternative approaches (vol 3: Performance and Tools). Rockville (M.D.): Agency for Healthcare Research and Quality (U.S.). https://www.ncbi.nlm.nih.gov/books/ NBK43670/

5

Response to Disease Outbreaks in Africa …

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19. Kruk ME, Myers M, Varpilah ST, Dahn BT (2015) What is a resilient health system? Lessons from Ebola. Lancet 385(9980):1910–1912. https://doi.org/10.1016/S0140-6736(15)60755-3 20. Barasa E, Mbau R, Gilson L (2018) What is resilience and how can it be nurtured? A systematic review of empirical literature on organizational resilience. Int J Health Policy Manag 7(6):491–503. https://doi.org/10.15171/ijhpm.2018.06 21. Barasa EW, Cloete K, Gilson L (2017) From bouncing back, to nurturing emergence: reframing the concept of resilience in health systems strengthening. Health Policy Plan 32 (suppl_3):iii91–iii94. https://doi.org/10.1093/heapol/czx118 22. Fridell M, Edwin S, von Schreeb J, Saulnier DD (2020) Health system resilience: what are we talking about? A scoping review mapping characteristics and keywords. Int J Health Policy Manag 9(1):6–16. https://doi.org/10.15171/ijhpm.2019.71 23. Walker B, Holling CS, Carpenter SR, Kinzig A (2004) Resilience, adaptability and transformability in social–ecological systems. Ecol Soc 1(9):2 24. The Economict Intelligence Unit and Johns Hopkins Bloomberg School of Public Health (2019) Global health security index [Internet]. The economict intelligence unit and Johns Hopkins Bloomberg school of public health, p 324. https://www.ghsindex.org/wp-content/ uploads/2019/10/2019-Global-Health-Security-Index.pdf 25. Razavi A, Erondu N, Okereke E (2020) The global health security index: what value does it add? BMJ Glob Health 5(4):e002477 26. WHO African Region (2020) Report on the performance of health systems in the WHO African region; 70th regional committee for Africa. Brazzaville Congo 27. Ayanore MA, Amuna N, Aviisah M, Awolu A, Kipo-Sunyehzi DD, Mogre V, Ofori-Asenso R, Gmanyami JM, Kugbey N, Gyapong M (2019) Towards resilient health systems in Sub-Saharan Africa: a systematic review of the english language literature on health workforce, surveillance, and health governance issues for health systems strengthening. Ann Glob Health 85(1). https://doi.org/10.29024/aogh.25145334/aogh.2514 28. Harvard Global Health Institute (2018) Monitoring of disease outbreak preparedness: a shared framework [Internet]. https://globalhealth.harvard.edu/monitoring-disease-preparedness. Accessed 26 Aug 2020 29. Moon S, Sridhar D, Pate MA, Jha AK, Clinton C, Delaunay S, Edwin V, Fallah M, Fidler DP, Garrett L, Goosby E, Gostin LO, Heymann DL, Lee K, Leung GM, Morrison JS, Saavedra J, Tanner M, Leigh JA, Hawkins B, Woskie LR, Piot P (2015) Will Ebola change the game? Ten essential reforms before the next pandemic. The report of the Harvard-LSHTM independent panel on the global response to Ebola. Lancet 386(10009):2204–2221. https:// doi.org/10.1016/S0140-6736(15)00946-0 30. WHO (2015) Report of the Ebola interim assessment panel. Geneva. https://www.who.int/csr/ resources/publications/ebola/report-by-panel.pdf?ua=1ebola-panel-report/en/. Accessed 4 Sept 2020 31. Economic Commission for Africa (2017) Report on the status of civil registration and vital statistics in Africa by the Economic Commission for Africa. Addis Ababa—Ethiopia 32. WHO Regional Office for Africa (2018) The state of health in the WHO African region: an analysis of the status of health, health services and health systems in the context of the sustainable development goals. Licence: CC BY-NC-SA 3.0 IGO. Brazzaville 33. World Bank (2017) Current health expenditure percapita US$, Washington DC. https://data. worldbank.org/indicator/SH.XPD.CHEX.PC.CD?name_desc=false. Accessed Sept 3 2020 34. Mcintyre D, Meheus F, Røttingen JA (2017) What level of domestic government health expenditure should we aspire to for universal health coverage? Health Econ, Policy Law 12 (2):125–137 35. Nabyonga-Orem J, Kwesiga B, Abewe C, Barroy H, Tumusiime P (2020) Building sustainable health financing options for emergency response: Lessons from the COVID-19 response in Africa. https://blogs.bmj.com/bmjgh/2020/05/26/building-sustainable-healthfinancing-options-for-emergency-response-lessons-from-the-covid-19-response-in-africa/. Accessed 3 Sept

108

J. Nabyonga-Orem et al.

36. WHO (2016) Acute public health events assessed by WHO Regional Offices for Africa, the Americas and Europe under the international health regulations (2005), Geneva. https://www. afro.who.int/sites/default/files/2017-11/2016_Joint_Report_AFRO_AMRO_EURO.pdf. Accessed 20 Sept 2020 37. Asamani JA, Akogun OB, Nyoni J, Ahmat A, Nabyonga-Orem J, Tumusiime P (2019) Towards a regional strategy for resolving the human resources for health challenges in Africa. BMJ Glob Health 4(Suppl 9):e001533. https://doi.org/10.1136/bmjgh-2019-001533 38 WHO Region Office for Africa (2020) https://www.afro.who.int/news/over-10-000-healthworkers-africa-infected-covid-19. Accessed 2 Sept 2020 39. Ballard M, Westgate C (2020) COVID19: it ain’t over until there’s PPE all over. Think Global Health. https://www.thinkglobalhealth.org/article/covid-19-it-aint-over-until-theresppe-all-over?mc_cid=cb86b8c16d&mc_eid=c8fb41925eDate. Accessed 25 June 2020 40. Nepomnyashchiy L, Dahn B, Saykpah R, Raghavan M (2020) COVID-19: Africa needs unprecedented attention to strengthen community health systems. Lancet 396(10245):50–52 41. Brinkerhoff DW, Bossert TJ (2014) Health governance: principal-agent linkages and health system strengthening. Health Policy Plan 29(6):685–693. https://doi.org/10.1093/heapol/ czs132 42. Olsson P, Folke C, Berkes F (2004) Adaptive comanagement for building resilience in social-ecological systems. Environ Manage 34(1):75–90. https://doi.org/10.1007/s00267-0030101-7 43. McKenzie A, Abdulwahab A, Sokpo EW, Mecaskey J (2015) Building a resilient health system: lessons from Northern Nigeria. In: IDS Working Paper, vol 2015, no 454. Nigeria 44. Kesete-Birhan A (2016) Designing a resilient national health system in Ethiopia: the role of leadership. Health Syst Reform 2(3):182–186.https://doi.org/10.1080/23288604.2016. 1217966 45. Booher DE, Innes JE (2010) Governance for resilience : CALFED as a complex adaptive network for resource management. Ecol Soc 15(3):35 46. McManus S, Seville E, Vargo J, Brunsdon D (2008) Facilitated process for improving organizational resilience. Nat Hazard Rev 9(2):81–90 47. Olu OO, Lamunu M, Chimbaru A, Adegboyega A, Conteh I, Nsenga N, Sempiira N, Kamara KB, Dafae FM (2016) Incident management systems are essential for effective coordination of large disease outbreaks: perspectives from the coordination of the Ebola outbreak response in Sierra Leone. Front Public Health 4:254. https://doi.org/10.3389/fpubh. 2016.00254 48. Haley CA, Brault MA, Mwinga K, Desta T, Ngure K, Kennedy SB, Maimbolwa M, Moyo P, Vermund SH, Kipp AM, Team WACSS (2019) Promoting progress in child survival across four African countries: the role of strong health governance and leadership in maternal, neonatal and child health. Health Policy Plan 34(1):24–36. https://doi.org/10.1093/heapol/ czy105 49. Olafsdottir AE, Reidpath DD, Pokhrel S, Allotey P (2011) Health systems performance in sub-Saharan Africa: governance, outcome and equity. BMC Public Health 11:237. https://doi. org/10.1186/1471-2458-11-237 50. Ayebale ET, Kassebaum NJ, Roche AM, Biccard BM (2020) Africa’s critical care capacity before COVID-19. South Afr J Anaesth Analg 26(3):162–164 51. WFSA (2020) World anaesthesiology workforce. https://www.wfsahq.org/workforce-map. Accessed Sept 9 2020 52. Craig J, Kalanxhi E, Hauck S (2020) National estimates of critical care capacity in 54 African countries. medRxiv 53. New York Times (2020) 10 African countries have no ventilators at all. That’s only part of the problem. https://www.nytimes.com/2020/04/18/world/africa/africa-coronavirus-ventilators. html. Accessed 9 Sept 2020

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54. Ade N, Rene A, Khalifa M, Babila KO, Monono ME, Tarcisse E, Nabyonga-Orem J (2016) Coordination of the health policy dialogue process in Guinea: pre- and post-Ebola. BMC Health Serv Res 16(Suppl 4):220. https://doi.org/10.1186/s12913-016-1457-8 55. Mwisongo A, Nabyonga-Orem J (2016) Global health initiatives in Africa—governance, priorities, harmonisation and alignment. BMC Health Serv Res 16(Suppl 4):212. https://doi. org/10.1186/s12913-016-1448-9 56. Blanchet K, Nam SL, Ramalingam B, Pozo-Martin F (2017) Governance and capacity to manage resilience of health systems: towards a new conceptual framework. Int J Health Policy Manag 6(8):431–435. https://doi.org/10.15171/ijhpm.2017.36 57. Haldane V, Ong SE, Chuah FL, Legido-Quigley H (2017) Health systems resilience: meaningful construct or catchphrase? Lancet 389(10078):1513. https://doi.org/10.1016/ S0140-6736(17)30946-7 58. Cardoso MA, Brito RS, Pereira C, Gonzalez ASJ, Telhado MJ (2020) RAF resilience assessment framework—a tool to support cities’ action planning. Sustainability 12(6):2349 59. Kruk ME, Ling EJ, Bitton A, Cammett M, Cavanaugh K, Chopra M, El-Jardali F, Macauley RJ, Muraguri MK, Konuma S, Marten R, Martineau F, Myers M, Rasanathan K, Ruelas E, Soucat A, Sugihantono A, Warnken H (2017) Building resilient health systems: a proposal for a resilience index. BMJ 357:j2323. https://doi.org/10.1136/bmj.j2323

Juliet Nabyonga-Orem is a leading health systems expert with experience spanning over two decades. She has been instrumental in transforming health systems in many African countries and has published extensively in health systems and services. She is a member of several scientific committees, including the Africa Health Economics and Policy Association, and the Portfolio board for Global development and international relations of the Research Council of Norway, and a member of the Scientific advisory committee of the European and developing countries clinical trials Partnership (EDCTP). She is an Associate Professor at North-West University, Potchefstroom Campus, South Africa. She is an Editor for the reputable BMC Public health, Frontiers Public Health, has served as the guest editor for several supplements in health systems, and has led the analysis and publication of impactful reports. Juliet is a graduate of Makerere University, Kampala, Uganda, where she obtained an M.B. ChB, obtained an MSc in Health Economics from the University of York, UK, and a Ph.D. in Public Health from the Catholic University of Louvain, Belgium. Hillary Kipruto is a health systems expert interested in Health Information Systems and Sector Monitoring with experience spanning 15 years. He is specialized in Bayesian modeling, sector monitoring, survey designs and implementation, and setting up and maintaining Civil Registration and Vital Statistics Systems (CRVSS). He has been instrumental in the transformation of health information and knowledge management systems in many African countries. He is passionate about UHC’s potential to confer to the most vulnerable communities across the region if a robust nexus is established between knowledge generation and policy action by the decision-makers. He has published extensively in the area of health systems and infectious diseases. Hillary holds a Ph.D. in Applied Statistics from Jomo Kenyatta

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Navigating Global Public Influenza Surveillance Systems for Reliable Forecasting Ryan B. Simpson, Jordyn Gottlieb, Bingjie Zhou, Shiwei Liang, Xu Jiang, Meghan A. Hartwick, and Elena N. Naumova

The future will be determined in part by happenings that it is impossible to foresee; it will also be influenced by trends that are now existent and observable. Emily Greene Balch

Summary

Global public health surveillance systems are imperative for monitoring, managing, and mitigating worldwide public health risks. Sustaining and enhancing these systems is imperative to detect, track, model, and predict outbreaks and pandemics of infections. Our understanding of the complex

R. B. Simpson
J. Gottlieb
B. Zhou
S. Liang
X. Jiang
M. A. Hartwick
E. N. Naumova (&) Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA e-mail: [email protected] R. B. Simpson e-mail: [email protected] J. Gottlieb e-mail: [email protected] B. Zhou e-mail: [email protected] S. Liang e-mail: [email protected] X. Jiang e-mail: [email protected] M. A. Hartwick
E. N. Naumova Tufts Initiative for the Forecasting and Modeling of Infectious Diseases, Tufts University, 150 Harrison Avenue, Jaharis-264, Boston, MA 02111, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_6

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dynamic systems and governing principles of outbreaks’ transmission and control depends on coordinated and integrated information gathering and processing. The interdisciplinary vision for such integration, data sharing, and curation warrants the development of reliable forecasts for circulating and emerging infections. Health professionals, researchers, and policymakers rely on the ongoing curation of public surveillance data. This chapter describes the role that public health surveillance data can play when modeling infectious disease outbreaks. We provide an overview of our previous work extracting, describing, and analyzing public and private health surveillance data to calculate trend and seasonality features of disease outbreaks. We describe important health surveillance data attributes that must be assessed for enabling modern analytical tools, including time series analysis, machine learning, and artificial intelligence methods. We illustrate these attributes’ importance for modeling disease outbreaks using the publicly available global influenza surveillance database FluNet. We conclude the chapter by discussing the importance of data integrity for the future of disease forecasting. Given the economic, social, political, and public health repercussions of the ongoing novel coronavirus (COVID-19) 2019–20 pandemic, greater attention is necessary on the attributes of surveillance data that render it usable for describing, explaining, and predicting disease outbreaks. Graphical Abstract/Art Performance

Global strides for harmonized, complete, and reliable monitoring and forecasting: completeness of influenza reporting in 166 FluNet-reporting countries and territories (2008–2019).

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The code of this chapter is 01110010 01,110,011 01,100,101 01,100,111 01,000,110 01,100,011 01,100,001 01,101,001 01,101,111 01,101,110 01,110,100. Keywords













Completeness Data integrity FluNet Infectious disease forecasting Influenza Public data Surveillance Time series World Health Organization




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Introduction

Public health surveillance systems (PHSS) serve a critical function of monitoring trends in disease trajectories important in guiding programs, policies, strategies, and recommendations to improve health. Routine monitoring allows to detect of the early warning signals of disease outbreaks and therefore contributes to strengthening the global response by connecting the response efforts and actions at the local, regional, and global levels. Extensive time, personnel, and fiscal resources are invested in collecting, processing, and maintaining time referenced and geographically tagged data for many infections of global concerns. With growing technological advancements in data gathering and processing, modern surveillance systems’ role continues to grow at local, national, regional, and global scales [1]. The data produced by these systems are an invaluable resource for enabling and protecting public health worldwide. The detailed analysis of information collected by surveillance systems can better mitigate diseases and improve public health decision-making. To acquire this critical information, ministries of health, program managers, surveillance officers, and public health laboratory personnel must collaborate to collect, verify, merge, and transform numerous data streams into products that can be analyzed to make immediate actions and form short-term and long-term strategies. These efforts are made possible by closely monitoring and evaluating disease data, including the detection, registration, confirmation, reporting, and dissemination of cases, hospitalizations, and deaths [2]. The intergovernmental collaboration required for surveillance data curation culminates in global disease control programming, including the development of vaccines, treatment protocols, non-therapeutic schemes, and approaches [3–11]. The ongoing efforts to build and support global surveillance systems offer a new benefit to the public health community—the ability to develop reliable disease forecasts. By publishing pre-processed data in standardized formats, a global community of data analysts, modelers, and forecasters can further advance surveillance data usability [2].

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This chapter shows how publicly available data arranged as time series datasets can be analyzed and interpreted to monitor trends and seasonality features for informing epidemic preparedness global responses. We illustrate the importance of the surveillance system attributes for modeling disease outbreaks using the publicly available global influenza surveillance database FluNet. We conclude the chapter by discussing the effects of data integrity on the future of disease forecasting.

2

The Use of Surveillance Data for Modeling Disease Outbreaks

Time referenced and geographically tagged records collected and maintained by large-scale health systems offer opportunities to explore disease patterns and how such patterns change over time and across populations. Health records should be arranged in chronological order so that records are presented in the required format called time-series data. By presenting data in time series format, we can use powerful analytical tools of time series analysis and machine learning to describe, define, and predict temporal behavior in disease outbreaks: how they start, peak, and resolve. The most attractive time series analysis features are its mathematical rigor for developing forecasts, accounting for uncertainties, and describing disease dynamics. Epidemiologists are expanding the range of applications where time series analysis offers substantial advantages in defining and characterizing the localized outbreaks, seasonal manifestation of regularly occurring infections, and spread of infections during pandemics. Frequent applications of time series methods are to model disease trends and quantify the features of seasonal infections. Many respiratory and enteric infections are seasonal in nature. Some infections appear during warm seasons, others during winter times, and their patterns are well documented. Yet, a reliable forecast on when and where an outbreak will be observed in a given season is still challenging. To build reliable forecasts, we need to learn disease temporal and spatial patterns and how they evolve. This information enables the creation of tools to monitor diseases in real-time, develop predictive models, and correct disease trajectories as observed on their transmission paths—like meteorologists predicting the path of a storm. Our research focuses on describing and explaining the magnitude and timing of peaks of infectious outbreaks at the local, national, and global levels. This section shows how our understanding of disease patterns could lead to improved strategies for disease forecasting. In the examples described below, we used large national databases of hospitalization records, data from regional and national surveillance systems, and data abstracted from multiple published sources.

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2.1 Establishing Traveling Waves of Infections In temperate climates, the risk of influenza increases in winter times with the arrival of colder weather. Yet, the mechanism underlying such a well-defined seasonal pattern is less understood. Moreover, the ways how these factors contribute to the seasonal transmission of emerging and re-emerging new strains remains elusive. In the warm and tropical climates, such interplay often involves zoonotic transmission, calling for the need for integrated surveillance of influenza in domestic and wildlife. Seasonal influenza epidemics typically spread over large geographic areas representing many climatic zones. Using *250,000 records of hospitalizations of pneumonia and influenza (P&I), we examined variations in seasonal patterns in older residents (65 + y.o.) of the United States for several influenza seasons from 1991 to 2004 [12]. We extracted all relevant records from databases maintained by the Centers for Medicare and Medicaid Services (CMS), arranged the records as daily time series on a state-by-state basis, and demonstrated that during the study period, Western states, including Nevada, Utah, and California, peaked earliest in the influenza season while Northeastern states, including Maine, New Hampshire, and Rhode Island, peaked 2–4 weeks later. A strong positive correlation was found between latitude and peak timing, signifying a traveling wave of infection from west-to-east nationwide. This type of information could be used to build influenza forecast models considering the order where the infection could appear in a local, regional, national, or global context.

2.2 Developing Dynamic Mapping Combined with Risk Factors In general, annual epidemics of seasonal influenza are starting abruptly, peaking within about two to three weeks and usually ending within five to ten weeks. Traveling waves of infections could be carefully examined using a combination of mapping techniques and time series methods or dynamic temporally ordered maps depicting the spatial spread of an outcome over time [13, 14]. Using hospitalization records from CMS, we have demonstrated that each seasonal outbreak had its own spread pattern. Heterogeneous seasonal patterns might have their roots in the emergence of a novel strain and strain-specific differences in pathogenicity, differences in cross-protective immunity gained from infection with a previously observed strain, differences in hosts’ behavioral characteristics due to environmental exposures like ambient temperature. Yet, the disease seasonality and its governing factors remain intertwined so that they can offer an integrated approach for local and global forecasts of infectious diseases. To illustrate these relationships, we applied a spatio-temporal dynamic mapping approach [13]. Superimposing maps of health outcomes and ambient temperature, we illustrate traveling waves of P&I hospitalizations across several climatic zones nationwide [12]. When we apply dynamic mapping to seasonal infections, we could depict how such infections are driven by a temperature gradient across geographic locations. Fusing daily records of

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hospitalization due to Salmonella infection with ambient temperature from the PRISM Group at Oregon State University and chicken broiler sales from the Census of Agriculture, we demonstrated how dynamic maps could explore disease dynamics and generate research questions. For example, we show a dense cluster in Mississippi where July peaks of Salmonella hospitalizations aligned with peaks in ambient temperatures and chicken broiler sales [13]. Dynamic maps offer detailed examination on when and where disease clusters emerge, how they spread, and where they persist for informing short- and long-term policies surrounding forecast predictions.

2.3 Considering Seasonal Migration and the Snowbird Effect Based on CMS data, Chui et al. evaluated spatiotemporal dynamics of seasonal influenza from July 1991 through June 2016 in older adults residing in the United States [15]. By incorporating information on resident status, we determined that state-by-state patterns of P&I differed between resident and non-resident hospitalizations; states such as California, Arizona, Texas, and Florida showed higher proportions of non-resident hospitalizations in winter months than summer months. This suggests that out-of-state residents were responsible for higher intensities of wintertime influenza outbreaks in these states due to the seasonal migration of the elderly. These types of analyses integrated with the knowledge of local and global traveling patterns are essential for developing forecasts and understanding how travel restrictions could affect disease transmission.

2.4 Considering the Individual Vulnerability of the Aging Population Assessments on influenza seasonality might be a challenging task, especially in the elderly population. For monitoring efforts to succeed and prove useful, the high-quality publicly reported surveillance data on demographic characteristics (like age, gender, and residential location) and clinical measures (like disease severity and health outcomes) have to be effectively compiled, validated, and managed for storage and retention. Critical to examining the seasonal spatiotemporal dynamics is the granularity of subpopulation surveillance data available for analysis. Risk factors implicated for driving seasonal variations are likely to differ between persons of different ages or existing conditions, even if seasonal peak timing and intensity estimates are similar [14]. Comparisons across subpopulations concerning seasonal drivers of infection must be done cautiously and use standardized methodological approaches. Pneumonia and influenza (P&I) diagnosis is ranked as the sixth leading cause of hospitalization in the USA for older people. The aging subpopulation is growing worldwide and as a fraction of immunocompromised adults with cognitive impairments [16, 17]. We abstracted over 6 million records of hospitalization of older people in the USA to examine the associations with the broad range of factors

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involving household and per capita income, relative population density, the ratio of older adults who live in nursing homes, and the index of relative rurality [16]. The P&I incidence was highest in rural and poor counties. Patients with dementia had a decrease in reported influenza diagnosis and hospital length of stay but a 1.5 times greater risk of death compared to the national average. Among all older patients hospitalized with P&I, the HIV-positive seniors represent a growing fraction, and despite the available treatment options, they still had about a 50% higher death rate than HIV-negative persons. As such, we can see that differences in risk factors for the same subpopulation can result in differing infection dynamics. Standardized modeling approaches are imperative for infectious disease forecasting, significantly as those forecasts predict differences in seasonal features across vulnerable subpopulations.

2.5 Forging Insights for Testing: From Non-Specific to Specific Chui et al. also used CMS hospitalization records from 1991–2004 to document seasonal patterns of specific and non-specific gastrointestinal conditions in older adults in the US [18]. Using this high-resolution daily time series data, we showed that 11 gastrointestinal illnesses demonstrated significant seasonality, with peaks predominantly occurring in early March between the 58th and 61st calendar days and peak timing varying by illness. Most importantly, some non-specific gastrointestinal conditions peaked very closely at the time of well-defined infections, allowing public health professionals to have an educated guess on the potential causal agent and developing strategies for targeted testing. Finer temporal and spatial granularity is imperative for improving disease forecasts’ accuracy and precision to assess differences across illnesses. Comparisons of peak timing alignment across infections can also inform testing requirements for seasonal reportable diseases. Using laboratory-confirmed cases of six diseases abstracted from the Massachusetts Department of Public Health from 01 January 1992 to 31 December 2001, we examined the synchronization of peak timing concerning ambient temperature [19]. We found that all examined infections could be classified into two distinct clusters of seasonal patterns: those with peaks coinciding with peak ambient temperatures, such as Salmonella and Campylobacter, and those with peaks nearly one month after temperature peaks, such as cryptosporidiosis, shigellosis, and giardiasis. Variability in peak timing, especially concerning environmental processes, allows for more targeted disease surveillance and rigorous testing protocols before and during times of expected peaks. This is further improved with granular temporal and spatial information, which prevents masking of seasonality features due to data aggregation.

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2.6 Creating National Disease Calendars We used national surveillance data reported by the Australian National Notifiable Disease Surveillance System (NNDSS) to describe seasonal patterns of three vector-borne diseases from 1991 to 2015 [20]. Results demonstrated that the peak timing of Ross River virus and Barmah Forest virus peaked annually between January and February, while Dengue fever peaked annually between November and December. As Chui and colleagues showed for the peak timing of gastrointestinal illnesses in the US, Stratton and colleagues showed how the peak timing in vectorborne illnesses varied in Australia. We also have to recognize the importance of local calendars as they reflect nuanced climatic conditions, social and religious traditions, agricultural practices, travel, and seasonal migrations. While epidemiological research is geared toward using the Gregorian calendar for standardizations, it could mask important features. Routine surveillance conducted over extensive periods absorbing and processing local and national temporal variation allows for enhanced forecast precision when using time series data. This enables creating local, regional, and national disease calendars for global forecasts so long as the surveillance systems used sustain frequent and timely reporting.

2.7 Integrating Social Calendars into Modeling By utilizing this health surveillance data, we demonstrate the role that public health policies and programming, like the timing of academic holidays, can influence infection rates even at the local level. Using weekly records of confirmed cases of influenza in the city of Milwaukee, Wisconsin (USA), Simpson et al. demonstrated that age-specific rates of influenza A and B varied according to the proximity of peak timing and school holidays [21]. We found that influenza A peak timing tended to peak during February after Milwaukee schools’ Winter Break, which dampened influenza intensity in school-aged children. Moreover, influenza B tended to peak in early March and coincided with the Super Bowl and Milwaukee schools’ Spring Break, which amplified seasonal peak intensity for school-aged children. Health surveillance data integrated with information on social events is valuable to access the risk factors expected to dampen or amplify transmission dynamics and peak infection intensity. Such integration is critical for building reliable forecasts.

2.8 Creating Regional Integrated Disease Calendars In a meta-analysis study of rotavirus diarrhea surveillance data, Jagai et al. examined associations between rotavirus peak timing and meteorological characteristics within Southeast Asia [22]. We determined that rotavirus was associated with cold, dry months in this region through careful extraction of monthly time series surveillance data from published works and alignment of monthly

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temperature, precipitation, and surveillance data. We further identified that these associations varied by geographic zones and climate conditions, suggesting the need for more targeted testing and implementation of vaccinations. In addition to describing and comparing spatiotemporal fluctuations in seasonal outbreaks, this analysis illustrated how surveillance data could be integrated with information on risk factors driving seasonal disease outbreaks. When properly aligned with other publicly available data streams, health surveillance data helps evaluate whether disease outbreaks’ timing and intensity are associated with the timing and intensity of other seasonal factors. Such integration is critical for tailoring the forecast to local conditions.

2.9 Consolidating the Efforts of Multiple Agencies Similar associations between disease outbreaks and environmental processes can be drawn when monitoring health effects for extreme weather events or natural disasters. For example, Naumova et al. characterized the effect of the Pichincha volcanic eruptions in Quito, Ecuador, in April 2000 using hospitalization records from Baca Ortiz Children’s Hospital [23]. Results showed that children experienced twice as many emergency-room visits during volcanic activity periods compared to periods of pre- and post-eruption. These results illustrated how surveillance health records could be used to develop pre-disaster preparedness planning, especially among vulnerable populations residing in disaster-prone areas. In developing reliable forecasts for infectious outbreaks in disaster-prone areas, forecasters must capitalize on pre-disaster preparedness planning and the ability to mobilize resources using integrative approaches.

3

Attributes of a Mature Surveillance System

As the volume and velocity of health surveillance records continue to grow, data users must be conscious of the structure and integrity of data used when developing reliable forecasts. This includes a detailed assessment of the health outcomes reported, the representativeness of reported cases, and the available data’s completeness. Only by clearly understanding these attributes can data users appropriately modify, adapt, and apply modeling approaches for disease forecasting. Given the extensive time and resources used for its surveillance worldwide, seasonal influenza provides an exceptional example for illustrating the role of data curation and integrity for reliable forecasting. For over 70 years, the World Health Organization (WHO) has delivered the information management for epidemiological data of seasonal, zoonotic, and pandemic influenza threats from the collection and dissemination of publicly available data to monitoring, planning, and implementation of alert and early warnings strategies [24]. In 1952, the Global Influenza Surveillance Network (GISN) began

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monitoring global influenza trends [25, 26] and over time has matured and expanded to provide rapid testing of influenza worldwide [25, 27–30]. In 1998, GISN developed a publicly available database for laboratory-confirmed influenza weekly cases, FluNet, which spans over 140 national influenza centers (NICs) from 113 countries [27, 29, 30]. The public dissemination of the chronologically ordered country-based time series data has encouraged the development of analytical approaches to describe, explain, and predict influenza patterns across periods, countries, and subtypes [31]. Since 1998, the WHO has consistently revised guidelines for efficiently processing and disseminating surveillance data. The A(H1N1) 2009 pandemic emphasized this need specifically for global influenza surveillance and led to revised influenza-specific surveillance guidelines in 2013. This focus gave rise to influenza-specific monitoring and evaluation (M&E) indicators, which aim to improve early outbreak warnings and influenza epidemic detection at the local and global scale [32]. For example, the timeliness indicator measures the delay between culture collection, laboratory testing, and surveillance reporting, including data dissemination from subnational and national sites to the sentinel, universal, or national surveillance systems. Similarly, completeness provides information on missing records by comparing differences between tests conducted and results reported. Finally, the consistency indicator provides information on how cases fluctuate over time, including transitions from suspected to confirmed cases. Together, these M&E indicators provide clear standards to national agencies responsible for influenza data collection to ensure the proper control and management of health resources. The revised 2013 guidelines emphasize data collection’s importance over data dissemination or analysis [32]. For example, key messages state that “data collection should be kept at a minimum and include only data needed for public health decision-making.” Furthermore, data reporting suggests a more relaxed approach from the expected weekly occurrence and instead “can be summarized and reported less frequently.” This creates a disconnect between data availability for public use and external data users’ ability to conduct analyses that “provide the means to monitor the course of the epidemic” by monitoring influenza trends and seasonality. The encouraging of data collection without its swift analysis inhibits data translation in a routine, standardized manner to generate information imperative for molding influenza health policy. By emphasizing the standardization of case definitions, operation manuals, and procedures across counties and regions, data users will expand how publicly disseminated data can be explored and analyzed. The WHO has warned the lack of established surveillance for severe influenza cases and the absence of historical data has contributed to the Member States’ abilities to forecast future outbreaks. Particularly, a lack of historical data prevents extrapolating the severity of outbreaks from previous seasons or evaluating changing disease patterns. The WHO also recognized the compilation of historical data for severe respiratory diseases associated with the influenza virus is necessary for timely comparative assessment of

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influenza seasons and prediction of future epidemics at the local and global scales in a more accurate way. To enable reliable forecasting, modelers must recognize that the structure and attributes of time series data dictate the accuracy and fidelity of statistical analyses performed using the data. A better understanding of the attributes of publicly disseminated surveillance data includes understanding factors governing data integrity and the current status of data completeness. When external users in charge of conducting research and analyses that inform health policies and programming are equipped with such information, they could avoid making improper modeling assumptions, drawing inappropriate conclusions, or failing to comprehend the constraints of the time series data they analyze. Below, we show an example of how data integrity and completeness could be assessed using FluNet records.

3.1 FluNet Data Integrity To improve global surveillance data usability, greater attention is needed to the attributes and structure of how data derived from Member States’ surveillance systems are compiled and presented to external users. As a means of demonstration, we compiled the case definitions, surveillance strategies, sampling quota, reporting lags, and surveillance capacity of FluNet data for 166 countries and territories worldwide using the most recent reports published between 01 January 2015 and 01 July 2020 [9, 33–74]. We abstracted information on case definitions, the type of surveillance strategies, and reporting quota characteristics and noted when such information was not available for each country. We aggregated the abstracted information within 6 WHO regions that report influenza surveillance data to FluNet as of 01 July 2020. The WHO Regions included: the African Region (AFRO), the Eastern Mediterranean Region (EMRO), the European Region (EURO), the Region of the Americas (PAHO), the South-East Asia Region (SEARO), and the Western Pacific Region (WPRO). Table 1 summarizes general characteristics of the regions and includes the total number of national influenza centers (NICs) and influenza-reporting facilities, regional averages for the number of people served by NICs and facilities (in millions), the average reporting lags (in weeks), and the percent of countries in a region that use specific case definitions in their national surveillance. Below, we describe each attribute of the abstracted surveillance data and explain its importance in the context of modeling infectious disease outbreaks and building reliable forecasts.

3.1.1 NICs and Facilities NICs are national institutions designated by national Ministries of Health and recognized by the WHO. They have a responsibility to collect virus specimens, perform preliminary analysis, and report influenza surveillance records to FluNet [31]. These NICs represent a fraction of influenza reporting facilities such as SARI hospitals, ILI centers, polymerase chain reaction (PCR) testing facilities, and influenza (IF) testing laboratories. These facilities are different in number from

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Table 1 Summary statistics for general regional characteristics and the attributes of the influenza surveillance systems for 166 Member States’ participating in FluNet based on published reports [9, 33–74] and prepared with data from 2018 population estimates, as reported by the World Bank’s World Development Indicators (as marked with *) [75] AFRO General regional characteristics Total number of countries 29 Total population (in 959.7 millions)* 20.17 Total land area (in millions km2)* Average population 105.1 density per km2* Total number of NICs 15 Total number of facilities 272 64.0 Average number of people per NIC (in millions) Average number of 3.52 people per facility (in millions) Average reporting lag (in 51.8 weeks) Use of case definitions ILI 96.6 ARI 3.45 SARI 75.9 Pneumonia 3.45 Influenza 0.00 Mortality 27.6

EMRO

EURO

PAHO

SEARO

WPRO

18 664.0

51 926.7

42 1013.0

11 1982.6

15 1876.3

8.3

27.0

38.5

6.5

21.2

292.7

140.5

166.5

421.6

664.6

17 1266 39.1

54 26,342 17.2

36 5138 28.1

10 347 198.3

19 1505 98.8

0.52

0.04

0.20

5.71

1.25

47.8

11.2

18.7

14.1

14.1

44.4 5.56 27.8 5.56 0.00 5.56

96.1 62.8 43.1 0.00 33.3 0.00

50.0 40.5 64.3 33.3 50.0 33.3

100.0 18.2 90.9 9.09 9.09 0.00

80.0 0.00 40.0 6.67 13.3 6.67

country to country but also process different testing activities in terms of volumes and speeds depending on the health outcome so as relevant timeframes and quotas are managed differently. Although FluNet is weekly-focused surveillance, the internal reporting timeframes present data on a daily, weekly, or monthly basis and thus, differ between countries. As shown in Table 2, the EURO and PAHO region have 2–3 times more NICs and 5–100 times more facilities than other regions. The extensive infrastructure results in increased capture rates of infected persons [76– 79] and has a greater chance of reflecting the true infected, tested and treated population. In addition to the total counts of NICs and facilities per region, we also estimated the average number of people served by NICs and facilities. These estimates emphasize disparities in the number of persons covered in FluNet surveillance across WHO regions. As expected, the EURO region has the fewest number of

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Table 2 The average and overall completeness of four influenza variables for 166 FluNet-reporting countries and territories across 6 WHO regions as of 01 July 2020 over the 626-week time series starting from Week 1 2008 to Week 52 2019. Completeness values range from 0.00% (no weeks of reported data) to 100.00% (all weeks of reported data). The regional average, top-three countries per region, and bottom-three countries per region are listed in descending order by overall completeness Country

Tests

Influenza A

African Region (AFRO) Regional average 65.42 64.77 Top 3 counties Ghana 99.84 99.84 Madagascar 97.64 97.80 Cameroon 97.64 97.48 Bottom 3 countries Seychelles 8.33 8.33 Chad 9.13 7.85 Congo 5.93 5.93 Eastern Mediterranean Region (EMRO) Regional Average 55.62 53.84 Top 3 counties 87.26 99.69 Islamic Republic of Iran Pakistan 93.63 93.63 Egypt 95.35 95.35 Bottom 3 countries Yemen 8.17 6.57 Sudan 3.30 3.30 United Arab Emirates 0.00 0.00 European Region (EURO) Regional Average 71.02 71.25 Top 5 counties Poland 99.84 100.00 Norway 98.90 98.90 Slovenia 99.84 99.84 Bottom 5 countries Tajikistan 16.03 16.03 Cyprus 14.26 14.26 Turkmenistan 8.81 8.81 Region of the Americas (PAHO) Regional Average 59.57 59.92 Top 3 counties Mexico 99.84 99.84

Influenza B

A(H1N1) pdm09

Overall

61.75

62.30

63.56

99.84 97.80 89.14

99.84 85.69 89.30

99.84 94.73 93.39

8.33 7.85 5.93

8.33 7.85 5.93

8.33 8.17 5.93

49.70

50.84

52.50

97.92

87.42

93.07

88.66 75.16

93.47 91.67

92.34 89.38

0.00 1.26 0.00

6.57 3.30 0.00

5.33 2.79 0.00

70.19

66.88

69.83

98.56 98.90 95.24

91.51 88.99 87.73

97.48 96.42 95.66

16.03 14.26 8.81

16.03 14.26 8.81

16.03 14.26 8.81

59.92

49.86

57.31

99.84

91.67

97.80 (continued)

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Table 2 (continued) Country

Tests

Paraguay 99.68 United States 100.00 Bottom 3 countries Bahamas 0.80 Antigua Barbuda 0.32 Guyana 0.32 South-East Asia Region (SEARO) Regional Average 62.89 Top 3 counties Thailand 99.04 India 96.03 Sri Lanka 90.72 Bottom 3 counties Maldives 37.52 Timor-Leste 20.35 North Korea 5.61 Western Pacific Region (WPRO) Regional Average 79.07 Top 5 counties Malaysia 100.00 Cambodia 99.37 Mongolia 99.84 Bottom 3 counties Fiji 78.54 Papua New Guinea 40.07 New Zealand 21.08

Influenza A

Influenza B

A(H1N1) pdm09

Overall

99.68 100.00

99.68 100.00

92.15 89.47

97.80 97.37

0.80 0.32 0.32

0.80 0.32 0.32

0.32 0.00 0.00

0.68 0.24 0.24

60.77

60.07

57.04

60.19

99.20 92.53 90.24

98.88 85.30 90.09

90.87 84.69 72.21

97.00 89.64 85.82

22.41 20.35 5.61

19.21 20.35 5.61

20.01 20.35 5.61

24.79 20.35 5.61

87.71

86.53

80.79

83.53

100.00 99.53 99.84

100.00 99.05 99.84

100.00 99.37 87.26

100.00 99.33 96.70

65.88 40.07 48.55

65.24 40.07 46.18

62.52 40.07 41.52

68.05 40.07 39.33

persons per NIC and per facility, with one-half the persons per NIC and one-fifth the persons per facility than the PAHO region. While EMRO and WPRO have a similar number of NICs and facilities, the testing capacities in WPRO should serve nearly 2.5-times more people on average. In the SEARO regions, 10 NICs and 347 facilities should serve 198 and 6 million people each, respectively, to ensure full population coverage. This summary emphasizes the great diversity in population coverage across WHO regions and does not reflect the true access to testing and test availability. Surveillance sites are noted to possibly neglect groups of people that do not have access to a hospital, have misconceptions on vaccine effectiveness, are unaware of immunization recommendations, or share a belief among some patients and physicians that influenza is not an important disease [80–83]. In some instances,

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surveillance systems only include information based on samples taken from patients attending specialized healthcare centers or from particular sub-populations; such selection may not represent the entire population and thus, bias the analysis. Underreporting is likely because those experiencing the disease in its mild form likely do not seek medical attention. Furthermore, the surveillance sites use administrative centers as population representations when they may only account for a small population percentage. The impact influenza has on marginalized populations or those who do not seek medical attention is unknown. These factors affect outbreak modeling, and the forecasts developed with FluNet reported data and may not accurately capture true influenza activity.

3.1.2 Reporting Lag In addition to the representativeness of data, information on the timeliness and completeness of data greatly influences forecast models’ accuracy and reliability. Reporting lags describe the time difference between the last reported observation and the data submission date, or more simply, the delay of health reporting. Lags differ by the case definition, surveillance strategy, and facility type within a country and are reported in the units or terms recorded within the surveillance system. Differences in reporting delays suggest that publicly reported data from one week may change or update over the weeks or even months after that. Retroactive revisions in publicly available data minimize the consistency and generalizability of statistical findings, which are dependent on the time that data were accessed for analysis. Without reporting surveillance metrics like timeliness or consistency, researchers using data might be uninformed of the reliability of the data they use. For example, reporting lags by region show that AFRO and EMRO regions have on average one-year of delayed reporting to FluNet (*47–52 weeks). In contrast, EURO, SEARO, and WPRO regions have much shorter delays of 2.5–3 months (*11–14 weeks). The delays in reporting for countries in AFRO and EMRO regions could have been associated with conflicts, social unrest, economic difficulties, or natural disasters [8, 84, 85]. The current value of influenza forecasts based on public surveillance data in these areas is questionable due to substantial reporting delays. Thus, it is more realistic to use select countries for reliable forecasts in the AFRO or EMRO regions when developing influenza outbreak projections. It is also critical to invest in the infrastructure to improve the reporting. 3.1.3 Case Definition Case definitions refer to the specific health outcomes used as the base for laboratory testing. Each case follows a different technical definition and is identified in a different healthcare setting. For some countries, these definitions follow an international reporting protocol as established by the WHO. For others, case definitions meet a portion of these international standards while also incorporating or omitting required symptoms of illness. Some countries use unique or ICD-10 definitions. It is generally assumed that whereas influenza-like illness (ILI) and severe acute respiratory infection (SARI) cases have experienced symptoms for ten days before the examination, the latter requires hospitalization. In contrast, the diagnosis of acute

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respiratory infection (ARI) is made in outpatient facilities and determined based on the clinician’s judgment according to four respiratory symptoms, only one of which overlaps with ILI and SARI. All these three case definitions differ from WHO case definitions of pneumonia, including the rate of breathing, vomiting, and difficulty swallowing as diagnostic criteria. Some countries define pneumonia according to the ICD-10 codes that overlap with SARI, ILI, and ARI. We calculated the percent of countries per region that utilized each of six case definitions and observed large country-to-country variations in the reported types of health outcomes, as well as the regional variations (Table 1). Different definitions might lead to the variability of forecasts because it would be based on more- and less-severe forms of illness and reduce modeling results’ generalizability. This begs the question: does the variety of case definitions examined across a country’s surveillance system influence the volume of cases reported and the generalizability of forecasts developed? On the one hand, more sensitive forecasts predict outbreaks based on mild health outcomes, enabling early outbreak detection that can mitigate more severe health outcomes. On the other hand, less generalized case definitions increase the risk for type II error and decrease forecast reliability. The reliable forecasts should allow improved detection of signal-to-noise ratios required for modeling epidemic curves and assessing reporting algorithms’ sensitivity [1]. Thus, the information on the utilized case definitions is essential for external users modeling disease trends.

3.1.4 Surveillance Strategy While case definitions may broadly be similar across countries, different case definitions and quotas may be monitored by different surveillance strategies. The surveillance strategy describes the population coverage and structure of the facilities involved in influenza data collection. These strategies are one or a combination of sentinel, national, or universal surveillance systems dependent on a country’s priorities and available health resources. For example, sentinel surveillance involves a select number of sites intended to be representative of a population. These sites consistently and systematically collect health records from patients that adhere to specific case definitions, usually ILI and SARI [32]. The surveillance system’s consistency and standardization ultimately dictate the quota of cases that are manageable for collection. The WHO reports sentinel surveillance systems as the most resource-efficient structure with reliable data to monitor influenza. By comparison, universal surveillance systems are more resource-intensive, including all healthcare facilities and providers, and report on specified events, including clinician-reported respiratory disease prevalence. Finally, national systems include all healthcare facilities nationwide and tend to monitor influenza-associated mortality. The types of surveillance strategies and reporting quotas implemented within a country suggest vast differences in FluNet data’s representativeness. As surveillance systems vary in size and scope, the publicly available surveillance data disseminated via FluNet vary concerning the representativeness of health outcomes. As shown in Fig. 1, 96–100% of countries in the AFRO, EURO, and SEARO

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Fig. 1 Cumulative percent of countries using case definitions: influenza-like-illness (ILI), acute respiratory illness (ARI), severe acute respiratory illness (SARI), pneumonia, influenza, and mortality by different surveillance strategies: sentinel, national, and universal per WHO region

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regions use ILI cases for testing. Thus, influenza forecasts using these data are likely to reliably predict trends and seasonality features of ILI for these countries. However, *80% of these countries use sentinel surveillance systems suggesting that forecasts are generalizable for only the specific geographic locations selected for health data reporting. However, approximately one-third of countries in the EURO region use universal surveillance systems, resulting in wider population coverage than sentinel systems. This tradeoff between the representativeness and generalizability of influenza forecasts must be considered when evaluating surveillance data comprised of numerous case definitions and surveillance strategies.

3.2 FluNet Data Completeness Besides having accurate data collection, forecasting models require complete time-series data for reliable estimation of trend and seasonality features. As surveillance data are time-referenced, time series analyses require lengthy historical reference periods to best determine longitudinal fluctuations in rates and seasonality features, like peak timing and peak intensity, over time. We measure the completeness of available time-series data by calculating the effective time-series length for four influenza test outcomes as reported by FluNet. To perform these calculations, we extracted FluNet weekly records on 01 July 2020. The records included 166 countries and territories reporting to FluNet from Week 1 2008 (31 December 2007) through Week 52 2019 (29 December 2019). We downloaded each country’s records individually using a scripted pipeline to standardize and merge country-specific datasets. We extracted time series for three available testing outcomes: specimens processed (tests), influenza A positives, and influenza B positives; and for A(H1N1)pdm09 positives as a marker of dedicated influenza surveillance attention after the 2009 global pandemic, e.g., specimens processed (tests), influenza A positives, and influenza B positives; and A(H1N1) pdm09 positives as a marker of dedicated influenza surveillance attention after the 2009 global pandemic. The overall data compilation included these four outcomes for 166 countries covering 626 weeks. We measured completeness using an effective time series length (ETSL), or the extent of time-series data that can be used in data analysis. This refers to the number of time units, in this case, weeks, where health outcome information is reported to FluNet. Time series length largely determines the method we use for completeness measurement. To ensure standardized comparisons, the completeness was estimated for each country using the same annual time series length (52 or 53 weeks). Using these annual values, the full time-series completeness for each outcome was computed as the average over the years 2008–2019. For each country, the overall completeness was computed as the average of the four available testing outcomes.

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Fig. 2 A map of average overall completeness for 166 FluNet-reporting countries and territories as of 01 July 2020 over the 626-week time series starting from Week 1 2008 to Week 52 2019. A purple color indicates near 100% completeness, while a white color indicates near 0% completeness

The annual completeness was computed using Ci,j,k “as a fraction of the time series length for which reliable data are available to the overall length of the considered time series, or the number of full weeks between the start and end of each year, multiplied by 100:” 
 Ci;j;k ¼ ni;j;k L1  100% where “Ci,j,k is completeness for i-outcome (i = 1–4), j-country (j = 1–166), k-year (k = 1–12); ni,j,k—the number of time units (weeks) in the time series when records are available (e.g. weeks with reported counts  0) for i-outcome, j-country, k-year; L1—the number of full weeks (52 or 53) for k-year. We calculated the annual completeness by using the total length in weeks (52 or 53) for each year. [88]” Table 2 summarizes completeness estimates for regional averages and top three and bottom three countries to illustrate the completeness metrics range. We also mapped the overall completeness for all countries worldwide in Fig. 2. By examining annual completeness measures, data analysts and external data users can make reliable estimations of the study interval and develop forecasts in a more accurate and realistic way. As shown in Table 2, average completeness varies widely across outcomes for each country and region. For example, 11 countries within the AFRO region have >80% completeness for all four health outcomes.

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These observations suggest consistent influenza surveillance investment over the past decade to monitor influenza activity (tests) and infectiousness (positives). However, six countries had R after the start of the process. After having defined the relevant elements of the phenomenon of the spread of a disease, we are able to define the following SIR epidemic model: dS dt dI dt dR dt

= −β IS N, =

β IS N

= γ I,

S(0) = S0 ≥ 0

− γ I , I (0) = I0 ≥ 0 R(0) = R0 ≥ 0

(1)

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where S(t) + I (t) + R(t) = N . If we divide by N (the total population) the Eq. 1 we get ds dt di dt

= −βis,

s(0) = s0 ≥ 0

= βis − γ i, i(0) = i0 ≥ 0

(2)

with r(t) = 1 − s(t) − i(t) where s(t), i(t) and r(t) are de fractions in the classes. We are also able to define the following SIR endemic model dS dt dI dt dR dt

= μN − μS − β IS N , S(0) = S0 ≥ 0 = β IS N − γ I − μI , I (0) = I0 ≥ 0 = γ I − μR,

(3)

R(0) = R0 ≥ 0

where S(t)+I (t)+R(t) = N . The SIR model (3) is very near to the epidemic version 1 with the difference that it has an inflow of newborns into the susceptible class at rate μN and an inflow of deaths into classes at a rate of μS, μI and μR. If we divide by N (the total population) the Eq. 3 we get ds dt di dt

= −βis + μ − μs, s(0) = s0 ≥ 0 = βis − (γ + μ)i, i(0) = i0 ≥ 0

(4)

with r(t) = 1 − s(t) − i(t) where s(t), i(t) and r(t) are de fractions in the classes. For a deep exposition of more sophisticated models and analysis of their respective thresholds consult [10].

3 Application of Models to Virus Spreading in Networks The exponential growth in the number of computers connected to the Internet has brought with it an increase in the complexity of computational problems that arise, such as security problems in a network or the need of quality of service in the distribution of content on P2P-type networks. Under this scenario, it is important to have models that allow to estimate how certainly a message will reach its destination, how information will be kept alive on a network, how a Social Network becomes polarized, or how to prevent a virus from spreading in such a way that it stops important fractions of a global network causing large economic losses. These topics have captured the attention of a growing number of computer scientist, physicists or mathematicians and led them to propose models to try to solve some of these problems. Some phenomena such as the emergence of giant components or small world phenomena that arise in graphs whose construction resembles the way sites are incorporated into the Internet have interested a large number of mathematicians who have addressed these problems using the mathematical tools of random graph theory and contact processes in probability theory [11]. Under this approach they

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have determined the conditions under which arise percolation phenomena that are the basis of many recent research works in the area of virus propagation in computer networks within which they stand out [12–14] as well as [15]. As for the group of researchers who have been interested in the study of virus propagation phenomena and who have studied it using methods that emerged in the area of statistical physics it is important to highlight the work of Romualdo Pastor-Satorras and Alessandro Vespignani in [16–18]. With regard to the issue of mechanisms of spreading rumours using methods inspired by contact processes, it is possible to highlight the article [19]. Another interesting article that proposes the distribution antidotes for controlling the virus spreading in networks can be found in [20]. As was mentioned in the abstract of the paper, the dynamical systems approach is very useful to determine the fast extinction condition of a virus spreading by the use of non-linear dynamic systems fix-point theorems. One of the first articles that I consulted where this approach was applied on virus propagation in P2P networks is [21]. This is the model that I will describe in this part of the present paper. The class of the epidemic model used in [21] is a SIS (Susceptible, Infected, Susceptible), that means that a node in a network is susceptible, can become infected by contact with an infected node in his neighborhood and after some elapsed time become susceptible again. Contributions made in [21] allow to analyze the survival of a infection in a population and are closely related to the results obtained in [13–15] where mathematical tools typical of graph theory are used. SIS and SIR mathematical models have been used successfully in the field of computer security to estimate epidemic thresholds and extinction conditions of worm-type viruses that spread on the Internet. [16–18]. Let’s imagine we have a network of N nodes and E lines of communication between them. In the same way, imagine that we take small discrete steps in time whose size is t where t → 0. The results that were raised in [21] can be extended to the continuous case. During the time interval t, each node i try to spread its information in each time step with probability ri , and each communication edge i → j is up with probability βi,j , and as a consequence the information is correctly propagated to node j. Each node i can fail with probability δi > 0. Each node j that is in dead state can become up with with rate γj and loosing the information that he had. The parameters involved in the mathematical model listed in the next Table. Looking at the diagram we could try to see each node as a Markov chain that passes through the states Has Info, No Info or Dead, that is, the total of possible configurations of the whole system would be 3N which makes the analysis of the system very difficult and for that reason it is preferable to approach the analysis of the system using fixed point stability theorems of non-linear dynamic systems [21]. The state transitions at each node are shown in the Fig. 2. I must mention that such a dynamic system has an absorbing state, which corresponds to the case in which no node is in the state Has Info. In this case the information dies with probability 1 as t → ∞. This means that after a very large amount of time all the nodes of the network don’t have the message. If this message is a virus then the nodes of the network will be healthy after after a very large amount of time. Thus, for some combinations of the values of the model parameters, the system is taken to the state of disappearance of the information, while for other values it is not. Now

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Fig. 2 Chakrabarti SIS model

we can ask what are the conditions under which the information lasts for a long time in the system?, and we may also ask Which values of the system parameters lead to a state of fast extinction of some information on the system? Next I will give a series of definitions that I took from the articles [21–23] Let C(t) represent the expected number of carriers (nodes that have information) at time t. Decay of C(t) can be exponential, polynomial or logarithmic (with a very large expected extinction for large graphs), and this depends on whether the system is operating above the threshold, at the threshold or below the threshold [11]. Let us focus on the fast extinction case where C(t) decays exponentially. The problem is stated in [21] as follows. Definition 1 Fast extinction is the configuration in which the number of carriers C(t) decays exponentially over time (C(t) ∝ c−t , c > 1). From the previous definitions we can state the problem as follows: Problem: • Given: the network topology (link up probabilities) βij the retransmission rates ri , the resurrection rates γi and the death rates (δi i = 1 . . . N , j = 1 . . . N ) • Find the condition under which a information message (or virus) will suffer fast extinction.

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To simplify the problem and to avoid dependencies on starting conditions, we consider the case where all nodes are initially in the have info state. Given that it is very hard to obtain, under the markovian approach, a closed form for the calculation of the fast extinction threshold, the authors of [21] proposed to obtain an approximation of the threshold by describing the problem as non-linear dynamic system with N variables representing the nodes and assumed that the state of two different nodes are independent. Taking into account the description of variables made in (1) this independence condition can be formally expressed ad follows: ζi (t) =

N  (1 − rj βji pj (t − 1))

(5)

j=1

The meaning of the variables involved in the equations are defined in Table 1. Then equations describing the state transitions in the dynamic systems for each node, taking into account what is depicted in the Fig. 2, can be expressed as pi (t) = pi (t − 1)(1 − δi ) + qi (t − 1)(1 − ζi (t))

(6)

qi (t) = qi (t − 1)(ζi (t) − δi ) + (1 − pi (t − 1) − qi (t − 1))γi

(7)

3.1 Fast Extinction Result In [21], the authors obtained through simulations based on real networks, experimental results consistent with the theoretical predictions of rapid extinction. The authors also comment that the accuracy of their predictions is related to the mixing properties of real networks. In order to expose more clearly the ideas behind the results on the fast extinction condition I will take some definitions and theoretical results presented in [21,23] and to show how some of them are obtained because our own results will be obtained by applying the same procedures. Definition 2 Define S to be the N × N system matrix:  if i = j 1 − δi Sij = i otherwise rj βji γiγ+δ i C(t) = Let |λ1,S | be the magnitude of the largest eigenvalue and  expected number of carriers at t of the dynamical system.

N

i=1 pi (t)

the

Theorem 1 (Condition for fast extinction) Define s = |λ1,S | to be the survivability score for the system. If s = |λ1,S | < 1, then we have fast extinction in the dynamical system, that is,  C(t) decays exponentially quickly over time.

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Table 1 Parameters of Chakrabarti SIS mathematical model Inteaction/transition parameters

Meaning

N

Total number of network nodes

βij

linki → j probability of beeing up

δi

Transition probability of node i to dead state

γi

Transition probability of node i to up state

ri

Probability that node i broadcasts information

ζi

Probability that node i doesnot receive info from

Node states

Description

pi (t)

Probability that node

qi (t)

Probability that node

1 − pi (t) − qi (t)

Probability that node i is dead

Dynamic system parameters

Description

p(t), q(t)

Probability column vectors

any of its neighbors at time t

i is alive at time t and has info i is alive at time t but without info

f : R2N → R2N

Function representing a dynamical system

∇(f )

The Jacobian matrix of f (.)

S

The N × N system matrix

λS

An eigenvalue of the S matrix

λ1,S

The largest in magnitude eigenvalue of the S matrix

s = |λ1,S |

Survivability score = Magnitude of λ1,S

Where |λi,S | is the magnitude of the largest eigenvalue of S, being S an N × N i otherwise, and system matrix defined as Sij = 1 − δi if i = j and Sij = rj βji γiγ+δ i N being  C(t) = i=1 pi (t) the expected number of carriers at time t of the dynamical system. Two additional results that appears in [21] are the following Corollary 1 (Condition for fast extinction homogeneous case for Chakrabati SIS model) If δi = δ, ri = r, γi = γ , for all i, and B = [βij ] is a symmetric binary matrix (links are undirected, and are always up or always down), then the condition for fast extinction is δ(γγ+δ) λ1,B < 1. i Lemma 1 Fixed point. The values (pi (t) = 0, qi (t) = γiγ+δ ) for all nodes i, are a i fixed point of the Eqs. (6) and (7). Proved by a simple application of the Equations.

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Theorem 2 (Stability of the fixed point) The fixed point point of Lemma 1 is asymptotically if the system is bellow threshold, that is, s = |λ1,S | < 1 Lemma 2 (From reference [24] of [21]) Define ∇(f ) (also called the Jacobian matrix) to be a 2N × 2N matrix such that [∇(f )]ij =

∂fi (v(t − 1)) ∂vj (t − 1)

(8)

where v is the concatenation of p and q. Then, if the largest eigenvalue (in magnitude) of ∇(f ) at vf (vector v valued at the fixed point) is less than 1 in magnitude, the system is asymptotically stable at vf . Also, if f is linear and the condition holds, then the dynamical system will exponentially tend to the fixed point irrespective of initial state. In [21] the authors apply (2) and obtain the following block matrix 

∇(f )|vf

S |0 = S1 | S2



The dimensions of each block matrix are N × N whose elements are  if i = j 1 − δi Sij = i otherwise. rj βji γiγ+δ i

(9)

(10)

The others are  S1ij =

1 − δi i −rj βji γiγ+δ i

if i = j otherwise

(11)

1 − γi − δi 0

if i = j otherwise

(12)

and  S2ij =

So the question is how can be obtained the fixed point of the system?. In the following paragraph we will sketch, in an alternative way of the used in [21], how it can be done. In dynamical systems theory the fixed point is called equilibrium point of the system. In this very point the state probabilities become stable, then pi (t) = pi (t − 1) and qi (t) = qi (t − 1). Then simplifying the notation by dropping the subindex and the time parameter we can state the following equations system: p = p · (1 − δ) + q · (1 − ζ ) q = q · (ζ − δ) + (1 − p − q) · γ

(13)

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after algebraic simplification it can be obtained the following equations system −δ · p + (1 − ζ ) · q = 0 γ · p + (ζ − 1 − δ − γ ) · q = q Expressing the equations system in matrix form we get      −δ 1 − ζ p 0 = −γ ζ − 1 − δ − γ q −γ

(14)

(15)

Solving by Cramer’s method we obtain p= q=

γ ·(1−ζ ) γ ·(1−ζ )−δ·(ζ −1−δ−γ ) δ·γ γ ·(1−ζ )−δ·(ζ −1−δ−γ )

(16)

The expressions (16) can be rewritten as p=0 γ q = δ+γ

(17)

For more details on the simplification see [21–23].

3.2 SIR Variation of Chakrabarti Model In order to compare the behavioral differences between a SIR and a SIS model, in this section of the article I will present an adaptation to a SIR model based on the Chakrabarti SIS model presented in [21] the modification in the transition diagram are shown in Fig. 3. The meaning of the variables involved in the equations are defined in Table 1. I introduced a state A which is an isolation state that can be taken as transition state before being recovered and the state R of being recovered and with immunity. Then equations describing the state transitions in the dynamic systems for each node, taking into account what is depicted in the Fig. 3, can be expressed as pi (t) = pi (t − 1)(1 − δi ) + qi (t − 1)(1 − ζi (t))

(18)

qi (t) = qi (t − 1)(ζi (t))

(19)

ai (t) = ai (t − 1)(1 − γi )

(20)

ri (t) = ai (t − 1)γi

(21)

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Fig. 3 Chakrabarti SIR model

3.3 Simulations of Chakrabarti SIS and SIR Model These simulations correspond to implementations of Chakrabarti’s SIS model based on his article [21] as well as a SIR variation of the same model in order to compare the differences in their behavior. Concerning the Chakrabarti SIS model presented it should be pointed out that for simplicity my MATLAB implementation assumes that the values of βi , γi ans δi are the same for all the nodes and we apply the fast extinction Theorem 1 of the present paper. Based on the results obtained in the section “Fast Extinction Result", in particular the Theorem 1 we know that the condition of fast extinction depends on the absolute value of largest eigenvalue |λ1,S | of the matrix S associated with the dynamic system as well as on the parameters δ, γ and β. The eigenvalue |λ1,S | is related with the biggest eigenvalue of the graph which is called the spectral radius of the graph associated to the network [25], which is also related with some interesting phenomena on random graphs that appear in the field of Complex Systems as for instance the small world property of a graph as well as percollation phenomena [1]. Because of that we run the virus propagation models simulations on the following kind of graphs: Lattice4, Powerlaw, Binomial and Exponential. To be clearer, I’ll define these different types of graphs below. Definition 3 Power law or scale-free degree distribution graph Is a graph whose degree distribution of nodes follows asymptotically a power law. More formally let P(k) the fraction of the total number of nodes in a given graph that have k connections

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with other nodes. This fraction of nodes have the following behaviour P(k)  k −γ

(22)

where γ is a parameter in the interval 2 < γ < 3.

Definition 4 Binomial degree distribution graph (Erdós-Rényi model, BarabasiAlbert model, etc.) Is a random graph whose degree distribution of nodes follows a binomial probability distribution law of degrees k that can be formally defined as follows. Each of the n nodes of the graph is independently connected with other node with probability p or not connected with probability (1 − p). Let P(k) the fraction of the total number of nodes in a given graph that have k connections with other nodes. This fraction of nodes have the following behaviour  n−1 k P(k) = p (1 − p)n−1−k (23) k Definition 5 Exponential degree distribution graph Is a random graph whose degree distribution of nodes follows a binomial probability distribution law of degrees k that can be formally defined as follows. Let P(k) the fraction of the total number of nodes in a given graph that have k connections with other nodes. This fraction of nodes have the following behaviour  P(k, λ) =

λe−λk k ≥ 0 0 k 0 is a parameter of the distribution called rate parameter. Definition 6 Lattice 4 connected graph (grid graph, mesh graph, etc.) Is a graph that each node is connected to four other nodes for all the n nodes belonging to the graph. This graphs have different degree distribution. By means of the simulations I will illustrate the impact that the graphs topologies have in the propagation of a virus and how the combination of the model parameters γ , δ and of largest eigenvalue λ1,B of the dynamic system matrix B determine the behaviour of a virus propagation process in a network. In Fig. 4 it can be seen the condition |λ1,S | of Theorem 1 for fast extinction is not accomplished given that the value of |λ1,S | then |λ1,S | > 1. In Fig. 5 it can be seen the condition |λ1,S | of Theorem 1 for fast extinction is not accomplished given that the value of |λ1,S | of the dynamic system is affected by the√eigenvalue of the Powerlaw distribution that can be of the order of (1 + o(1)) (m) where m is the maximum degree of a node in this kind of graphs [26]. As a consequence of that |λ1,S | > 1.

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Fig. 4 Simulation of SIS Chakrabarti model on a powerlaw 50 nodes graph showing non fast extinction δ = 0.04, γ = 0.5, β = 0.4 Initially Infected nodes 5

Fig. 5 Simulation of SIS Chakrabarti model on a Powerlaw 100 nodes graph showing non fast extinction γ = 1, δ = 0.1, Initially Infected nodes 50

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Fig. 6 Simulation of SIS Chakrabarti model on a Exponential 100 nodes graph showing non-fast extinction γ = 1, δ = 0.1, β = 0.8, Initially Infected nodes 10

In Fig. 6 it can be seen the condition |λ1,S | of Theorem 1 for fast extinction is not accomplished given that the biggest eigenvalue√of an Exponential distribution degree graph can be of the order of (1 + o(1)) max({ ()}, np) where  is the maximum degree of a node, n is the number of nodes and p the probability. This result has been shown in [27] for any value of p in a random graph. Then we have that |λ1,S | > 1. In Fig. 7 it can be noticed that the fast extinction condition is again violated for the same reasons explained in the case illustrated in Fig. 6. In Fig. 8 it can be noticed that we meet the fast extinction condition stated in Theorem 1. The biggest eigenvalue of a lattice is of the order of the degree of the nodes in the lattice [28] and has an impact in the biggest eigenvalue |λ1,S | < 1 of the dynamic system. In that case we have that |λ1,S | < 1. In Fig. 9 it can be noticed that we meet the fast extinction condition stated in Theorem 1. The biggest eigenvalue of a Powerlaw degree distribution can have an impact in the biggest eigenvalue of |λ1,S | but given that it is a SIR model, the recovered nodes become immune and because of that the number of carriers descend as time pass. In Fig. 10 the case is very similar to the shown in Fig. 9. Under the same parameter values independently of type of graph the fast extinction is meet. The same is true for the Fig. 11 as well as for the Fig. 12. The Fig. 13 shows that the rate at which extinction occurs is lower and is dictated by the values of the parameters δ, γ and β rather than by the type of graph.

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Fig. 7 Simulation of SIS Chakrabarti model on a Binomial 100 nodes graph showing non fast extinction δ = 0.6, γ = 0.3, β = 0.3 Initially Infected nodes 50

Fig. 8 Simulation of SIS Chakrabarti model on a Lattice4 100 nodes graph showing fast extinction δ = 0.6, γ = 1, β = 0.3 Initially Infected nodes 50

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Fig.9 Simulation of SIR Chakrabarti model on a powerlaw 100 nodes graph showing fast extinction δ = 0.6, γ = 1, β = 0.3 Initially infected nodes 50

Fig. 10 Simulation of SIR Chakrabarti model on a Exponential 100 nodes graph showing fast extinction δ = 0.6, γ = 1, β = 0.3 Initially infected nodes 50

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Fig. 11 Simulation of SIR Chakrabarti model on a Binomial 100 nodes graph showing fast extinction δ = 0.6, γ = 1, β = 0.3 Initially infected nodes 50

Fig.12 Simulation of SIR Chakrabarti model on a Lattice4 100 nodes graph showing fast extinction δ = 0.6, γ = 1, β = 0.3 Initially infected nodes 50

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Fig. 13 Simulation of SIR Chakrabarti model on a Binomial 100 nodes graph showing slow extinction δ = 0.6, γ = 1, β = 0.3 Initially infected nodes 50

4 My Own Model The model described in section “Application of Models to Virus Spreading in Networks ” as well as the results described in the section “Fast Extinction Result” help to estimate the conditions under which the fast extinction of a virus happen in a network. This condition is called a threshold. If the network is working under this threshold it can be assured that the virus will disappear very fast from the network. In order to achieve this situation some nodes have to be temporarily disconnected and repaired. This isolation method is very efficient for containing the diffusion of the virus because in this way the number of carriers decay exponentially. In my own model I suppose that not only a virus message is being spread, but also a kind of message that I called warning, that in the best case can be an antidote, and that helps to prevent a node about the propagation of a virus message and take some security measures to avoid the infection [20]. I will describe in the following subsections the fast extinction results under my own model. The variables of the model are listed in the next Table. Each node can be in one of four states: Infected,Warn Info, No Info or Dead, with transitions between them as shown in Fig. 14. The next graph represent the transitions that take place in each node for my own model (Table 2).

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Table 2 Parameters of my own SIRS mathematical model Inteaction/transition parameters

Meaning

N

Total number of network nodes

βij

linki → j probability of beeing up

δi

Transition probability of node i to dead state

γi

Transition probability of node i to up state

ri

Probability that node i broadcasts information

ζi

Probability that node i doesnot receive info from any of its neighbors at time t

νi

Probability that node i receive virus

1 − νi

Probability that node i receive a warning

χi

Probability that node i applies vaccin

Node states

Description

pi (t)

Probability that node

qi (t)

Probability that node has no Info

1 − pi (t) − qi (t) − wi (t)

Probability that node i is dead

wi (t)

Probability that node has warning Info

Dynamic system parameters

Description

p(t), q(t)

Probability column vectors

f : R2N → R2N

Function representing a dynamical system

∇(f )

The Jacobian matrix of f (.)

S

The N × N system matrix

λS

An eigenvalue of the S matrix

λ1,S

The largest in magnitude

i is infected at time t and has virus info i is healthy at time t but susceptible

i is warned at time t

eigenvalue of the S matrix s = |λ1,S |

Survivability score = Magnitude of λ1,S

4.1 Fast Extinction Result If we make the same assumption about the independence of the transitions within each node that was established in the Eq. (5) and taking into account the new states and transition probabilities shown in the Fig. 14, the Eqs. (6) and (7) as well as the new equation corresponding to wi the new model equations can be expressed as follows: pi (t) = pi (t − 1)(1 − δi ) + qi (t − 1)(1 − ζi (t))νi

(25)

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Fig. 14 My own SIRS model

qi (t) = qi (t − 1)(ζi (t) − δi ) + (1 − pi (t − 1) − qi (t − 1) − wi (t − 1))γi +χi wi (t − 1)

(26)

wi (t) = (1 − ζi (t))(1 − νi )qi (t − 1) +(1 − χi − δi )wi (t − 1)

(27)

In this case again we are faced with a system whose nodes have four states inside them that they pass through, which leads us to the fact that the system would have 4 n possible configurations and this would greatly complicate the analysis using a Markovian approach. For this reason it is more appropriate to make use of an approach based on non-linear dynamic systems and fixed point stability theorems. That is why I describe the dynamic system by means of the Eqs. (25–27). Following [29] I will calculate the fixed point of the system. As we have stated before, in these very points the state probabilities become stable, then pi (t) = pi (t − 1), qi (t) = qi (t − 1) and wi (t) = wi (t − 1). Using (25), (26) and (27), we can state the following result Lemma 3 pi (t) + qi (t) + wi (t) →

γi γi +δi

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Proof In the same way that has been done in [21] we can do the subtraction 1 − pi (t) − qi (t) − wi (t) and simplify by renaming xi (t) = pi (t) + qi (t) + wi (t) what give us the following linear system xi (t) = (1 − δi − γi ) · xi (t − 1) + γi

(28)

In the fixed point xi (t) = xi (t − 1) so if we apply this to the last equation we have that γi (29) xi (t) = γi + δi i that is, pi (t) + qi (t) + wi (t) = γiγ+δ . Then by Lemma 2 in [21], this convergence is i exponential. The other way to demonstrate this result is adding the Eqs. 25–27 and subtracting as follows

1 − pi (t) − qi (t) − wi (t) = 1 − pi (t − 1) + δi pi (t − 1) − qi (t − 1) +pi (t − 1)νi ζi − ζi qi (t − 1) +δi qi (t − 1) − γi + γi pi (t − 1) +γi qi (t − 1) + γi wi (t − 1) − χi wi (t − 1)

(30)

Applying algebraic simplifications in Expression 30 and putting in terms of Expression 29 we get the following dynamic system xi (t) = γi + (1 − δi − γi )xi (t − 1)

(31)

By applying the Lemma 2 to the dynamic system (31) we know that it converges to following fix-point xi (t) =

γi γi + δi

(32)

and by expression 29 we can conclude that pi (t) + qi (t) + wi (t) =

γi γi + δi

(33)

It is interesting to note that the fixed point can also be calculated, considering the linear behavior of the system at these points. Using (25), (26) and (27) and for simplicity dropping the indexes and the time dependence, we obtain the equations −δ · p + ν(1 − ζ ) · q = 0 (−1 + ζ − δ − γ )q + (−γ + χ )w = −γ (1 − ζ − ν + ζ ν)q + (1 − χ − δ)w = 0

(34)

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This linear system can be solved using Cramer’s method γ ·(−1+ζ )ν(δ+χ) p = − (γ +δ) δ+δ ( 2 −δζ +δχ+νχ−ζ νχ )

q=

γ δ(δ+χ) (γ +δ)(δ+δ 2 −δζ +δχ+νχ−ζ νχ )

w=

γ δ(1−ζ −ν+ζ ν) (γ +δ)(δ+δ 2 −δζ +δχ+νχ−ζ νχ )

(35)

The expressions 35 can be simplified and we obtain the following equations (for details see [22]) p=0 q=

γ γ +δ

(36)

w=0

4.2 Fast Extinction of My Own Model To obtain the condition of rapid extinction I will proceed as in [21]. Firstly. They are first defined the column vectors p(t) = (p1 (t), p2 (t), . . . , pN (t)), q(t) = (q1 (t), q2 (t), . . . , qN (t)) and w(t) = (w1 (t), w2 (t), . . . , wN (t)). Let the vector v(t) = (p(t), q(t), w(t)) be the concatenation of the previous vectors and let vf (t) be the vector v(t) evaluated at the fixed point. The whole system can then be described by the following equations v(t) = f(v(t − 1)) (37) where ⎧ pi (t − 1)(1 − δi ) if i ≤ N ⎪ ⎪ ⎪ ⎪ +qi (t − 1)(1 − ζi (t))νi ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ qi (t − 1)(ζi (t) − δi ) fi (v(t − 1)) = +(1 − pi (t − 1) − qi (t − 1) if N < i ≤ 2N ⎪ ⎪ ⎪ −wi (t − 1))γi + χi wi (t − 1) ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ (1 − ζi (t))(1 − νi )qi (t − 1) ⎪ ⎪ ⎩ if 2N < i ≤ 3N +(1 − χi − δi )wi (t − 1)

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Now using the techniques described in [29], let us define the the Jacobian matrix of the system, ∇f as ∂fi (v(t − 1)) [∇f]ij = . ∂v(t − 1) In order to explore the asymptotic stability of fixed points and according to [29] we will have to take into account the value of the function ∇f in these points In our case, we obtain the 3N × 3N Jacobian matrix ⎡ ⎤ S |0 |0 (38) ∇(f)|vf = ⎣ S1 | S2 | S3 ⎦ S4 | 0 | S6 where each block S, S1 , . . . is a N × N matrix whose elements are given by  if i = j 1 − δi Sij = i otherwise rj βji νi γiγ+δ i

(39)

and the others are  S1ij =  S2ij =

−γi i −rj βji γiγ+δ i

if i = j otherwise

(40)

1 − γi − δi 0

if i = j otherwise

(41)

 S3ij =  S4ij =  S6ij =

−γi + χi 0

if i = j otherwise

(42)

0 i )γi rj βji (1−ν γi +δi

if i = j otherwise

(43)

1 − χi − δi 0

if i = j otherwise.

(44)

Based on the Jacobian matrix that we have just calculated we can extend the results of Lemma 2. That is, if the largest eigenvalue (in magnitude) is less than one then it is assured that the system is asymptotically stable in the fixed point v and the dynamical system will exponentially tend to the fixed point whatever was the initial state. Those interested in the detailed proof of this Lemma 2 can consult the appendix of [21]. Now the fast extinction condition can be stated in the following corollary.

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Fig. 15 Simulation of my model on a powerlaw 100 node graph showing non-fast extinction δ = 0.6, γ = 1, β = 0.3, χ = 1, (1 − ν) = 1 Initially infected nodes 50

Corollary 2 (Condition for fast extinction homogeneous case for my model) If δi = δ, ri = r, γi = γ , χi = χ , νi = ν, for all i, and B = [βij ] is a symmetric binary matrix (links are undirected, and are always up or always down), then the condition for fast extinction is δ(γγ+δ) λ1,B < 1.

4.3 Simulations of My Model These simulations correspond to implementations of my SIRS model inspired in the Chakrabarti SIS model described in section “Application of Models to Virus Spreading in Networks” of the present article. Concerning my model it should be pointed out that for simplicity my MATLAB implementation assumes that the parameters γi , δi , βi , χi and νi are the same for all the nodes in the network. In this virus propagation model it was introduced the possibility to propagate not only a virus message but also other kind of message called in [22] warnings. The additional type of message of this model was to give place to the possibility of social distribution of antidotes that was treated in [20]. I should also mention that my own model is in some sense a generalization of the Chakrabarti SIS model because it behaves as a SIS model as well as a SIR model depending on the parameter values given to my model as will be explained in this subsection. In Figs. 15 and 16 it can be seen the condition |λ1,S | of Theorem 1 for fast extinction is not achieved given that the value of |λ1,S | of the dynamic system is affected by the eigenvalue of the Powerlaw distribution that can be of the order of (1 +

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Fig. 16 Simulation of my model on a powerlaw 100 node graph showing non-fast extinction with probability of antidote equal to 0 δ = 0, γ = 1, β = 0.0, χ = 1, (1 − ν) = 0.1 Initially infected nodes 50

√ o(1)) (m) where m is the maximum degree of a node in this kind of graphs [26]. As a consequence of that |λ1,S | > 1. It should be pointed out that the Fig. 16 has the same behaviour of the SIS Chackrabarti model 4. If we observe the Fig. 17 the behaviour of my model running in an Exponential degree distribution graph the fast extinction λ1,S < 1 was not fulfilled given that the topology of the network has more impact in the value of the biggest eigenvalue of the dynamic system than the values of the node transition parameters of the model. The final result is that 40% of the nodes stay infected along the time. From the Fig. 18 it can be seen that the behaviour of my model running in an Exponential degree distribution graph fulfils the fast extinction λ1,S < 1 because the impact of the topology of the network on the value of the biggest eigenvalue of the dynamic system combined with effect of the values of the node transition parameters of the model produce this behaviour. The explanation of why my model achieves achieves the fast extinction condition in the case shown in Fig. 19 is similar to the one given in the case of Fig. 18. The reasons about the behaviour of my model that is shown in Fig. 20 are the same as those given for the case of the Figs. 19 and 18. If we look at Graphs 20, 19 and Fig. 18 it is important to note that if the efficiency of the vaccine is χ = 1 the behaviour of my model is the fulfilment of the condition of fast extinction. Observing the behaviour of may model in Fig. 21 and the Chakrabarti SIS model of Fig. 22 for the same parameter values and the same graph topology is the same. The my model works like a SIS model when vaccine effect is χ = 0 and the probability of becoming isolated is δ = 0.

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Fig. 17 Simulation of my model on a Exponential 100 nodes graph showing non-fast extinction δ = 0.6, γ = 1, β = 0.3, χ = 1, (1 − ν) = 0.1 Initially infected nodes 50

Fig. 18 Simulation of my model on a Exponential 100 nodes graph showing fast extinction δ = 0.6, γ = 0.3, β = 0.3, χ = 1, (1 − ν) = 0.1 Initially infected nodes 50

Finally if we look the Fig. 23 it can be noticed that when the probability of becoming isolated δ = 0.1 and the vaccine effect is χ = 0 in my model the number of carriers diminish slowly along the time but never get extinct.

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Fig. 19 Simulation of my model on a Binomial 100 nodes graph showing fast extinction δ = 0.6, γ = 0.3, β = 0.3, χ = 1, (1 − ν) = 0.1 Initially infected nodes 50

Fig. 20 Simulation of my model on a Lattice4 100 nodes graph showing fast extinction δ = 0.6, γ = 0.3, β = 0.3, χ = 1, (1 − ν) = 0.1 Initially infected nodes 50

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Fig. 21 Simulation of my model on a Lattice4 100 nodes graph showing non-fast extinction δ = 0, γ = 1, β = 0.6, χ = 0, (1 − ν) = 0.1 Initially infected nodes 50

Fig. 22 Simulation of SIS Chakrabarti model on a Lattice4 100 nodes graph showing non-fast extinction δ = 0, γ = 1, β = 0.6 Initially infected nodes 50

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Fig. 23 Simulation of my model on a Binomial 100 nodes graph showing fast extinction δ = 0.1, γ = 1, β = 0.6, χ = 0, (1 − ν) = 0.1 Initially infected nodes 50

5 The Different Impact of Isolation Strategies Many of the characteristics of this new respiratory disease such as how it can be spread, the size of the virus, the time it remains in the air or the fact that a large proportion of the people infected is asymptomatic has introduced a great deal of uncertainty into the information needed for appropriate and timely decision-making by governments. In addition, those of the non-asymptomatic infected can achieve a degree of the disease that endangers their lives. Combining these elements of the disease and taking into account the low availability of adequate equipment to attend emergencies or specialist physicians to care for critical patients, as well as the absence of a vaccine, the likelihood of collapse of health systems is very high. Another aspect of the disease that further complicates the picture is the presence of pre-existing chronic diseases in the population such as age, gender, diabetes, obesity, high blood pressure or immunodeficiency. Under such circumstances decision-making is a very complicated task and the effects of such decisions can be successful or disastrous. Decisions made in different parts of the world have been varied and so have the results. Some governments chose not to isolate the population and wait for what is known epidemiology to emerge as herd immunity. As soon as I know, at least two countries opted for this possibility that was England and Sweden. In the case of Sweden the results apparently were not as bad but the cost in elderly deaths was high. In England, however, the result was so bad in terms of contagion and deaths that they subsequently had to impose mandatory isolation measures. As for herd immunity, it should be mentioned that for it would have to be infected at least 70%

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of the population in order to become immune. On the other hand, very little is known at the moment about the degree of immunity it generates and for how long. Other countries as for instance, Sud Corea and Japan the strategy was to detect at the beginning of the epidemic the infected and their direct contacts and isolate them. In the case of Sud Corea the detection was made by massive testing of suspect and if they were positive then quarantined them and their direct contacts. In the case of Japan the detection of suspects was made by teams of trackers who were medical staff from small local clinics. In the case of Sud Corea measures of obligatory social distance were applied while in Japan the measures of social distance were less strict than in Sud Corea. The effect of the strategy opted in both cases has given good results. The measures taken by the Chinese government were the imposition of strict isolation of entire cities such as Wuhan, the treatment of suspects who were sent to hospitals for laboratory testing and kept them in hospital until they had the test results. In addition, they built very effectively large hospitals in record time for the specific attention of covid-19. In some european countries such as Italy, Spain or France, it was also imposed the closure of cities and social isolation combined with a system of fines to those who did not respect the measures. The result of the strategy adopted in these countries was a large number of infected and dead. This terrible result was perhaps due to the long-running isolation measures and given the presence of asymptomatic infected the spread was very rapid. As we can see the same strategy of containment of the epidemic can yield very different results in different countries. However, we can also note that, in the absence of vaccines or treatments, the application of isolation measures are necessary and its effect is positive if applied in a timely manner. When we are applying isolation measures we are removing from the network some nodes and edges. This actions modify the largest value of the dynamic system. This necessarily influences the dynamics of the spread of the virus and therefore its extinction or permanence as explained in the sections “Application of Models to Virus Spreading in Networks” and “My Own Model” of this article. Given that the biggest eigenvalue of the adjacency matrix of a graph represent the spectral radius of the graph then to obtain the optimal isolation strategy can be related with the problem of minimization of the spectral radius of a graph. This problem was demonstrated to be NP-hard in [30]. Because of that, it is very hard to find the optimal isolation strategy. The authors of [30] state the problem of minimization of the spectral radius of a graph by removing m links of a graph and by reducing this problem to the Hamiltonian path problem that is a well known NP-complete problem they demonstrated the NP hardness of the spectral radius minimization problem. They also explored in this same paper four strategies to approximate the optimal solution to this problem.

5.1 Adaptation of My Own Model to the COVID-19 Pandemic In this section I will describe how my model can be adapted to model some aspects of the covid-19 pandemic. It is important to mention that the most simple classic models of mathematical epidemiology exposed in the section “Formulation of Two Well

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Known Epidemiology Models” that are known as homogeneous standard models assume that the number of contacts of each individual is the same which would result in a regular network that is a particular case of the type of contact networks both of the Chakrabarti model exposed in the section “Application of Models to Virus Spreading in Networks” and that of my model exposed in the section “My Own Model”. Due to the fact that in the field of study of complex systems it has been observed that interconnection structures that are formed in networks such as the Internet, or on social networks such as Twitter or Facebook, follow node degree distribution laws of the type of powerlaw, coupled with the fact that such structures are a copy of the interconnection structures that we usually build socially , it is possible to say that models such as Chakrabarti or mine incorporate an additional element of reality that does not include the classical homogeneous standard models described in the section “Formulation of Two Well Known Epidemiology Models”. The inclusion of the interconnection network in mathematical models of virus propagation allows us to realize the importance they have in the behavior of the phenomenon of spreading a virus and thus understand the importance of isolation strategies to be able to contain it efficiently and even be able to predict efficient immunization strategies when having a vaccine or antidote. In the absence of a vaccine to immunize the population against covid-19 virus, isolation measures that allow the exponential increase in the number of contagions to be contained are necessary to prevent the collapse of the country’s health services. As was mentioned in the section “The Different Impact of Isolation Strategies” many measures for containing the spread of a virus have been taken with many different results. The goodness of the strategies adopted depend on the time they are taken and the quality of the information available. One thing that can support decisionmaking in pandemic situations is to have mature mathematical models that allow us to understand the phenomenon of virus spread as well as reliable data with which to feed the models in order to ensure the precision of the estimates produced by these models. In my model the parameter δ can be used as way to take into account different levels of rigour in isolation policy followed by the society in a pandemic scenario. Aspects of an epidemic such as the effectiveness of a vaccine or some treatment can be incorporated into the model’s processing using the χ parameter of my model. The ν parameter of my model can be used to introduce mechanisms of socially distributed immunization.

6 Conclusions and Future Work In section “Application of Models to Virus Spreading in Networks” as well as in section “My Own Model” it was demonstrated the fast extinction conditions and their respective stability properties. From these results we were able to convince ourselves of the importance of network topology and the values of the transition parameters between node states in the largest eigenvalue of the associated dynamic

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system matrix to meet the condition of rapid extinction. An aspect that of the covid-19 pandemic that is not included in my model, but has a big impact, is the demographic structure of the population. It was observed the evolution of the covid-19 pandemic all around the world and pointed out that the probability of becoming infected,as well as the dead rate and the recover rate are strongly related with the age, gender and the prevalence of chronic diseases of the patients. I will include these important pieces of demographic information of the covid-19 epidemic in future versions of my model and in the corresponding simulations. In the future, I will also try to address the problem of optimizing isolation strategies. The main messages of this article are • In a globally interconnected world the spread of disease can easily get out of control. • The interconnection structure of individuals is important in how quickly a virus spreads in that network. • Knowledge of the connectivity characteristics of the graphs associated with said networks can serve as the basis for proposing modifications of said networks that can be translated into strategies that make it possible to reduce infections in the event of virus spread and eventually their extinction. • Interdisciplinary work between epidemiologists, computer scientists and experts in complex networks can help solve problems of virus spread.

7 My Opinion About Future of My Field 30 Years Later Given the speed of knowledge production in computer science as well as the area of complex networks, it is a bit difficult to foresee the direction that these scientific fields will take. However I will try to do a forward-thinking exercise based on some developments in these areas. For many of the interesting problems to solve with a computer, when posed in their general form, they do not have efficient algorithmic solutions for arbitrarily large instances. Such is the case of the Hamiltonian cycle problem mentioned in the isolation strategies section discussed in this article. One possibility in the short term is to develop efficient approximate algorithms for this problem that provide an approximation to the optimal solution with a certain guarantee of it, that is, for example, that always return a solution that deviates from the optimal by no more than a certain factor deviation.

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Core Messages

• In a globally interconnected world, the spread of disease can easily get out of control. • The interconnection structure of individuals is important in how quickly a virus spreads in that network. • Knowledge of the connectivity characteristics of the graphs associated with said networks can serve as the basis for proposing modifications of said networks that can be translated into strategies that make it possible to reduce infections in the event of virus spread and eventually their extinction. • Interdisciplinary work between epidemiologists, computer scientists, and experts in complex networks can help solve problems of virus spread.

Acknowledgements I would like to thank the Universidad Auténoma Metropolitana Unidad Cuajimalpa for the support for the realization of this chapter.

References 1. Durrett R (2007) Random graph dynamics. In: Cambridge series in statistical and probabilistic mathematics. Cambridge University Press 2. Bernoulli D (1760) Esai d’une nouvelle analyse de la mortalité causeé par la petite vérole et des avantages de l’inoculation pour la prévenir, in Memoires de Mathématiques et de Physique, Académie Royale des Sciences, Paris, pp 1–45 3. Hamer WH (1906) Epidemic disease in England. Lancet 1:733–739 4. Ross R (1911) The prevention of malaria, 2nd edn. Murray, London 5. Bailey NTJ (1975) The mathematical theory of infectious diseases, 2nd edn. Hafner, New York 6. Dietz K (1967) Epidemics and rumours: a survey. J Roy Statist Soc Ser A 130:505–528 7. Dietz K (1988) The first epidemic model: a historical note on P. D. En’ko. Aust J Stat 30A:56–65 8. Kermack WO, McKendrick AG (1927) Contributions to the mathematical theory of epidemics, part 1. Proc Roy Soc Lond Ser A 115:700–721 9. McKendrick AG (1926) Applications of mathematics to medical problems. Proc Edinburgh Math Soc 44:98–130 10. Hethcote HW (2000) The mathematics of infectious diseases. SIAM Rev 42(4):599–653 11. Durrett R, Liu XF (1988) The contact process on a finite set. Ann Probab 16(3):1158–1173 12. Albert R, Barabási AL (2000) Error and attack tolerance of complex networks. Nature 406 13. Barabási AL, Albert R (1999) Emergence of scaling in random graphs. Science 286:509–512 14. Ben-Avraham D, Barabási AL, Schwartz N, Cohen R, Havlin S (2002) Percolation in directed scale-free networks. Phys Rev E 66(1):0151041–0151044 15. Benjamini I, Alon N, Stacey A (2004) Percolation on finite graphs and isoperimetric inequalities. Ann Probab 32(3A):1727–1745

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16. Pastor-Satorras R, Vespignani A (2001) Epidemic dynamics and endemic states in complex networks. Phys Rev E 63(2):0661171–0661178 17. Pastor-Satorras R, Vespignani A (2001) Epidemic spreading in scale-free networks. Phys Rev Lett 86(14):3200–3203 18. Pastor-Satorras R, Vespignani A (2002) Epidemic dynamics in finite size scale-free networks. Phys Rev E. 65:0351081–0351084 19. Kempe D, Kleinberg J (2002) Protocols and impossibility results for gossip-based communication mechanisms. In: Proceedings of the symposium on foundations of computer science (FOCS 2002) 20. Ganesh A, Borgs C, Chayes J, Saberi A (2010) How to distribute antidote to control epidemics. Random structures and algorithms, vol 37, no 2. John Wiley and Sons, Inc. New York, NY, USA, pp 204–222 21. Faloutsos C, Madden S, Guestrin C, Leskovec J, Chakrabarti D, Faloutsos M (2007) Information survival threshold in sensor and p2p networks. In: IEEE INFOCOM 2007 22. Rodríguez Lucatero C , Bernal Jaquez R (2011) Virus and warning spread in dynamical networks. Adv Complex Syst 23. Rodríguez Lucatero C (2018) Modelling and simulating a opinion diffusion on twitter using a multi-agent simulation of virus propagation. Springer Science and Business Media 24. Chakrabarti D, Wang Y, Wang C, Leskovec J , Faloutsos C (2008) Epidemic thresholds in real networks. ACM Trans Inf Syst Secur 10(4). Article 13 25. Van Mieghem P (2010) Graph spectra for complex networks. Cambridge University Press 26. Chung F, Lu L, Vu V (2003) Eigenvalues of random power law graphs. Ann Comb. Springer, 21–33 27. Krivelevich M, Sudakov B (2001) The largest eigenvalue of sparse random graphs. arXiv:math/0106066v1 28. Lovász L (2007) Eigenvalues of graphs. http://web.cs.elte.hu/lovasz/eigenvals-x.pdf 29. Hirsch M W, Smale S (1974) Differential equations, dynamical systems, and linear algebra. 2nd edn. Academic Press 30. Van Mieghem PP, Stevanovi’ c D, Kuipers F, Li C, van de Bovenkamp R, Liu D, Wang H (2011) Optimally decreasing the spectral radius of a graph by link removals. Phys Rev E 84:016101

Carlos Rodríguez Lucatero Biosketch Ph.D. He has a Ph.D. in Computer Science from the University of Paris VI (1994). He also has a DEA (Diplôme d’études approfondies) in Computational Sciences AI and recognition of forms, with a specialty in Robotics, by the University of Paris VI (1991). He has a degree in Computer Engineer from UNAM (Universidad Nacional Autónoma de México) (1987). He was a Full-time Research Professor at the Graduate and Research Division of the Instituto Tecnológico de Monterrey Campus Estado de México (1995– 1999). He was a Full-time Research Professor in the Computing department of the Instituto Tecnológico de Monterrey Campus Ciudad de México (1999–2005). On this campus he was a Fulltime Research Professorr in undergraduate courses of Computer Theory, Algorithm Analysis, Artificial Intelligence and Numerical Methods. At the postgraduate level he was a Full-time Research Professor of Computational Logic, Robot Movement Planning and Multi-Agent Systems. He was a Full-time Research Pro-

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fessor from the Department of Physics and Mathematics at the Universidad Iberoamericana A.C. Mexico City Campus (2005–2009), tenured professor in undergraduate courses in Calculus II, III and numerical analysis (1995–2005). It has a considerable number of publications in its area. He is currently a Full-time Research Professor “C” in the Department of Information Technologies of the Division of Communication Sciences and Design at UAM, Cuajimalpa Unit.

9

Optimal Control: Application and Applicability in Times of Pandemics Ilias Elmouki, Ling Zhong, Abdelilah Jraifi, and Aziz Darouichi

All that is not perfect down to the smallest detail is doomed to perish. Gustav Mahler

Summary

In times of pandemics, researchers hurry to find the best control policy to limit the spread of a disease and its consequences on the health care and economic sectors. To reach a reasonable control strategy, it is becoming more important than ever to rapidly detect the geographical extent of a global epidemic and/or infodemic and analyze all means of interconnections and intra-connections that exist and develop between and within different regions; otherwise, some would wonder whether the classical epidemiological methods are still really able to resist to the complications due to the continuous evolution of networks, or their development is urgently needed? In this context, spatiotemporal control systems would represent some of the suitable frameworks to start with to help in international health decision-making seriously. Based on the most recent research in control modeling of epidemics, this chapter answers the two questions: How to effectively apply control at a large geographical scale, and what applicable control is convincingly possible to follow in times of global epidemics crises? This chapter also reviews an example of the most recently I. Elmouki (&)
A. Jraifi Laboratory of Mathematics, Computer Science and Communication Systems (MISCOM), National School of Applied Sciences-Safi (ENSA-S), Safi, Morocco e-mail: [email protected]; jraifi[email protected] L. Zhong Department of Economics, Cheung Kong Graduate School of Business, Beijing, China e-mail: [email protected]; [email protected] A. Darouichi Department of Computer Science, FST, Cadi Ayyad University, Marrakesh, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_9

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developed model for the COVID-19 pandemic, which aims to guide the reopening strategies of an area with low domestic epidemic risk amid the danger of importing cases from other areas. After all, the chapter concludes that many problems could hinder the effect of different types of control regardless of any approach, as in the end and at times of race between viruses mutation or variation and vaccines research, there would be a need to prioritize a redesign of the health education systems before taking further steps in future. Graphical Abstract/Art Performance

Optimal control of an epidemic (Adapted with permission from the Health and Art (HEART), Universal Scientific Education and Research Network (USERN); Painting by Zahra Hassan Alhiki)

The code of this chapter is 01101111 01,110,100 01,101,110 01,101,100 01,110,010 01,000,011 01,101,111. Keywords













COVID-19 Epidemic Infodemic Multi-region modeling Optimal control Pandemic




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Introduction

1.1 General Context and Goal of the Chapter Unfortunately, many lives could be lost because of some pandemics as the world is still facing today the threat of the coronavirus disease 2019 (COVID-19) and its variants. When there is insufficient information about the initial time, source, and nature of a local or global epidemic, some may begin to believe that, during the early stages of clinical research, the only recourse would be some special mathematics to at least understand or take the initiative in predicting the spatiotemporal dynamics of the disease, as tried in the case of COVID-19 [1–4]. Classical modeling frameworks already investigated in many subjects in epidemiology, ecology, and immunology using compartmental systems as studied in books [5, 6], often helped to analyze the infection spread from one cell to cell at a microscopic level, or more generally from one host to another. The study of the epidemic dynamics in multi-connected groups or patches would reasonably be done only through metapopulation-like models as in [7–9]. However, there is still a disinclination to this kind of modeling despite the fact that they are more meaningful in analyzing pandemic situations at a large geographical scale. Once a model of that kind is validated, the optimization of anti-epidemic measures can be deduced by applying optimal control theory as done for the case of the metadata models in [10–12]. This chapter provides the techniques that can be used to develop the work done in [5–12]. Additionally, to avoid trouble in policies and prove the applicability of some of them, the chapter highlights the importance of the education-based strategies suggested for COVID-19 in [12–14] or theoretically as studied earlier in the case of any pandemic using results in [15–18]. In conclusion, we found there is a need in the nearest future to novel control differential-based systems modeling that can handle large datasets to help in international health decision-making seriously; otherwise, some research would become restricted to old and bounded statistical techniques which are, to the best of the authors’ knowledge, not advanced enough and ready to answer any questions in the present time of networked big data, even with the use of the new learning methods.

1.2 The Past, Present, and Future of Interventions In times of unknown pandemics, whose causes are frequently unknown [19], affected regions were forced to follow the simplest control policies accepted by the majority of people as the rational interventions to succeed directly in the war against an epidemic, without resorting to any complicated method that would require time, a whole scientific theory [20], and enough evidence behind. Talking about most cases of global infectious diseases, amongst the control approaches that were

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advised to respect, have been just cultural [21], such as reviving the idea of the importance of cleanliness that is always indispensable to achieve hygiene (personal cleanliness) and sanitation (public cleanliness) [22]. When an organism demonstrates the ability to transfer from one host to another, humans will automatically resort to isolating infectious agents with unmistakable symptoms. This has been a very old practice for centuries, and it was even applied to cases that are not that dangerous now [23]; then, later, its value has become limited with the concept of the immunization of population [24]. Some traditional measures were inspired by the advice of some savants in the far past, such as the use of vinegar as a disinfectant [25]. Benefits of other strategies such as quarantining infected individuals or groups were generally deduced from accumulative knowledge of historical epidemic events, past experiences, and economic circumstances of different regions [26, 27]. However, this cannot be that simple in this new world. No one can deny the exponential growth of the global population and the issues accompanying it. In fact, the more complex this environment is becoming, the more difficult the solutions would be available, especially when any sort of epidemic appears suddenly, without warning, while having the potential to spread rapidly in a large geographical area. The increasing number of a region intra-connections within itself via movements of its people and the interconnections with other regions via air travel, for instance, imposed to the scientific community not only to find the possibly classical anti-epidemic measures that would decrease infection fast but to necessarily seek the best strategy that has to take into account many factors and constraints of various types and of diverse locations to avoid more unpredictable outcomes. These are among the main objectives of novel optimal control approaches when they are applied to epidemic models and networks, and here, it can also be shown that this science of modeling and control is still not investigated enough for further development and practicability, especially when it comes to the subject of a global phenomenon. As the world is still facing COVID-19, this chapter focuses on studying the results of recent research about applying optimal control theory to this pandemic in particular. This work also aims to discuss the concrete applicability of such methods and propose alternative strategies to better understand the dynamics of new epidemics while introducing and suggesting the appropriate controls and technical steps.

1.3 Applied Control Models in Times of Pandemics The main idea behind the importance of optimal control when it is applied to epidemiology is that it helps to suggest the best strategy to fight against an infection subject to a particular dynamical system which is used to describe the spread of an epidemic from one state to another as noted in the next section through the mathematical Eqs. (1, 4) using the X vector, and/or from one area to another as

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noted in the same relations by the domain and subdomains Omega(s). For a very simple explanation of this general concept, we can describe the main goal that often concerns decreasing the number of infectives while increasing the number of recovered people and which are components of X, by an optimization problem as noted in Eqs. (2, 3, 5) with a pre-chosen objective function J of the state variables and the control function using vector u, see, for instance, the case of human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) controlled by awareness [16]. The dynamics of these states, which could be numbers of individuals or proportions of the classes considered in a population, are described using equations of discrete variations [28] as in Eq. (1) for times i = 0,…, N-1 or continuous variations [29] with respect to time and/or space as in Eqs. (4) for time t in [0, T] and regions j = 1,…, p, and that represent the main constraint to this optimization as defined in (2, 3), and as more the dynamics are complicated, as more the optimal control problem becomes difficult to solve as in (5, 6). As for the control function introduced to these dynamics, it concretely represents the chosen intervention to move the susceptibility and infectivity and/or recovery to the best controllable states after determining the optimal values of this control. For explicit mathematical and numerical details, we often recommend to researchers interested in developing optimal control problems for more difficult theoretical cases to start with the reference [30] as the authors were among the first to present accurate algorithms in this regard. Before going into further details of this theory, we note that in the case of COVID-19 and because of the absence of any treatment or vaccine, many researchers often referred to the control non-pharmaceutical interventions (NPIs) [31–45], and that showed their effectiveness to parallel pandemics such as influenza [46, 47] but if implemented sooner for better economic and health outcomes [48]. The studied approach we are dealing with here, as explained before, requires a clear modeling framework where the control could be incorporated. Always in the context of NPIs against the spread of COVID-19, Davies et al. in [49] used an age-structured transmission model whereby they found that such interventions are extremely important measures to reduce the number of deaths and demands on hospital beds. As for the optimal control now, Zamir et al. in [50] reached an optimal policy of COVID-19, and as a conclusion, they advised to keep the NPIs, or otherwise, the new outbreaks will automatically occur again. In another work, Ullah et al. [51] modeled the dynamics of COVID-19. Using an optimal control analysis, they studied the impact of NPIs. In order to avoid the worst scenario of the infection spreading, they proposed from their results to focus on social distancing and quarantine. Jasmina Panovska-Griffiths concluded in her work [52] that researchers need to devise and develop more models as it looks not obvious to get all answers using only one or a limited number of the current frameworks especially when we talk about the baffling situation caused by the COVID-19 epidemic.

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We think that the two infrequent factors that are still not studied and mathematically detailed enough in most epidemic differential systems concern firstly the modeling of the movement of individuals, and secondly, the modeling of inevitable perturbations that could hinder most strategies, rumors as a simple example. In this context, some recent development of modeling and optimal control methods have been proposed, as we will explain in the following section.

2

Novel Modeling and Control Approaches

2.1 Recent Contributions to the Spatiotemporal Control Modeling of Pandemics Many papers discussed the impact of human mobility on the COVID-19. For instance, Kraemer et al. showed [53] the usefulness of travel restriction in the early stage of the outbreak in China. Using a multiple linear regression model, Cartenì et al. [54] explained from a statistical perspective how human mobility habits have direct effects on the number of COVID-19 infections in Italy. In their analysis of the spatial–temporal potential exposure risk of COVID-19, Zhu et al. [55] even found that movement restrictions improve the air quality as a result of the reduction of industrial production, transportation, and traffic. In the last decades, some researchers have been more interested in modeling the spatial spread of pandemics using a simple form of equations inspired from the classical epidemic models such as the discrete [56–58] or continuous [56, 59] ones. Still, with the consideration of new factors such as the movement of infectives from one region to another, we can cite the multi-region continuous-time models applied to the case of Ebola in [17] for the description of travel in a large geographical area since there existed many interconnections that facilitated the spread of the disease between the three affected countries. A similar case was HIV/AIDS [18], as described by intra-connectivity within a region itself because of the human mobility inside the cities, towns, and neighborhoods. Arino and Van Den Driessche [60] earlier adopted a similar idea in this case of continuous-time as in [18] but considered all classes’ mobility, including the susceptible, the exposed, and the recovered population. Then, with the introduction of a discrete optimal control approach, Zakary et al. in [61] observed that particularly when aiming to target and control a region, the most important class that should be quickly controlled and that is more responsible for spreading a pandemic from one domain to another, concerned the population of infectives only, through their movements and, of course, in addition to their contacts with other categories. Discrete-time models would rather be preferred to continuous-time ones. In fact, the theoretical framework of these findings was based on simpler systems in the form of difference equations first devised in [62] and which were discrete as the authors respected the idea that data were usually collected in discrete times, as highlighted more also in [28].

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In summary, the general form of the first discrete-time multi-region epidemic models [61, 62] is
 XiXþ 1 ¼ XiX þ f XiX ; uX i ; i ¼ 0; . . .; N  1

ð1Þ

as the main constraint of the discrete optimal control problem, which takes this form N1      X  X X X X J uj j ¼ U N; XN j þ f 0 Xi j ; ui j

ð2Þ

i¼0

Analogously, with the use of the derivative instead of variation with respect to time, and the integral in place of the sum, the general form of the continuous optimal control problem, is  J

X uj j




 ¼ U T; X Xj ðTÞ þ

Z 0

T


 f0 t; X Xj ðtÞ; uXj ðtÞ dt

ð3Þ

subject to this form of continuous-time multi-region epidemic models [17, 18]. More research has benefited later from work done in references [61, 62] and applied their modeling and control techniques to other systems [11, 12, 63–69] using a new form of cellular simulations for epidemics and that showed more clearly the importance of the infection mobility in the spatial propagation of an epidemic in a large domain, as well as exhibiting the effectiveness of the optimal control which gave the best proportions of the mobility restriction of infected travelers only, as the authors supposed no other intervention because of the unavailability of any medical treatment or vaccination. In addition to travel, an important cause of the spatial spread of infection nowadays and that is often ignored has to do with rumors, and that can delay the effect of any control followed by the authorities concerned. In fact, with the new technologies and the presence of online social networks, it has become easier now for most people to create rumors that can spread faster at the same time as the epidemic. These epidemics of rumors are called infodemics. To the best of our knowledge, the first scientist who used this term more in the scientific community and warned about it was doctor Briand, director of Infectious Hazards Management at the world health organization (WHO)’s Health Emergencies Programme and architect of WHO’s strategy, with a similar opinion reported in [70]. Then, with the spread of COVID-19, researchers started to recognize more the problem of infode2mics and tried to suggest methods to fight against it [71–77]. Just on September 23, 2020, the WHO published their joint statement [78] with UN, UNICEF, UNDP, UNESCO, UNAIDS, ITU, UN Global Pulse, and IFRC and where it has been recommended to fight against misinformation and disinformation in order to manage the COVID-19 infodemic. As theoretically noted in [11] and in the concrete examples [79, 80] in the case of Ebola in Sierra Leone, the lack of trust could lead to fears and misperceptions, while some beliefs could be based on

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not-yet-proven scientific facts. Eliza Y. L. Cheung, who was deployed as IFRC psychosocial delegate to Liberia during the Ebola outbreak, reported in her paper [81] that understanding the culture and perception of a community about an outbreak it is facing is crucial before giving any health information or support, while unnecessary use of protection may create more confusion. For a general mathematical framework in the subject of the spatial spread of pandemics under perturbations and in addition to the deterministic results in [17], El Kihal et al. in [11] were the first to devise a new stochastic control model for the study of the effect of infodemics.

 dX X ðtÞ ¼ f t; X X ðtÞ; uX ðtÞ dt þ g t; X X ðtÞ; uX ðtÞ dW X ðtÞ; t 2 ½0; T

ð4Þ

while the associated form of the objective function in the stochastic continuous-time case is presented as  Z  
 Xj Xj J uj ¼ E U T; X ðTÞ þ

T




Xj

Xj



f0 t; X ðtÞ; u ðtÞ dt

 ð5Þ

0

With the novel stochastic optimal control approach incorporated, this model also highlighted the role of media in minimizing contacts between susceptibles and infectives without ignoring the importance of travel control. To the best of our knowledge, we think this theoretical design should be considered among the dynamical control systems of reference to many researchers to respond to some questions in [52] and many problems with complex networks. Just recently, some papers have started to talk about the importance of taking into account some stochasticity in COVID-19 dynamics, and that could represent a good background for further development of the theory around the mathematical modeling of global epidemics, unless there should be respect to the spatial aspect of pandemics as considered in [11] as well as applying optimal control theory to determine the best values of the anti-epidemic measures followed, to evaluate them. Among the first results of simple models that we have found in literature in this regard, there is [82] where the authors studied the general effects of the environment on the spread of COVID-19, such as precipitation, absolute humidity, and temperature as stated in a different model in [83]. In addition to all modeling and optimal control approaches discussed until this part, we present in the following section some alternative methods that join the same theory but with new significant development that would help to find better control policies against pandemics and a better description of their dynamics under different constraints and hypotheses.

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2.2 A Reopening Control Approach Amid COVID-19 Using a New Dynamic Model In this subsection, an example of a modeling tool is presented to guide public health policy design subject to precisely-described disease mechanisms. The example shows how one could use the standard compartmental model of epidemiology as the basis, modify the dynamics to adapt to the disease of interest, and then add government policy parameters to enable its utility in policy analysis. The example is first introduced in a recently published work [88]. The model guides the reopening strategy of an area with low domestic epidemic risk amid the danger of importing cases from other areas. The model setup includes multiple epicenters of infectious disease with heterogeneous levels of epidemic risk. It highlights the impact of government initiative in the disease containment process when parts of the spreading, e.g., the severity of the outbreak in the travelers’ departure areas, are out of the government’s control. The model is able to, but not limited to, answer a set of important questions that are of common interests among the government, academic researchers, and the general public: • What are the quantitative effects of various reopening strategies? • How do multiple reopening policy measures interact with each other? • What are the optimal reopening strategies given specific constraints and objectives? To demonstrate the general method of specifying this type of model, the model development is presented in steps as follows. – Step 1 expands the standard Susceptible–Infectious–Recovered (SIR) model to a generalized model that allows for asymptomatic infection and recovery, reinfection, and mortality. – Step 2 parametrizes government policies for local people. – Step 3 considers the pandemic scenario, which includes multiple areas and allows for travelers. – Step 4 parametrizes government policies for travelers and presents a general pandemic model with internal and external policy interventions. A list of notations used in the final model is presented in Table 1. Step 1 A generalized epidemic model 0

0

0

The discrete-time standard SIR model is X t þ 1 ¼ Pt  X t , in which the states are 0 X t ¼ ½St I t Rt T and the transition matrix is  bIt bIt 00 1  c00c1 P0t ¼ 1  N N

ð6Þ

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Table 1 Notations Notation

Description

In the standard SIR model Number of susceptible people in period t St Number of infected people in period t It Number of recovered people in period t Rt N Population size, i.e. N ¼ St þ I t þ Rt for all t b Transmission rate c Recovery rate Disease parameters in the transition matrix ( Pit ) of the expanded model q Reinfection rate, i.e. the transmission rate for recovered people l Diagnosis rate s Severe symptom development rate d Mortality rate of people with severe symptoms Recovery rate for asymptomatic patients c1 Recovery rate for patients with mild symptoms c2 Improvement rate, the rate at which severe symptoms disappear c3 Government’s internal policy parameters and functions Social distancing parameter git cit

Contact tracing and random testing parameter

hit

Quarantine probability of asymptomatically infected people. It is a function of cit , hit , and git Self-quarantine exit rate. It is the rate at which susceptible people in self-quarantine rejoins the local community Self-quarantine intensity parameter, reflecting the government policy on asking susceptible people to self-quarantine

rit sqit wit

Quarantine probability of susceptible people. It is a function of sqit Effectiveness of one test in area i in period t, depending on technology hit advancements Travel related parameters and matrices in the expanded model Population size of area i in the initial state Ni ij Number of travelers from area i to area j in period t Nt ij Distribution across the 8 epidemic states of travelers from area i to area j in period travelt t. Numbers of people in all states sum to N ijt b it P ~ P

Testing matrix of area i in period t, reflecting the test effectiveness hit

Pijt

Quarantine matrix of area i. The matrix only captures changes in traveler’s health status and does not involve local transmission Local transmission matrix among travelers from area i to area j in period t

qit

Quarantine duration for inbound travelers in area i who arrived in period t

Number of tests for inbound travelers in area i in period t r it (Reproduced from [88] under Creative Commons Attribution License)

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To incorporate asymptomatic infection, we separate I t into IA for asymptomatic and IS for symptomatic. To allow future analysis to differ between mild symptomatic patients and severe patients, we further separate IS into ISM for mild and ISS for severe. To incorporate mortality, we add a state D for dead. Now the 00 00 00 expanded model is X t þ 1 ¼ Pt  X t , in which 00

Xt ¼ ½St IAt ISMt ISSt Rt Dt T

ð7Þ

and the transition matrix is 2 Pt ¼ 4 00

1

00

bIt N

00000

00

bIt N

1  c1  l 0 0

00

qIt N

0 0 l 1  c2  s c3 0 0 0 0 s 1

c3  d 0 0 0 c1 c2 0 1 

00

qIt N

0000d01

3 5 ð8Þ

00

with I t ¼ IAt þ ISM t þ ISSt is the total of infectious people in the population. Note that Eq. (3) may easily transform into the transition matrices of other classical compartmental models. The SI model and its variants, in which infection does not convey immunity, can be modeled by setting q ¼ b. The SIRD model and its variants, in which infection can cause mortality, is modeled by setting d [ 0. The SEIR model and its variants, in which the exposed state is not infectious, can 00 be modeled by adding it between states S and IA or setting I t ¼ ISM t þ ISSt , depending on the medical findings on the infection mechanism. Step 2 A generalized epidemic model with policy interventions Epidemic containment policies within an area aim to lower infection. This means bI

00

that the government would focus on making Nt in Eq. (3) as small as possible. Each component in the expression corresponds to a policy direction. First, social distancing lowers the probability that a susceptible person is infected, keeping the I

00

fraction of infectious people in the active population, Nt , constant. We add a parameter g to represent the intensity of the social distancing policy implementation. Second, contact tracing lowers the number of infectious people who interact closely with susceptible people. We separate the IA state into IAQ for quarantined and IANQ for not quarantined. The rate at which IANQ people can be identified through the contact tracing system is denoted by h. Naturally, symptomatic patients of the infectious disease would be isolated as well. So, the numerator is modified to 00 I t ¼ IANQt . Third, the self-isolation of susceptible people lowers the number of susceptible people, especially people with high medical risks once infected, who would have close interaction with infectious people. We separate the S state into SQ for quarantined and SNQ for not quarantined. SQ people do not interact with anyone and thus cannot be infected, so the infection rate of SQ people is 0. The self-isolation policy can be parametrized in many ways. We abstract the policy as two parameters. The rate at which SNQ is asked to self-isolate (w) and the rate at which SQ people are released from self-isolation (r). The self-isolation policy

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reduces the number of people in the SNQ state and thus lowers the number of new bI

00

infections, whose mathematical expression is SNQt  Nt . Therefore, the single-region generalized model with policies is X t þ 1 ¼ Pt  X t , in which X t ¼ ½SQt SNQt IAQt IANQt ISM t ISSt Rt Dt T

ð9Þ

and the transition matrix is 2

1r 6 r 6 6 0 6 6 0 i Pt ¼ 6 6 0 6 6 0 6 4 0 0

1

wit bgit ItNQ

0 bgit ItNQ 0 0 0 0



wit

0 0 1  l  c1 0 l 0 c1 0

0 0 hit 1  l  hit  c1 l 0 c1 0

0 0 0 0 1  c2  s s c2 0

0 0 0 0 c3 1  c3  d 0 d

0 0 0 qgit ItNQ 0 0 1  qgit ItNQ 0

3 0 07 7 07 7 07 7: 07 7 07 7 05 1

ð10Þ t ¼ SNQt þIANQ in which I NQ t IANQt þ Rt is a shorthand for convenience.

Step 3 A generalized pandemic model with travelers Expanding an epidemic to a pandemic involves modeling multiple areas. The areas are heterogeneous in their population distribution across the states and their governments’ internal policies. We use superscripts i and j to denote different areas. When traveling is allowed, the population of an area is the overall population, minus the outbound travelers, plus the inbound travelers. We denote the distribution of travelers from area i to area j among the states bytravelijt . The infectious disease changes the population who does not travel with the local transition matrix Pt defined in Step 2, indexed byi. The travelers spent the time period on the road and their state changes are described by the traveler’s transition matrixPijt . Hence, in the model with multiple areas and travelers across the areas, a representative area i is described by Xti þ 1

¼

Pit

Xti



X j6¼i

! travelijt

þ

X

Pijt traveljit

ð11Þ

j6¼i

Step 4 A generalized pandemic model with internal and external policies Pandemic containment policies often involve special treatment for travelers. This model illustrates the parametrization of travel restrictions, quarantine, and testing inbound travelers. Travel restrictions allow the government may choose the total number of inbound travelers from each foreign area, i.e., the value of N ijt ¼



travelji while it cannot control the composition of the travelers. The quarantine t

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mechanism keeps inbound travelers in facilities for qit days before allowing them to interact with each other or with the local population. We denote the transition ~ This matrix has zero infection rate, i.e., g ¼ 0. matrix of quarantined people byP. The government may also test the inbound travelers for r it times. We use a testing b it to account for the possibly imperfect test. transition matrix P Therefore, in the pandemic model that accounts for internal and external policies, area i is described by Xti þ 1

¼

Pit

Xti



X j6¼i

! travelijt

þ

X

X



^ ity P

i rty
y ij ~ Pty traveljity P

j6¼i ys:t:qity ¼y&y  0

ð12Þ In other words, Eq. (7) means that the population in area i today is composed of two groups of people. The first group is local people who did not depart area i yesterday. They are exposed to the epidemic condition and policies in the area i. The second group includes all inbound travelers who are allowed to step out of quarantine today. Each of these just-released travelers was first exposed to the infection risk (Pijty ) on their way to area i, and were tested subject to the testing b ity ) on the day they arrived policy (r ity times with test accuracy characterized by P (t  y), and were quarantined subject to the quarantine policy (y days with daily ~ on the day they arrived. change characterized by P)

2.3 Applying the Model in a Reopening Strategy Evaluation Analysis This generalized model is introduced and used to evaluate reopening policies. It is focused on the policy design of an area with low domestic epidemic risks, characterized by small values of IANQ inX it . The primary concern in reopening is whether imported cases would cause a domestic outbreak, counteracting all the efforts of the internal policies in containing the epidemic. So the value of the interest in the model output is the time series of new cases of the low-risk area. For most parts of the paper, we focused on newly infected cases, i.e., the value ofSNQt  bgt I NQ t , and we discussed alternative definitions of new cases in the extension section. In our results in [88], we experimented with various internal and external policy combinations to show the marginal effects of each policy tool on containing the potential outbreak when reopening amid foreign risks. The section also provides an example of using the generalized model as the state equation in an optimal control problem. The cost function ismaxfnewimportedcaset ; C g, meaning that the government does not want the daily number of new cases among the inbound travelers to be more thanC. The time series to be solved are traveljit for allj.

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Discussion: Alternative and Promising Optimal Control Methods for Better Applicability

Among the other optimal control methods that are still not investigated enough and would be more beneficial for many cases of pandemics concern the consideration of a discrete control function in a continuous-time dynamical system. To be clearer, this has to do with the impulse control problems [84] that are not sufficiently studied despite their advantage to describe the impulse process intervention, such as in our case of global epidemics. In fact, as we know, most control measures are or cannot practically be introduced at continuous times but rather applied on a discrete basis. It is important to note and perceive the problem of implementation of any control policy that would be similar to the problem of timing in the administration procedure of a vaccine. However, most mathematical models in literature and the ones we discussed here are still incapable of considering this problem, especially in the presence of control, because of the lack of convincing theoretical and numerical methods. But if this is resolved, it would certainly provide more realistic results. In this context, and to advance in research on this topic, Abouelkheir et al. in [86] suggested a clear theoretical optimal control concept and a detailed algorithm that solved the impulse vaccination approach they have applied to the case of an epidemic model with short-term immunity. Their results are very promising as they can be studied again for any case of epidemic differential systems. Another question that is also rare to meet in literature would be about the time sufficient to control a pandemic. Using optimal control theory, we can reach the answer. In recent work, Zakary et al. in [87] responded to how much time would be needed to control an epidemic by considering a free final time-optimal control approach and which they have applied to four compartmental epidemic models. Later, Abouelkheir et al. in [29] devised a more accurate numerical method for a more difficult problem that considers an additional constraint for fixing the fraction of people who could be controlled because of the limited medical resources as also considered in [28]. In a situation where there is still a lack of information about the availability of vaccines and the problems that some control interventions would create, researchers should maybe think more about investigating the importance of simpler measures to fight against COVID-19. For instance, in the case of HIV/AIDS, Zakary et al. in [16] suggested a novel mathematical model with an optimal control approach that showed the effectiveness of awareness programs in reducing the number of infectives with this disease. In a more interesting framework that respects the spatial aspect of pandemics, the authors in [17, 18] reached the same conclusions using the multi-region control approach applied to the same epidemic.

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Conclusion

This chapter applied the results of the most known optimal control designs to different mathematical models of pandemics and provided many examples of this theory in the case of COVID-19. Further, it was suggested the adaptation of novel types and forms of models to such pandemics in order to describe factors that often have a direct impact on the epidemic, while proposing to work more on the most recent optimal control techniques in order to exhibit some realistic features that aid to the spread of the epidemic and the delay of a control policy. Taking just the example of mobility restrictions, they can positively affect public health. Still, they could, at the same time, have adverse effects on freedom of movement, the economy, and society [87]. The same could be deduced for other strategies and where greater losses would seem inevitable. This is why some of the controls named here may not represent the ideal approach to contain and fight against a global epidemic. However, as explained in all sections, the problem lies not in control but in ignoring some recent mathematical concepts that should serve as starting points for further development rather than repetitive techniques that are unlikely to respond realistically to newly born pandemics and infodemics. Core Messages

• Infodemics are still underestimated. • Control would fail if there is no intention to redesign the health education system.

References 1. Arenas A, Cota W, Gómez-Gardeñes J, Gómez S, Granell C, Matamalas JT, Soriano-Paños D, Steinegger B (2020) Modeling the Spatiotemporal Epidemic Spreading of COVID-19 and the Impact of Mobility and Social Distancing Interventions”. Phys Rev X 10(4):041055 2. Viguerie A, Lorenzo G, Auricchio F, Baroli D, Hughes TJ, Patton A, Reali A, Yankeelov TE, Veneziani A (2021) Simulating the spread of COVID-19 via a spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion. Appl Math Lett 111:106617 3. Wang Z, Zhang X, Teichert GH, Carrasco-Teja M, Garikipati K (2020) System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19. Comput Mech 66(5):1153–1176 4. Zhou Y, Wang L, Zhang L, Shi L, Yang K, He J, Zhao B, Overton W, Purkayastha S, Song P (2020) A spatiotemporal epidemiological prediction model to inform county-level COVID-19 risk in the United States. Special Issue 1-COVID-19: Unprecedented Challenges and Chances 5. Brauer F, Castillo-Chavez C, Feng Z (2019) Mathematical models in epidemiology, vol 32. Springer, New York 6. Brauer F, Castillo-Chavez C (2012) Mathematical models in population biology and epidemiology New York: Springer, 2, p. 508

206

I. Elmouki et al.

7. Bichara D, Iggidr A (2018) Multi-patch and multi-group epidemic models: a new framework. J Math Biol 77(1):107–134 8. Arino J (2009) Diseases in metapopulations. In modeling and dynamics of infectious diseases (pp. 64–122) 9. Arino J, Van den Driessche P (2006) Disease spread in metapopulations. Fields Inst Commun, 48(1):1–13 10. El Kihal F, Abouelkheir I, Rachik M, Elmouki I (2019) Role of media and effects of infodemics and escapes in the spatial spread of epidemics: A stochastic multi-region model with optimal control approach. Mathematics 7(3):304 11. El Kihal F, Rachik M, Zakary O, Elmouki I (2017) A multi-regions SEIRS discrete epidemic model with a travel-blocking vicinity optimal control approach on cells. Int J Adv Appl Math Mech 4(3):60–71 12. Zakary O, Rachik M, Elmouki I (2017) A new epidemic modeling approach: Multi-regions discrete-time model with travel-blocking vicinity optimal control strategy. Infect Dis Model 2 (3):304–322 13. Peyravi M, Marzaleh MA, Shamspour N, Soltani A public education and electronic awareness of the new coronavirus (COVID-19): Experiences from iran. disaster medicine and public health preparedness, 1 14. Alahdal H, Basingab F, Alotaibi R (2020) An analytical study on the awareness, attitude and practice during the COVID-19 pandemic in Riyadh, Saudi Arabia. J Infect Public Health 13 (10):1446–1452 15. Samanta S, Rana S, Sharma A, Misra AK, Chattopadhyay J (2013) Effect of awareness programs by media on the epidemic outbreaks: A mathematical model. Appl Math Comput 219(12):6965–6977 16. Zakary O, Rachik M, Elmouki I (2016) On the impact of awareness programs in HIV/AIDS prevention: an SIR model with optimal control. Int J Comput Appl 133(9):1–6 17. Zakary O, Rachik M, Elmouki I (2017) A multi-regional epidemic model for controlling the spread of Ebola: awareness, treatment, and travel-blocking optimal control approaches. Math Methods Appl Sci 40(4):1265–1279 18. Zakary O, Larrache A, Rachik M, Elmouki I (2016) Effect of awareness programs and travel-blocking operations in the control of HIV/AIDS outbreaks: a multi-domains SIR model. Adv Difference Equ 2016(1):169 19. Hays, Jo N. Epidemics and pandemics: their impacts on human history. Abc-clio, 2005. 20. Rosenberg CE (1992) Explaining epidemics. Camb Univ Press 21. Speltini G, Passini S (2014) Cleanliness/dirtiness, purity/impurity as social and psychological issues. Cult Psychol 20(2):203–219 22. Conant, J. (2005). Sanitation and cleanliness for a healthy environment. Hesperian Foundation. 23. Davies ML, Davies TL (1987) Biblical ills and remedies. J R Soc Med 80(8):534 24. Goodall EW (1934) A short history of the epidemic infectious diseases. John Bale, Sons and Danielsson, Limited 25. Orey Cal (2008) The healing powers of vinegar-revised and updated. Kensington Publishing Corp 26. Conti AA (2008) Quarantine through history. Int Encycl Public Health, 454 27. Gensini GF, Yacoub MH, Conti AA (2004) The concept of quarantine in history: from plague to SARS. J Infect 49(4):257–261 28. El Kihal F, Abouelkheir I, Rachik M, Elmouki I (2018) Optimal control and computational method for the resolution of isoperimetric problem in a Discrete-time SIRS system. Math Comput Appl 23(4):52 29. Abouelkheir I, El Kihal F, Rachik M, Elmouki I (2018) Time needed to control an epidemic with restricted resources in SIR model with Short-term controlled population: A fixed point method for a free isoperimetric optimal control problem. Math Comput Appl 23(4):64 30. Lenhart S, Workman JT (2007) Optimal control applied to biological models. CRC Press

9

Optimal Control: Application and Applicability in Times …

207

31. cdc.gov/nonpharmaceutical-interventions/index.html 32. Imai N, Gaythorpe KA, Abbott S, Bhatia S, van Elsland S, Prem K, Liu Y, Ferguson NM (2020). Adoption and impact of non-pharmaceutical interventions for COVID-19. Wellcome Open Research 5 33. Flaxman S, Mishra S, Gandy A, Unwin HJT, Coupland H, Mellan TA, Zhu H, Berah T, Eaton JW, Guzman PN, Schmit N (2020). Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries. Imperial College London. https://doi.org/10.25561/77731 34. Flaxman S, Mishra S, Gandy A, Unwin HJT, Mellan TA, Coupland H, Whittaker C, Zhu H, Berah T, Eaton JW, Monod M (2020) Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature 584(7820):257–261 35. Hens N, Vranck P, Molenberghs G (2020) The COVID-19 epidemic, its mortality, and the role of non-pharmaceutical interventions. Eur Heart J Acute Cardiovasc Care 9(3):204–208 36. Patiño-Lugo DF, Vélez M, Velásquez Salazar P, Vera-Giraldo CY, Vélez V, Marín IC, Ramírez PA, Quintero SP, Castrillón Martínez E, Pineda Higuita DA, Henandez G (2020) Non-pharmaceutical interventions for containment, mitigation and suppression of COVID-19 infection. Colomb Med 51:2 37. Lin X (2020). Analysis of 25,000 Lab-Confirmed COVID-19 cases inWuhan: Epidemiological characteristics and Non-Pharmaceutical intervention effects. Department of biostatistics and department of statistics, Harv Univ Broad Inst 38. Ferguson N, Laydon D, Nedjati Gilani G, Imai N, Ainslie K, Baguelin M, Bhatia S, Boonyasiri A, Cucunuba Perez ZULMA, Cuomo-Dannenburg G, Dighe A (2020) Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand. Imperial College London 10:77482 39. Wong M, Huang J, Teoh J, Wong S (2020) Evaluation on different non-pharmaceutical interventions during COVID-19 pandemic: an analysis of 139 countries. J Infect 40. Seale H, Dyer CE, Abdi I, Rahman KM, Sun Y, Qureshi MO, Dowell-Day A, Sward J, Islam MS (2020) Improving the impact of non-pharmaceutical interventions during COVID-19: examining the factors that influence engagement and the impact on individuals. BMC Infect Dis 20(1):1–13 41. Haushofer J, Metcalf CJE (2020) Evaluation of non-pharmaceutical interventions is needed to mitigate the COVID-19 pandemic. Working Paper of Princeton University 42. Castex G, Dechter E, Lorca M (2020) COVID-19: Cross-country heterogeneity in effectiveness of non-pharmaceutical interventions. Covid Economics 14:175–199 43. KRR G, Casella F (2020) Non-Pharmaceutical interventions (NPIs) to reduce COVID-19 mortality. Available at SSRN 3560688 44. Cho SWS (2020) Quantifying the impact of non-pharmaceutical interventions during the COVID-19 outbreak-The case of Sweden. Econ J 45. Al Wahaibi A, Al Manji A, Al Maani A, Al Rawahi B, Al Harthy K, Alyaquobi F, Al-Jardani A, Petersen E, Al Abri S (2020) COVID-19 epidemic monitoring after non-pharmaceutical interventions: the use of time-varying reproduction number in a country with a large migrant population. Int J Infect Dis 46. Sun J, Shi Z, Xu H (2020) Non-pharmaceutical interventions used for COVID-19 had a major impact on reducing influenza in China in 2020. J Travel Med 47. Cowling BJ, Ali ST, Ng TW, Tsang TK, Li JC, Fong MW, Liao Q, Kwan MY, Lee SL, Chiu SS, Wu JT (2020) Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: an observational study. The Lancet Public Health 48. Demirguc-Kunt A, Lokshin M, Torre I (2020) The sooner, the better: The early economic impact of non-pharmaceutical interventions during the COVID-19 pandemic. World Bank Policy Res Work Pap, (9257) 49. Davies NG, Kucharski AJ, Eggo RM, Gimma A, Edmunds WJ, Jombart T, O'Reilly K, Endo A, Hellewell J, Nightingale ES, Quilty BJ (2020) Effects of non-pharmaceutical

208

50.

51.

52. 53.

54.

55. 56.

57. 58. 59. 60. 61.

62. 63.

64. 65. 66.

67. 68.

69.

I. Elmouki et al. interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study. The Lancet Public Health Zamir M, Shah Z, Nadeem F, Memood A, Alrabaiah H, Kumam P (2020) Non pharmaceutical interventions for optimal control of COVID-19. Comput Methods Programs Biomed 196:105642 Ullah S, Khan MA (2020) Modeling the impact of non-pharmaceutical interventions on the dynamics of novel coronavirus with optimal control analysis with a case study. Chaos, Solitons Fractals 139:110075 Panovska-Griffiths, Jasmina (2020) Can mathematical modelling solve the current Covid-19 crisis?.: 1–3 Kraemer MU, Yang CH, Gutierrez B, Wu CH, Klein B, Pigott DM, Du Plessis L, Faria NR, Li R, Hanage WP, Brownstein JS (2020) The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368(6490):493–497 Cartenì A, Di Francesco L, Martino M (2020) How mobility habits influenced the spread of the COVID-19 pandemic: Results from the Italian case study. Sci Total Environ 741 (2020):140489 Zhu Y, Xie J, Huang F, Cao L (2020) The mediating effect of air quality on the association between human mobility and COVID-19 infection in China. Environ Res 189:109911 Kermack WO, McKendrick AG (1927) A contribution to the mathematical theory of epidemics. Proceedings of the royal society of london. Series A, Containing papers of a mathematical and physical character, 115(772):700–721 Allen LJ (1994) Some discrete-time SI, SIR, and SIS epidemic models. Math Biosci 124 (1):83–105 Brauer F, Feng Z, Castillo-Chavez C (2010) Discrete epidemic models. Math Biosci Eng 7 (1):1 Brauer F, Castillo-Chavez C, Castillo-Chavez C (2012) Mathematical models in population biology and epidemiology. New York: Springer, 2, p. 508 Arino, J., & Van Den Driessche, P. (2003) The basic reproduction number in a multi-city compartmental epidemic model. In Positive Systems. Springer, Berlin, Heidelberg, pp. 135–142 Zakary O, Rachik M, Elmouki I (2017) A new analysis of infection dynamics: multi-regions discrete epidemic model with an extended optimal control approach. Int J Dyn Control 5 (4):1010–1019 Zakary O, Rachik M, Elmouki I (2017) On the analysis of a multi-regions discrete SIR epidemic model: an optimal control approach. Int J Dyn Control 5(3):917–930 Abouelkheir I, El Kihal F, Rachik M, Zakary O, Elmouki I (2017) A multi-regions SIRS discrete epidemic model with a travel-blocking vicinity optimal control approach on cells. J Adv Math Comput Sci, 1–16 Abouelkheir I, Rachik M, Zakary O, Elmouk I (2017) A multi-regions SIS discrete influenza pandemic model with a travel-blocking vicinity optimal control approach on cells Am J Comput Appl Math, 7(2):37–45 Zakary O, Rachik M, Elmouki I, Lazaiz S (2017) A multi-regions discrete-time epidemic model with a travel-blocking vicinity optimal control approach on patches. Adv Difference Equ 2017(1):120 Zakary O, Bidah S, Rachik M, Elmouki I (2019) Cell and patch vicinity travel restrictions in a multi-regions SI discrete epidemic control model. Int J Adv Appl Math Mech 6:30–41 Bidah S, Rachik M, Zakary O, Boutayeb H, Elmouki I (2018) Travel-blocking optimal control policy on borders of a chain of regions subject to SIRS discrete epidemic model. Asian J Res Infect Dis, 1–12 Chouayakh K, Rachik M, Zakary O, Elmouki I (2017) A multi-regions SEIS discrete epidemic model with a travel-blocking vicinity optimal control approach on cells. J Math Comput Sci 7(3):468–484

9

Optimal Control: Application and Applicability in Times …

209

70. Boutayeb H, Bidah S, Zakary O, Elmouki I, Ferjouchia H, Mostafa R (2020) SIS multi-regions discrete Influenza pandemic model and travel-blocking vicinity optimal control strategy on two forms of patch. Commun Math Biol Neurosci, Article-ID 71. Tejedor S, Cervi L, Tusa F, Portales M, Zabotina M (2020) Information on the COVID-19 pandemic in daily newspapers’ front pages: Case study of Spain and Italy. Int J Environ Res Public Health 17(17):6330 72. Tangcharoensathien V, Calleja N, Nguyen T, Purnat T, D’Agostino M, Garcia-Saiso S, Landry M, Rashidian A, Hamilton C, AbdAllah A, Ghiga I (2020) Framework for managing the COVID-19 infodemic: methods and results of an online, crowdsourced WHO technical consultation. J Med Internet Res 22(6):e19659 73. Zarocostas J (2020) How to fight an infodemic. The Lancet 395(10225):676 74. Nielsen RK, Fletcher R, Newman N, Brennen SJ, Howard PN (2020) Navigating the ‘infodemic’: How people in six countries access and rate news and information about coronavirus. Reuters Institute 75. Naeem SB, Bhatti R (2020) The Covid-19 ‘infodemic’: a new front for information professionals. Health Information & Libraries Journal 76. Alvarez-Risco Aldo (2020) The Peru Approach against the COVID-19 Infodemic: Insights and Strategies. Am J Trop Med Hyg, tpmd200536 77. Allahverdipour H (2020) Global challenge of health communication: Infodemia in the Coronavirus Disease (COVID-19) Pandemic. J Educ Community Health, 0–0 78. Andersen A, Godoy E (2020) Infodemia em tempos de pandemia: batalhas invisíveis com baixas imensuráveis. Rev Memorare 7(2):184–198 79. who.int/news/item/23–09–2020-managing-the-covid-19-infodemic-promoting-healthybeha viours- and-mitigating-the-harm-from-misinformation-and-disinformation 80. Yamanis T, Nolan E, Shepler S (2016) Fears and misperceptions of the Ebola response system during the 2014–2015 outbreak in Sierra Leone. PLoS Negl Trop Dis 10(10):e0005077 81. Wigmore R (2015) Contextualising Ebola rumours from a political, historical and social perspective to understand people’s perceptions of Ebola and the responses to it. Ebola Response Anthropol Platf, 4. 82. Cheung E (2015) An outbreak of fear, rumours and stigma: psychosocial support for the Ebola virus disease outbreak in West Africa. Intervention 13(1):70–76 83. Zhang Z, Zeb A, Hussain S, Alzahrani E (2020) Dynamics of COVID-19 mathematical model with stochastic perturbation. Adv Difference Equ 2020(1):1–12 84. Rihan FA, Alsakaji HJ, Rajivganthi C (2020) Stochastic SIRC epidemic model with time-delay for COVID-19. Adv Difference Equ 2020(1):1–20 85. Yang, T. (2001). Impulsive control theory (Vol. 272). Springer Science & Business Media 86. Abouelkheir I, El Kihal F, Rachik M, Elmouki I (2019) Optimal impulse vaccination approach for an SIR control model with short-term immunity. Mathematics 7(5):420 87. Zakary O, Rachik M, Elmouki I (2017) How much time is sufficient for benefiting of awareness programs in epidemics prevention? A free final time optimal control approach. Int J Adv Appl Math Mech 4:26–40 88. Zhou Y, Xu R, Hu D, Yue Y, Li Q, Xia J (2020) Effects of human mobility restrictions on the spread of COVID-19 in Shenzhen, China: a modelling study using mobile phone data. The Lancet Digital Health 2(8):e417–e424 89. Zhong L (2021) A dynamic pandemic model evaluating reopening strategies amid COVID-19. PLoS ONE 16(3):e0248302

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Ilias Elmouki is a Ph.D. in applied mathematics from Hassan II University of Casablanca in Morocco. His research until 2022 was about control theory and systems mathematical modeling with applications to epidemics, cancer, environmental sustainability, bioeconomy, and finance. Ling Zhong is an assistant professor of economics at Cheung Kong Graduate School of Business in China. Her research fields are labor economics, the economics of education, and the Chinese economy.

Abdelilah Jraifi is the Chief Department of Computer Science at the National School of Applied Sciences in Safi-Morocco. His main research area is stochastic calculus and analysis with applications to models in finance.

Aziz Darouichi is an associate professor at the FST-Marrakesh in Morocco. His research recently focused on scientific computing, data science, and artificial intelligence.

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Analysis of a COVID-19 Model Implementing Social Distancing as an Optimal Control Strategy

Sangeeta Saha and G. P. Samanta

No human investigation can be called real science if it cannot be demonstrated mathematically. Leonardo da Vinci

Summary

COVID-19 has turned into one of the greatest pandemics ever witnessed within a very short period. The governments of almost every country have announced to maintain physical distancing and to use precautionary measures to prevent the high disease transmission. Here, a compartmental epidemiological model of COVID19 transmissions has been formulated. People of the susceptible class move to the asymptotically exposed class by coming close to asymptotically exposed people, symptomatically infected people, quarantined people, and hospitalized people. The analysis reveals that when most people from the symptomatically infected class move to quarantine, then even the higher probability of virus transmission hardly makes any impact on the growth of the infected population and the count of the infected people starts to reduce. In the case of coronavirus, there are no existing vaccines which means maintaining physical distancing and hygiene are the only way-outs to avoid the infection. In the optimal control problem, social distancing is considered as one of the important control interventions to mitigate the disease prevalence. Moving the symptomatically infected population to quarantine or hospitals is taken as the other two control strategies. The trajectory profiles of the asymptomatically exposed class show that a lesser number of people become infected in the presence of the control interventions. In conclusion, it can be stated that the simultaneous use of all control interventions reduces the

S. Saha · G. P. Samanta (B) Department of Mathematics, Indian Institute of Engineering Science and Technology Shibpur, Howrah, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_10

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virus transmission in the present pandemic situation and also decrease the count of the infected population in the environment.

Graphical Abstract/Art Performance

Epidemic in complex networks (Adapted with permission from the Health and Art (HEART), Universal Scientific Education and Research Network (USERN); Painting by Ummugulsun Topcu). The code of this chapter is 01001101 01101111 01100100 01100101 01101100. Keywords

COVID-19 · Epidemic model · Optimal control · Social distancing · Stability

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1 Introduction Novel Betacoronavirus spread from Wuhan, capital of Hubei Chinese province when the first case was reported in the December of 2019 [1–3]. The wholesale Huanan seafood market at Wuhan is known for the trade of live animals and in the beginning, most cases were reported near that area [4]. Surprisingly, the virus spread all over the Chinese province within a few weeks and Worldwide within months. Looking at the severity, World Health Organisation (WHO) declared COVID-19 as a pandemic on March 17, 2020, and within March 25, 2020, almost 136 countries implemented additional health precautions which particularly interfere with international traffic defined under Article 43 of the International Health Regulations. The novel Coronavirus is one type of RNA virus from Coronaviridae family and it is also named as SARS-CoV-2 [5,6]. Symptoms of COVID-19 include viral pneumonia, dry cough, fever, tiredness, aches, and pains, breathing problems etc. [1,7–9] and according to the recent reports, a COVID-19 patient may face the problem of losing the sense of smell. The data from the dashboard of CSSE and worldometers reveal that the count of infected, recovery and death cases at 2nd September, 2020, has reached upto 26,170,360; 18,435,692 and 866,614 respectively worldwide [10,11]. Among 213 countries and territories, United States (6,290,737 cases), Brazil (4,001,422 cases) India (3,848,968 cases) and Russia (1,005,000 cases) are in worse condition because the confirmed infected cases, as per the database, exceed already 1 million there. In fact, in the United States the confirmed COVID-19 cases is still the highest due to high transmission (Case fatality ratio 3.0%) [12]. The reported infected cases of COVID-19 has increased from 15 to 6,290,737 till September 2, 2020. Also, the counts of total death and recovered cases have become highest in the US with number 189,964 and 3,547,032 respectively. Government of Wuhan, China first called for quarantine strategy on January 23, of the year to control the outbreak of disease. The huge spread of the virus within a very short time led the governments of other countries to announce for half or full lockdowns. France and Santa Catarina (Brazil) announced lockdown on 17th March; Sao ˜ Paulo (Brazil) announced on 24th March; the United Kingdom announced on 23rd March; India announced on 25th March etc. Observing the severity and the reported cases in India, one can easily assume that there is a big portion of COVID-19 cases is not officially documented till now due to inadequacy of test kits. Some reports indicate of the possibility for a person having COVID-19 positive result without showing symptoms. In this case, those infected people who are exposed in the environment mainly facilitate the transmission of novel coronavirus [2]. The first case of COVID-19 in India was documented at Kerala on 30th January, 2020 when a student came to India from Wuhan. From the database of NIC, India, total 3,848,968 cases is confirmed in the country among which 814,086 active cases, 2,967,396 recoveries and 67,486 death cases are documented till 2nd September, 2020 [13,14]. In fact, on 2nd September, the number of reported COVID-19 cases in a single day exceeds all the records till now as 82,860 new cases are registered on that day in India, i.e., the country has crossed 80,000 benchmarks for the first time in terms of per day infection. India has already crossed 50,000 newly infected cases per

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day on 26th July when 50,525 number of confirmed cases of COVID-19 is registered. Reports of August 8, 2020, has shown that the country has crossed the USA in terms of newly active cases in 24 h. The USA has reported 54,199 new cases on 8th August where India has taken the top place with number 65,156 new cases. On September 4, 2020, India has reported 87,115 new Coronavirus cases which is the highest till that date in the World since the very beginning. India’s total confirmed cases of COVID19 is 4,020,239, death cases rises up to 69,635. The infection rate is increasing day by day at a very higher rate. According to the data of September 6, 2020, India is on the top position on the basis of newly infected cases per day for continuously 32 days. India moves to second position crossing Brazil in terms of total confirmed cases on 6th September. The reports have revealed that on that day total confirmed cases of USA, India and Brazil are respectively 6,460,250; 4,202,562 and 4,137,606. It is the second day when India is in the highest position for newly infected cases as well as reported deaths per day with the number 91,723 and 1,008 respectively. In case of recovery also, it is in the second position with 3,247,297 recovered cases. On 6th September, the total confirmed cases of COVID-19 across the World is 27,283,718 where 231,553 newly infected cases are reported. Total reported deaths and recovery till now are 887,305 and 19,367,165 respectively. In India, the ‘case fatality ratio’ is estimated and is found for this viral infection is about 1.7% [12] and this turns out to be a matter of concern as there is no vaccine invented till now [15–18]. From the very beginning of the Coronavirus outbreak, the government in India has announced preventative measures to avoid disease transmission. The measures include maintaining physical distancing, adopting the self-quarantine strategy, using a face mask, avoiding touching faces frequently etc. The Indian government ordered a 14-h voluntary public curfew on March 22, 2020, when the confirmed cases of COVID-19 crossed 500. Observing the severity the Government announced for national lockdown for 21 days from 25th March but later, the lockdown extended up to May 31, 2020. From June 1, 2020, unlock 1.0 is started in only those places where the severity is in moderate level. From September 1, 2020, 4th phase of unlockdown has been started. In the containment zones, the movement has been restricted by imposing lockdown even during the unlockdown phases, while activities are permitted in other zones. The unlockdown phases have come up with some restrictions but according to the recent reports, India has reached to the highest peak for new reported cases of COVID-19 on 1st June and the count of new cases was approximately 8392. And the second peak has occured on 13th June when 12,023 number of newly infected cases per day are documented [10,11,14]. According to the reports of 5th September, 2020, India’s total confirmed case has reached to 4,110,839 where the newly infected cases are 90,600; active cases are 862,487; new deaths reported are 1,044 (total death 70,679). The reports of Worldometers and CSSE at JHU on September 5, 2020, has shown that this is the first time India has crossed all the countries including USA and Brazil taking the first place in terms of confirmed infected cases and deaths in a single day. As there are no vaccines exist to prevent the Coronavirus infection in humans, so, the effective ways to avoid the infection is to maintain physical distancing, applying self-quarantine etc. [19]. Reports on September 2020 reveal that almost 39 vaccines are currently under clinical research among which 6 are in Phase II-III trials

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and rest are in Phase I-II trials. The current pandemic situation due to COVID-19 has affected almost all sectors. The outbreak has severely affected both economical and public health which make an impact on our living style. Reports from the RBI and World Bank state that, after 1991, it is the first time when the economic growth of India is declined at a huge rate due to pandemic. Many people have already lost their jobs in the current situation. Amid this unemployment crisis, the fuel price has become another challenge for the population. According to the reports of May of the Centre for Monitoring Indian Economy (CMIE), India’s unemployment rate (30-day moving average) rise to almost 25% amid COVID-19 crisis though, by the reports of September, the rate is decreased to almost 8%. According to the reports of 31st August 2020, India has officially entered a phase of recession where the GDP data shows a collapse of almost 24% of gross domestic product in the second quarter. The Indian economy is affected by the current COVID-19 pandemic and the severest of lockdowns which causes a temporary halt in business activities. Some works have already been published showing the disease propagation in this COVID-19 pandemic situation [20–26]. Wu et al. has proposed an epidemic model on COVID-19 both nationwide and worldwide range based on the data of 31st December, 2019, to 28th January, 2020, [27]. Tang et al. [21] has proposed a deterministic compartmental model on coronavirus transmission including the clinical progression of the virus, epidemiological status of the infected people, and intervention strategies. Their results have shown that the control policies like tracing of human contact, quarantine etc. can minimize overall confirmed cases by reducing the transmission risk and also by reducing the control reproduction number. A research group of Cambridge University has submitted a report stating that 21-days lockdown strategy followed in India may not be sufficient to control the severity of the disease in large scale. In fact it can cause higher infection at later stage [28]. In this chapter, a compartmental epidemic model on Coronavirus transmission is formulated where the susceptible population move to asymptomatic stage who are exposed to environment when they come in contact with the people of same class, infected population who show symptoms, quarantined people and even hospitalised individuals. Currently India has almost 138 crores population and this huge population somehow hinders in the nationwide complete lockdown. It is true that a large portion of the susceptible individuals maintains the precautionary measures strictly, but there exists another portion who moves to asymptomatically exposed class. As most of the people recover due to their natural immunity, so, a portion of the asymptomatically exposed class, symptomatically exposed class, quarantined class and hospitalised class directly move to recovered class. Also, a portion of the symptomatically infected population moves to home-quarantine to prevent further transmission. Section “Formulation of Mathematical Model” consists of the proposed epidemiological model on COVID-19 and section “Positivity and Boundedness” shows that the system is biologically well-defined. Section “Equilibrium Analysis” contains the form of basic reproduction number (R0 ) along with the endemic equilibrium point of the proposed system. Sensitivity analysis for some vital parameters is performed in section “Sensitivity Analysis” and the stability conditions of the disease-free equilibrium point, as well as the endemic equilibrium point, are obtained in section “Stability Analysis”. The theorem in section “Bifurcation

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Analysis at R0 = 1” states that a transcritical bifurcation occurs around the diseasefree equilibrium point at R0 = 1 and the consequent section contains the numerical simulation of the proposed model when there is no optimal control strategies are implemented. In section “Optimal Control Problem”, a corresponding optimal control problem is formulated to reduce the disease burden by minimizing the count of the overall infective population. Section “Numerical Simulation of the Optimal Control Problem” contains the numerical simulations with one or all applied control strategies and the last part of the work consists of a brief conclusion.

2 Formulation of Mathematical Model We have formulated an epidemiological model of COVID-19 to analyse the disease outbreak on population across the World. In this model, the total population N can be subdivided into the following compartments: susceptible population (S), asymptomatic people who are exposed to coronavirus without showing any symptoms (A), the symptomatically infected population (I ), quarantined population (Q), hospitalised people (H ) and recovered people (R). The virus transmission from susceptible to asymptomatically exposed class is denoted by the term β(I + p1 A+ p2 Q + p3 H )S where β is the disease transmission rate per contact by a symptomatically infected individual and pi ’s for i = 1, 2, 3 are the reduction factors of infectivity by asymptomatically exposed (A), quarantined (Q) and hospitalised (H ) class compared to the symptomatically infected population. Here, we have considered that the virus can be transmitted with a higher rate from those people who are symptomatically infected than from hospitalised and quarantined people. Also, asymptomatically exposed compartment contains those people who are not showing any symptoms of COVID-19. The parameter , which is considered in the susceptible population, is the recruitment rate. And the parameter C denotes the average flow of asymptomatically exposed individuals which represents the portion of immigrating or emigrating individuals who are in the asymptomatic or pre-symptomatic state. It is justifiable as a continuous inflow of travellers may not even be checked due to inadequacy of test kits. So, there will be some individuals who are asymptomatically infected and exposed to susceptible. The parameter d is the natural mortality rate of each population and μ1 , μ2 are disease-induced death rates, incorporated in symptomatically infected and hospitalised class respectively. A person, whether is COVID-19 positive, is detected mostly by RT–PCR test. The swab from a person’s throat or nose is used in this test. Besides, there are several tests like TrueNAT, antigen testing etc. to detect COVID-19 in the human body. But these tests do not show appropriate results every time and may result in false-negative tests. So, a person tests negative through the tests still may have COVID-19. Moreover, in some cases, the symptoms develop after one or two weeks and so, a person turns out COVID positive even after two or three tests. A portion of asymptomatically exposed can show physical symptoms after some time and move to the symptomatically infected compartment with a rate of σ . And, some people move to recovered class by natural immunity with a rate of η. Now, from the symptomatically infected compartment, a portion of population

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moves to quarantine or isolation with the rate ρ1 whereas those infected who are in critical condition and need regular observations move to hospitals with the rate ρ2 . A portion of symptomatically infected people moves to recovered class with the rate ρ3 if they recuperate by natural immunity. From quarantined situation either people move to hospitals (with the rate ξ1 ) and recovered class (with the rate ξ2 ) depending on their health conditions. Lastly, the people from hospitalised class recover with a rate of γ . Henceforth, the proposed model takes the following form: dS dt dA dt dI dt dQ dt dH dt dR dt

=  − β(I + p1 A + p2 Q + p3 H )S − d S,

S(0) > 0,

= C + β(I + p1 A + p2 Q + p3 H )S − (d + σ + η)A,

A(0) ≥ 0,

= σ A − (ρ1 + ρ2 + ρ3 )I − (d + μ1 )I, I (0) ≥ 0, (1) = ρ1 I − (ξ1 + ξ2 )Q − d Q,

Q(0) ≥ 0,

= ρ2 I + ξ1 Q − γ H − (d + μ2 )H, = ρ3 I + η A + ξ2 Q + γ H − d R,

H (0) ≥ 0, R(0) ≥ 0,

In the calculation C is taken to be 0 as the international flights have been stopped in India from March, so, in this work, the assumption for inflow of immigrant travellers in the country has been excluded. A schematic diagram is provided in Fig. 1 for a better understanding of the proposed system.

3 Positivity and Boundedness System (1) is biologically well-posed as the system variables are positive and bounded with time and it is proved in this section. Theorem 1 Solutions of system (1) in R6+ are positive for t > 0. Proof System (1) has a unique solution on [0, κ) with 0 < κ ≤ +∞ as the functions on right side of the proposed model is both locally Lipschitz and continuous on the interval [29]. Let us show, S(t) > 0, ∀ t ∈ [0, κ). If it is not true, then ∃ t1 ∈ (0, κ) such that ˙ 1 ) ≤ 0 and S(t) > 0, ∀ t ∈ [0, t1 ). So we have A(t) ≥ 0, ∀ t ∈ [0, t1 ). S(t1 ) = 0, S(t ˙ 2 ) < 0 and Suppose this does not hold, then ∃ t2 ∈ (0, t1 ) such that A(t2 ) = 0, A(t A(t) ≥ 0, ∀ t ∈ [0, t2 ). Next we claim I (t) ≥ 0, ∀ t ∈ [0, t2 ). If it is not true, then ∃ t3 ∈ (0, t2 ) such that I (t3 ) = 0, I˙(t3 ) < 0 and I (t) ≥ 0, ∀ t ∈ [0, t3 ). From third equation of system (1), we have  d I  = σ A(t3 ) − (d + μ1 + ρ1 + ρ2 + ρ3 )I (t3 ) = σ A(t3 ) ≥ 0, dt t=t3

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Fig. 1 Schematic diagram of system (1)

which is a contradiction to I˙(t3 ) < 0. So, I (t) ≥ 0, ∀ t ∈ [0, t2 ). Next, let us prove Q(t) ≥ 0, ∀ t ∈ [0, t2 ). Suppose it is not true, then ∃ t4 ∈ (0, t2 ) ˙ 4 ) < 0 and Q(t) ≥ 0, ∀ t ∈ [0, t4 ). Now, from the fourth such that Q(t4 ) = 0, Q(t equation of (1):  d Q  = ρ1 I (t4 ) − (d + ξ1 + ξ2 )Q(t4 ) = ρ1 I (t4 ) ≥ 0, dt t=t4 ˙ 4 ) < 0. Hence, Q(t) ≥ 0, ∀ t ∈ [0, t2 ). which is a contradiction to Q(t Our next claim is H (t) ≥ 0, ∀ t ∈ [0, t2 ). If it does not hold, then ∃ t5 ∈ (0, t2 ) such that H (t5 ) = 0, H˙ (t5 ) < 0 and H (t) ≥ 0, ∀ t ∈ [0, t5 ). Fifth equation of system (1) gives:  d H  = ρ2 I (t5 ) + ξ1 Q(t5 ) − (γ + d + μ2 )H (t5 ) = ρ2 I (t5 ) + ξ1 Q(t5 ) ≥ 0, dt t=t5 which contradicts H˙ (t5 ) < 0. So, H (t) ≥ 0, ∀ t ∈ [0, t2 ).

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Second equation of (1) gives  d A  = β(I (t2 ) + p1 A(t2 ) + p2 Q(t2 ) + p3 H (t2 ))S(t2 ) − (d + σ + η)A(t2 ) dt t=t2 = β(I (t2 ) + p2 Q(t2 ) + p3 H (t2 ))S(t2 ) ≥ 0 ˙ 2 ) < 0. Hence, A(t) ≥ 0, ∀ t ∈ [0, t1 ). Then, we also have which contradicts A(t I (t) ≥ 0, Q(t) ≥ 0, H (t) ≥ 0, ∀ t ∈ [0, t1 ). Our next claim is R(t) ≥ 0, ∀ t ∈ [0, t1 ). Suppose it is not true, then ∃ t6 ∈ (0, t1 ) ˙ 6 ) < 0 and R(t) ≥ 0, ∀ t ∈ [0, t6 ). Further sixth equation such that R(t6 ) = 0, R(t of system (1) gives  d R  = η A(t6 ) + ρ3 I (t6 ) + ξ2 Q(t6 ) + γ H (t6 ) − d R(t6 ) dt t=t6 = η A(t6 ) + ρ3 I (t6 ) + ξ2 Q(t6 ) + γ H (t6 ) ≥ 0, ˙ 6 ) < 0. So, R(t) ≥ 0, ∀ t ∈ [0, t1 ). which contradicts R(t First equation of (1) implies  d S  =  − β(I (t1 ) + p1 A(t1 ) + p2 Q(t1 ) + p3 H (t1 ))S(t1 ) − d S(t1 ) dt t=t1 =  > 0, ˙ 1 ) ≤ 0. Hence we have, S(t) > 0, ∀ t ∈ [0, κ) with 0 < κ ≤ which contradicts S(t +∞. Following the previous steps we get A(t) ≥ 0, I (t) ≥ 0, Q(t) ≥ 0, H (t) ≥ 0 and R(t) ≥ 0, ∀ t ∈ [0, κ) with 0 < κ ≤ +∞.  Theorem 2 Solutions of system (1) starting from R6+ are bounded with time. Proof

Let, N (t) = S(t) + A(t) + I (t) + Q(t) + H (t) + R(t) dN ∴ =  − d N − μ1 I − μ2 H dt ≤  − dN     −dt e ⇒ 0 < N (t) ≤ + N (0) − d d

Here N (0) is total population size at initial time.  So, 0 < lim N (t) ≤ . The solutions of the system remain in the region: t→∞ d    6 . 

= (S, A, I, Q, H, R) ∈ R+ : 0 < N (t) ≤ d

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4 Equilibrium Analysis System (1) has a disease-free equilibrium point (DFE) E 0 (S0 , 0, 0, 0, 0, 0), where  S0 = and an endemic equilibrium point E ∗ (S ∗ , A∗ , I ∗ , Q ∗ , H ∗ , R ∗ ). d

4.1 Basic Reproduction Number (R0 ) Basic reproduction number R0 is obtained by the process developed by van den Driessche and Watmough [30]. Consider, x ≡ (A, I, Q, H ). Denote, α0 = d + σ + η, α1 = d + μ1 + ρ1 + ρ2 + ρ3 , α2 = d + ξ1 + ξ2 and α3 = γ + d + μ2 . Then we have: dx = F(x) − ν(x), dt ⎞ ⎞ ⎛ ⎛ α0 A β(I + p1 A + p2 Q + p3 H )S ⎟ ⎟ ⎜ ⎜ 0 −σ A + α1 I ⎟, ⎟ , ν(x) = ⎜ F(x) = ⎜ ⎠ ⎠ ⎝ ⎝ −ρ1 I + α2 Q 0 −ρ2 I − ξ1 Q + α3 H 0 where F(x) and ν(x) contain the compartment containing new infection term and other terms respectively. So, at the disease-free equilibrium E 0 = (S0 , 0, 0, 0, 0, 0) we have ⎛ ⎜ F = (DF(x)) E 0 = ⎜ ⎝

⎞ ⎛ βp1 S0 β S0 βp2 S0 βp3 S0 α0 0 0 ⎟ ⎜ −σ α1 0 0 0 0 0 ⎟ ⎜ ; V = (Dν(x)) E 0 = ⎝ 0 0 0 0 ⎠ 0 −ρ1 α2 0 0 0 0 0 −ρ2 −ξ1

⎞ 0 0 ⎟ ⎟ 0 ⎠ α3

The spectral radius of the next generation matrix F V −1 is R0 and is given by: R0 =

β S0 [ p1 α1 α2 α3 + σ α2 α3 + p2 σρ1 α3 + p3 σ (ρ1 ξ1 + ρ2 α2 )] α0 α1 α2 α3

(2)

Endemic equilibrium point E ∗ (S ∗ , A∗ , I ∗ , Q ∗ , H ∗ , R ∗ ) Consider, α0 = d + σ + η, α1 = d + μ1 + ρ1 + ρ2 + ρ3 , α2 = d + ξ1 + ξ2 and α3 = γ + d + μ2 . From system (1), we have  − β(I ∗ + p1 A∗ + p2 Q ∗ + p3 H ∗ )S ∗ − d S ∗ β(I ∗ + p1 A∗ + p2 Q ∗ + p3 H ∗ )S ∗ − (d + σ + η)A∗ σ A∗ − (ρ1 + ρ2 + ρ3 )I ∗ − (d + μ1 )I ∗ ρ1 I ∗ − (ξ1 + ξ2 )Q ∗ − d Q ∗ ∗ ρ2 I + ξ1 Q ∗ − γ H ∗ − (d + μ2 )H ∗ ρ3 I ∗ + η A∗ + ξ2 Q ∗ + γ H ∗ − d R ∗

= 0, = 0, = 0, = 0, = 0, = 0.

(3)

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Solving these equations, we get S ∗ =

221

S0 α1 I ∗ ρ1 I ∗ , A∗ = , H∗ = , Q∗ = R0 σ α2

(ρ1 ξ1 + ρ2 α2 )I ∗ , α2 α3    1 I ∗ ηα1 γ (ρ1 ξ1 + ρ2 α2 ) σ ξ2 ρ1 , I∗ = 1− . So, + ρ3 + R∗ = + d σ α2 α2 α3 α0 α1 R0 I ∗ > 0 when R0 > 1.

Theorem 3 System (1) contains one unique    (i) disease-free equilibrium (DFE) E 0 , 0, 0, 0, 0, 0 for any parametric values d and (ii) endemic equilibrium point E ∗ (S ∗ , A∗ , I ∗ , Q ∗ , H ∗ , R ∗ ) for R0 > 1.

5 Sensitivity Analysis The basic reproduction number R0 depends on different parameters such as recruitment rate (), disease transmission rate from symptomatically infected (β), reduction factors of infectivity by asymptomatically exposed, quarantined and hospitalised people ( p1 , p2 , p3 ), disease related death rates (μ1 , μ2 ), natural death rate (d), moving rates of asymptomatically exposed people into symptomatically infected and recovered classes (σ, η), progression rates of symptomatically infected people into quarantined, hospitalised and recovered classes (ρ1 , ρ2 , ρ3 ), progression rates of quarantined people into hospitalised and recovered classes (ξ1 , ξ2 ) and progression rate of hospitalised people to recovered class (γ ). Among all these parameters, we can control β, p1 , p2 , p3 , ρ1 , ρ2 . β S0 [ p1 α1 α2 α3 + σ α2 α3 + p2 σρ1 α3 + p3 σ (ρ1 ξ1 + ρ2 α2 )] Now, R0 = α0 α1 α2 α3  where S0 = , α0 = d + σ + η, α1 = d + μ1 + ρ1 + ρ2 + ρ3 , α2 = d + ξ1 + ξ2 d and α3 = γ + d + μ2 . From the expression of R0 : ∂ R0 ∂β ∂ R0 ∂ p1 ∂ R0 ∂ p2 ∂ R0 ∂ p3 ∂ R0 ∂ρ1 ∂ R0 ∂ρ2

= = = = = =

S0 [ p1 α1 α2 α3 + σ α2 α3 + p2 σρ1 α3 + p3 σ (ρ1 ξ1 + ρ2 α2 )] > 0 α0 α1 α2 α3 β S0 >0 α0 β S0 σρ1 >0 α0 α1 α2 βσ S0 (ρ1 ξ1 + ρ2 α2 ) >0 α0 α1 α2 α3 β S0 [ p2 σ α3 (d + μ1 + ρ2 + ρ3 ) + p3 σ {ξ1 (d + μ1 + ρ2 + ρ3 ) − ρ2 α2 }] α0 α12 α2 α3 β S0 [ p3 σ α2 (d + μ1 + ρ1 + ρ3 ) − ( p3 σ ξ1 ρ1 + σ α2 α3 + p2 σρ1 α3 )] α0 α12 α2 α3

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Computing the normalized forward sensitivity index for the parameters β, p1 , p2 , p3 , ρ1 and ρ2 by the method of Arriola and Hyman we have [31]:  β =   p1 =   p2 =   p3 =   ρ1 =   ρ2 =

∂ R0 R0 ∂β β ∂ R0 R0 ∂ p1 p1 ∂ R0 R0 ∂ p2 p2 ∂ R0 R0 ∂ p3 p3 ∂ R0 R0 ∂ρ1 ρ1 ∂ R0 R0 ∂ρ2 ρ2



=



=



=



=



=



=

β ∂ R0 R0 ∂β p1 ∂ R0 R0 ∂ p1 p2 ∂ R0 R0 ∂ p2 p3 ∂ R0 R0 ∂ p3 ρ1 ∂ R0 R0 ∂ρ1 ρ2 ∂ R0 R0 ∂ρ2

 =1 

 p1 α1 α2 α3 0. Theorem 5 The endemic equilibrium point E ∗ of system (1) is LAS for R0 > 1 when the conditions (i) and (ii) are satisfied. Proof The Jacobian matrix at endemic equilibrium point E ∗ is given as: ⎞ ⎛ a11 a12 a13 a14 a15 0 ⎜ a21 a22 a23 a24 a25 0 ⎟ ⎟ ⎜ ⎜ 0 a32 a33 0 0 0 ⎟  ⎟ J E ∗ = ⎜ ⎜ 0 0 a43 a44 0 0 ⎟ ⎟ ⎜ ⎝ 0 0 a53 a54 a55 0 ⎠ 0 a62 a63 a64 a65 a66

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where a11 = −β(I ∗ + p1 A∗ + p2 Q ∗ + p3 H ∗ ) − d, a12 = −βp1 S ∗ , a13 −β S ∗ , a14 = −βp2 S ∗ , a15 = −βp3 S ∗ , a21 = β(I ∗ + p1 A∗ + p2 Q ∗ p3 H ∗ ), a22 = βp1 S ∗ − α0 , a23 = β S ∗ , a24 = βp2 S ∗ , a25 = βp3 S ∗ , a32 σ, a33 = −α1 , a43 = ρ1 , a44 = −α2 , a53 = ρ2 , a54 = ξ1 , a55 = −α3 , a62 η, a63 = ρ3 , a64 = ξ2 , a65 = γ , a66 = −d. Characteristic equation of J  E ∗ is λ6 +Q 1 λ5 +Q 2 λ4 +Q 3 λ3 +Q 4 λ2 +Q 5 λ+Q 6 0, where

= + = = =

Q 1 = α0 + α1 + α2 + α3 − a11 − βp1 S ∗ , Q 2 = d(α0 + α1 + α2 + α3 − a11 ) + α0 (α1 + α2 + α3 ) − a11 (α0 + α1 + α2 + α3 ) + α1 (α2 + α3 ) + α2 α3 − βp1 S ∗ (2d + α1 + α2 + α3 ), Q 3 = −βp1 S ∗ [d(d + α1 + α2 + α3 ) + α1 (d + α2 + α3 ) + α2 (d + α3 ) + dα3 ] − σβ S ∗ (ρ1 p2 + ρ2 p3 ) − σβ S ∗ (d + α2 + α3 − a11 a21 ) + d[α0 (α1 + α2 + α3 − a11 ) + α1 (α2 + α3 − a11 ) + α2 (α3 − a11 ) − α3 a11 ] + α0 [α1 (α2 + α3 − a11 ) + α2 (α3 − a11 ) − α3 a11 ] + α1 α2 (α3 − a11 ) − α3 a11 (α1 + α2 ), Q 4 = −βσ d S ∗ [d + α2 + α3 + p2 ρ1 + p3 ρ2 ] − β S ∗ [ p1 α1 α2 α3 + σ α2 α3 + σ p2 ρ1 α3 + σ p3 (ρ1 ξ1 + α2 ρ2 )] − β S ∗ d p1 [α1 (d + α2 + α3 ) + α2 (d + α3 ) + dα3 ] − β S ∗ dσ ( p2 ρ1 + p3 ρ2 ) − β S ∗ d p1 (α1 α2 + α2 α3 + α3 α1 ) − β S ∗ dσ (α2 + α3 ) + d[α0 α1 (α2 + α3 − a11 ) + α2 (α3 − a11 )(α0 + α1 + α0 α1 ) − α3 a11 (α0 + α1 + α2 + α0 α1 + α0 α2 + α1 α2 )],   d R0 S ∗ Q 5 = dα0 α1 α2 α3 1 − − βσ S ∗ d 2 {σ (α2 + α3 ) + p2 ρ1 + p3 ρ2 } − β S ∗ d 2 p1 (α1 α2 + α2 α3  R S∗ + α3 α1 ) − d 2 α0 α1 α2 α3 0 − βσ d 2 S ∗ (α2 + α3 ) + dα0 (α1 α2 α3 − α1 α2 − α2 α3 − α3 α1 )  − (d + α0 )α1 α2 α3 a11 ,

  da21 R0 d R0 S ∗ Q 6 = dα0 α1 α2 α3 − a11 1 − .  

Let us consider:

  Q1 1      Q1 1 0      Q1 1    , 3 =  Q 3 Q 2 Q 1  , 4 =  Q 3 Q 2 1 = Q 1 , 2 =   Q5 Q4    Q3 Q2   Q5 Q4 Q3   0 Q6    Q1 1 0 0 0     Q3 Q2 Q1 1 0     5 =  Q 5 Q 4 Q 3 Q 2 Q 1  , 6 = Det (J  E ∗ ) = Q 6 .  0 Q6 Q5 Q4 Q3     0 0 0 Q6 Q5 

0 Q1 Q3 Q5

 0  1  , Q 2  Q4 

According to Routh-Hurwitz criterion, E ∗ is locally asymptomatically stable (LAS) when i > 0 for i = 1, 2, 3, 4, 5, 6, i.e., (i) Q i > 0 for i = 1, 6; (ii) i > 0 for i = 2, 3, 4, 5.



6.2 Global Stability Theorem 6 Disease-free equilibrium E 0 of system (1), if locally asymptotically stable (LAS), is also globally asymptotically stable (GAS).

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Proof Consider a Lyapunov function V1 (t) with positive constants m 1 , m 2 , m 3 , m 4 as:   S V1 (t) = m 1 S − S0 − S0 ln + m1 A + m2 I + m3 Q + m4 H S0 Time derivative of V1 along the solutions of system (1) is given by   d V1 S0 = m1 1 − [ − β(I + p1 A + p2 Q + p3 H )S − d S] dt S + m 1 [β(I + p1 A + p2 Q + p3 H )S − α0 A] + m 2 [σ A − α1 I ] + m 3 [ρ1 I − α2 Q] + m 4 [ρ2 I + ξ1 Q − α3 H ] d = − (S − S0 )2 m 1 + β S0 m 1 I + βp1 S0 m 1 A + βp2 S0 m 1 Q + βp3 S0 m 1 H S − m 1 α0 A + m 2 σ A − m 2 α1 I + m 3 ρ1 I − m 3 α2 Q + m 4 ρ2 I + m 4 ξ1 Q − m 4 α3 H

Consider, m 1 = α1 α2 α3 , m 2 = β S0 [α2 α3 + p2 α3 ρ1 + p3 (ρ1 ξ1 + ρ2 α2 )], m 3 = β S0 α1 ( p2 α3 + p3 ξ1 ) and m 4 = βp3 S0 α1 α2 . Then we have d V1 dα1 α2 α3 =− (S − S0 )2 + α0 α1 α2 α3 (R0 − 1)A dt S  d V1 d V1  So, = 0. Therefore, by Lyapunov < 0 when R0 < 1. Moreover, dt dt  E 0 LaSalle’s theorem [32], E 0 is GAS when it is LAS. Because in the limit R(t) is dR given by the solution of  = −d R and so lim R(t) = 0. t→∞ dt Theorem 7 Endemic equilibrium point E ∗ of system (1) is globally asymptomatically stable (GAS) when d > max{βp1 S ∗ , βp2 S ∗ }, β S ∗ < < d + μ2 hold in the region  = d + μ1 and βp3 S ∗ (S, A, I, Q, H, R) ∈ R6+ : βp1 S A∗ > 3α0 A∗ + α1 I ∗ + α2 Q ∗ + α3 H ∗ + d R ∗ . Proof Consider a Lyapunov function V2 (t) as:       S A I V2 (t) = S − S ∗ − S ∗ ln ∗ + A − A∗ − A∗ ln ∗ + I − I ∗ − I ∗ ln ∗ + S A I       Q H R Q − Q ∗ − Q ∗ ln ∗ + H − H ∗ − H ∗ ln ∗ + R − R ∗ − R ∗ ln ∗ Q H R

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Time derivative of V2 along the solutions of system (1) is given by   d V2 S∗ = 1− [ − β(I + p1 A + p2 Q + p3 H )S − d S] dt S     I∗ A∗ [β(I + p1 A + p2 Q + p3 H )S − α0 A] + 1 − [σ A − α1 I ] + 1− A I     ∗ ∗ Q H + 1− [ρ1 I − α2 Q] + 1 − [ρ2 I + ξ1 Q − α3 H ] Q H   ∗ R [ρ3 I + η A + ξ2 Q + γ H − d R] + 1− R   ∗ S [−β(S − S ∗ )(I ∗ + p1 A∗ + p2 Q ∗ + p3 H ∗ ) − β S(I − I ∗ ) − βp1 S(A − A∗ ) = 1− S   A∗ − βp2 S(Q − Q ∗ ) − βp3 S(H − H ∗ ) − d(S − S ∗ )] + 1 − [β(S − S ∗ )(I ∗ + p1 A∗ + p2 Q ∗ + p3 H ∗ ) A − β S(I − I ∗ ) − βp1 S(A − A∗ ) − βp2 S(Q − Q ∗ ) − βp3 S(H − H ∗ ) − α0 (A − A∗ )]     I∗ Q∗ + 1− [σ (A − A∗ ) − α1 (I − I ∗ )] + 1 − [ρ1 (I − I ∗ ) − α2 (Q − Q ∗ )] I Q     H∗ R∗ [ρ2 (I − I ∗ ) + ξ1 (Q − Q ∗ ) − α3 (H − H ∗ )] + 1 − [ρ3 (I − I ∗ ) + η(A − A∗ ) + 1− H R + ξ2 (Q − Q ∗ ) + γ (H − H ∗ ) − d(R − R ∗ )]

Let, SS∗ = x, AA∗ = y, II∗ = z, QQ∗ = u, HH∗ = v and RR∗ = w. Also the steady state of system (1) at E ∗ gives  − β(I ∗ + p1 A∗ + p2 Q ∗ + p3 H ∗ )S ∗ − d S ∗ = 0, β(I ∗ + p1 A∗ + p2 Q ∗ + p3 H ∗ )S ∗ − (d + σ + η)A∗ = 0, σ A∗ − (ρ1 + ρ2 + ρ3 )I ∗ − (d + μ1 )I ∗ = 0, ρ1 I ∗ − (ξ1 + ξ2 )Q ∗ − d Q ∗ = 0, ρ2 I ∗ + ξ1 Q ∗ − γ H ∗ − (d + μ2 )H ∗ = 0, ρ3 I ∗ + η A∗ + ξ2 Q ∗ + γ H ∗ − d R ∗ = 0. Here α0 = d + σ + η, α1 = d + μ1 + ρ1 + ρ2 + ρ3 , α2 = d + ξ1 + ξ2 and α3 = γ + d + μ2 . ∴

    z d V2 1 1 = β S ∗ (I ∗ + p1 A∗ + p2 Q ∗ + p3 H ∗ ) 2 − x − + βSI∗ 1 − z + − dt x x x       y u v 1 1 1 ∗ ∗ ∗ + βp2 S Q 1 − u + − + βp3 S H 1 − v + − + βp1 S A 1 − y + − x x x x x x     x 1 1 ∗ ∗ ∗ ∗ ∗ ∗ + dS 2 − x − + β S (I + p1 A + p2 Q + p3 H ) x − 1 − + x y y       z 1 u 1 1 ∗ ∗ ∗ z−1− + + βp1 S A y + − 2 + βp2 S Q u − 1 − + + βSI y y y y y       v 1 y 1 1 ∗ ∗ ∗ + α0 A 2 − y − +σA y−1− + + βp3 S H v − 1 − + y y y z z       1 z 1 1 ∗ ∗ ∗ + α1 I 2−z− z−1− + + ρ1 I + α2 Q 2 − u − z u u u       z u 1 1 1 ∗ ∗ ∗ z−1− + + ξ1 Q u − 1 − + + α3 H 2 − v − + ρ2 I v v v v v       z y u 1 1 1 + η A∗ y − 1 − + ξ2 Q ∗ u − 1 − + + + + ρ3 I ∗ z − 1 − w w w w w w     v 1 1 + γ H∗ v − 1 − + + d R∗ 2 − w − w w w

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227

    z d V2 1 x 1 1 z 1 + βSI∗ = β S ∗ (I ∗ + p1 A∗ + p2 Q ∗ + p3 H ∗ ) 1 − − + − − + dt x y y x x y y       1 1 1 u 1 1 v 1 y u v ∗ ∗ ∗ − − + − − + + βp1 S A −1 + − + + βp2 S Q + βp3 S H x x y x x y y x x y y       1 1 y ∗ ∗ ∗ 1+y−z− + α0 A 2 − y − + α1 I + dS 2 − x − x y z       z z u ∗ ∗ + ρ1 I z + 1 − − u + ρ2 I z + 1 − − v + ξ1 Q ∗ u + 1 − − v u v v       z y u + ρ3 I ∗ z + 1 − − w + η A∗ y + 1 − − w + ξ2 Q ∗ u + 1 − −w w w w   v + γ H∗ v + 1 − −w w   1 ≤ d S∗ 2 − x − + [3α0 A∗ + α1 I ∗ + α2 Q ∗ + α3 H ∗ + d R ∗ − βp1 S A∗ ] x + A∗ y(βp1 S ∗ − d) + I ∗ z{β S ∗ − (d + μ1 )} + Q ∗ u(βp2 S ∗ − d) + H ∗ v{βp3 S ∗ − (d + μ2 )}

d V2 ≤ 0 in the region  provided following conditions are satisfied: dt (i) d > max{βp1 S ∗ , βp2 S ∗ }, (ii) β S ∗ < (d + μ1 ) and (iii) βp3 S ∗ < (d + μ2 ). d V2  Moreover, = 0. So, by Lyapunov LaSalle’s theorem [32], E ∗ is globally dt  E ∗ asymptomatically stable in the interior of  subject to the stated parametric conditions.  So,

7 Bifurcation Analysis at R0 = 1 The result of the central manifold theory, discussed by Castillo-Chavez ´ and Song [33], is stated in the following theorem: Theorem 8 Consider the following system of ODEs with a parameter :   dX = f (X, ), f : Rn × R → Rn and f ∈ C 2 Rn × R . dt Let O be taken as an equilibrium point of the mentioned system with f (O, ) = O for all . Let us further assume (I) B = D X f (O, 0) = ( ∂∂xfij (O, 0)) be the linearization matrix of the mentioned system at the equilibrium O and  evaluated at 0. B has a simple zero eigenvalue and other eigenvalues of the matrix have negative real parts. (II) B contains a right eigenvector w which is non-negative and also a left eigenvector v corresponding to the zero eigenvalue. If f k is considered to be the kth component of f and a=

n  k,i, j=1

vk wi w j

n  ∂ 2 fk ∂ 2 fk (O, 0), b = vk wi (O, 0), ∂ xi ∂ x j ∂ xi ∂ k,i=1

then the local dynamics of a system around O is determined by the sign of a and b.

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1. a > 0, b > 0. O is locally asymptotically stable and there exists a positive unstable equilibrium for  < 0 and || 1. Further O is unstable and there exists a negative and locally asymptotically stable equilibrium for 0 <  1. 2. a < 0, b < 0. O is unstable for  < 0 and || 1. Further O is locally asymptotically stable, and there exists a positive unstable equilibrium for 0 <  1. 3. a > 0, b < 0. O is unstable, and there exists a locally asymptotically stable negative equilibrium for  < 0 and || 1. Further O is stable, and a positive unstable equilibrium appears for 0 <  1. 4. a < 0, b > 0. O changes its stability from stable to unstable when  changes its sign from negative to positive. As a result, a negative unstable equilibrium point turns into positive and locally asymptotically stable. The components of the right eigenvector w may not be non-negative and it depends on the positivity of corresponding component of equilibrium Remark 1 in [33]. Even if some components of w become negative, then also the theorem can be applied, though on that case one need to compare w with the equilibrium. The comparison is necessary as the general parameterization of the center manifold theory before changing the coordinate is W θ = {X 0 + θ (t)y + k(θ, ) : v · k(θ, ), |θ | ≤ θ0 , θ (0) = 0} provided that X 0 is b a non-negative equilibrium of system (usually X 0 is the DFE). Hence, X 0 −2 >0 a requires that w( j) > 0 whenever X 0 ( j) = 0. If X 0 ( j) > 0, then w( j) need not be positive. Let us redefine S = x1 , A = x2 , I = x3 , Q = x4 , H = x5 and R = x6 , then the system (1) can be rewritten as: d x1 dt d x2 dt d x3 dt d x4 dt d x5 dt d x6 dt

=  − β(x3 + p1 x2 + p2 x4 + p3 x5 )x1 − d x1 ≡ f 1 , = β(x3 + p1 x2 + p2 x4 + p3 x5 )x1 − α0 x2 ≡ f 2 , = σ x 2 − α1 x 3 ≡ f 3 , (5) = ρ1 x 3 − α 2 x 4 ≡ f 4 , = ρ2 x3 + ξ1 x4 − α3 x5 ≡ f 5 , = ρ3 x3 + ηx2 + ξ2 x4 + γ x5 − d x6 ≡ f 6 .

We have considered  = β as bifurcation parameter for R0 = 1. Thus at  = ∗ = α0 α1 α2 α3 β ∗ , R0 = 1 gives β ∗ = . S0 [ p1 α1 α2 α3 + σ α2 α3 + p2 σρ1 α3 + p3 σ (ρ1 ξ1 + ρ2 α2 )]  The linearized matrix of the model system (5) at E 0 , 0, 0, 0, 0, 0 with bifurd

229

Analysis of a COVID-19 Model Implementing Social Distancing …

cation parameter β = β ∗ is given by ⎛ −d −βp1 S0 −β S0 −βp2 S0 −βp3 S0 ⎜ 0 βp1 S0 − α0 β S0 βp2 S0 βp3 S0 ⎜ ⎜ 0 0 0 σ −α1 J | E0 = ⎜ ⎜ 0 −α 0 0 ρ 1 2 ⎜ ⎝ 0 ξ1 −α3 0 ρ2 ξ2 γ 0 η ρ3

⎞ 0 0 ⎟ ⎟ 0 ⎟ ⎟ 0 ⎟ ⎟ 0 ⎠ −d

 Two eigenvalues of J  E are λ1 = λ2 = −d and other four eigenvalues are roots of 0 the following equation: λ4 + P1 λ3 + P2 λ2 + P3 λ + P4 = 0, where, P1 = α0 + α1 + α2 + α3 − βp1 S0 , P2 = α0 α2 + α1 α3 + (α0 + α2 )(α1 + α3 ) − βp1 S0 (α1 + α2 + α3 ) − β S0 σ , P3 = α0 α2 (α1 + α3 ) + α1 α3 (α0 + α2 ) − βp1 S0 {α1 α3 + α2 (α1 + α3 )} − β S0 σ (α2 + α3 ) − β S0 σ ( p2 ρ1 + p3 ρ2 ) and P4 = α0 α1 α2 α3 (1 − R0 ). So, J | E 0 (β ∗ ) has a zero eigenvalue at R0 = 1 as P4 | R0 =1 = 0. The right eigenvector corresponding to the zero eigenvalue of J | E 0 (β ∗ ) is denoted by w = (w1 , w2 , w3 , w4 , w5 , w6 )T , where w1 = −α0 α1 α2 α3 , w2 = dα1 α2 α3 , w3 = dσ α2 α3 , w4 = ρ1 dσ α3 , w5 = dσ (ρ1 ξ1 + ρ2 α2 ) and w6 = ηα1 α2 α3 + ρ3 σ α2 α3 + σ ξ2 ρ1 α3 + γ σ (ρ1 ξ1 + ρ2 α2 ). Also, the left eigenvector of J˜| E 0 (β ∗ ) corresponding to zero eigenvalue is v = (v1 , v2 , v3 , v4 , v5 , v6 )T , where v1 = 0, v2 = α1 α2 α3 , v3 = β S0 [α2 α3 + ρ1 (ξ1 p3 + p2 α3 ) + α2 ρ2 p3 ], v4 = β S0 α1 (ξ1 p3 + p2 α3 ), v5 = βp3 S0 α1 α2 and v6 = 0. Hence a=

n 

vk wi w j

k,i, j=1

b=

n  k,i=1

vk wi

∂ 2 fk (E 0 ) = 2βv2 w1 [ p1 w2 + w3 + p2 w4 + p3 w5 ] < 0, (as w1 < 0), ∂ xi ∂ x j

∂ 2 fk (E 0 ) = v2 S0 [ p1 w2 + w3 + p2 w4 + p3 w5 ] > 0 ∂ xi ∂

So, we have the following theorem: Theorem 9 System (1) undergoes a transcritical (forward) bifurcation around the disease-free equilibrium E 0 at R0 = 1 taking β as the bifurcation parameter.

8 Numerical Results Without Implementing Control Strategy Numerical pictures support the analytical results of a system to get a proper insight into the dynamical behaviour. India is the second most populated country in the world with 137.8 million people (according to the data up to June 2020). The annual birth rate in the country is about 18.2 births/1000 people and so, we are taking S(0) = 1.378 × 109 and  = 7 × 104 by applying unit conversion from year to day.

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Table 1 Parameter values used for numerical simulation of system (1) Parametric values β

7.04 × 10−10



7 × 104

p1

0.1

p2

0.005

p3

0.00002

d

0.0002

mu 1

0.02

μ2

0.000109

γ

0.00429

σ

0.15

η

0.04

ρ1

0.07

ρ2

0.001376

ρ3

0.1

ξ1

0.001376

ξ2

0.01

According to the data of CSSE at John Hopkins University and also Worldometers database India has total 2,904,329 confirmed COVID-19 cases till 20th August, 2020 [10,11]. Also total activated cases, recovered cases and death cases are 691,413; 2,157,941 and 54,975 respectively till that date. So, the unit conversion from month to day gives μ2 as 0.000109 and γ as 0.00429. As the count of total active cases is 691,413 among 2,904,329 till 20th; so, we get ρ2 as 0.001376 approximately [14]. In this model, the newly infected cases (from asymptomatically exposed class) per day is noted by β1 S I (where β1 = βp1 ). Up to June, total documented COVID19 cases (I ) is approximately 585,792, the population in India (S) in August is 1,381,863,561 ≈ 1.381 × 109 , the new human cases (β1 S I ) from June to 20th August is about 2,318,537 (total 2,904,329 COVID-19 cases up to 20th August) [13,14]. Hence, we get β1 ≈ 7.04 × 10−11 by doing the unit conversion from month to day. Assuming p1 as 0.1, we have β as 7.04 × 10−10 . As per the data of 20th August, the COVID-19 infected cases up to June is 220,546 [10,11,14]. Hence, I (0) is taken 20,000 approximately. Table 1 contains the parametric values used in numerical simulation. Other initial population sizes are taken as follows: A(0) = 5000, Q(0) = 5000, H (0) = 500 and R(0) = 4 × 105 . Figure 2 depicts that the trajectory starting from mentioned initial point tends to DFE E 0 (3.5 × 108 , 0, 0, 0, 0, 0) for β = 7.04 × 10−11 and the basic reproduction number R0 is 0.1174591448 in this case which lies below unity. Therefore, we get a disease-free system for the mentioned value of β. Now, for increasing value of β (mentioned in Table 1), the trajectory starting from the same initial point converges to the endemic equilibrium E ∗ (2.9798 × 108 , 54704.5764, 42832.5389, 259008.0961, 90309.3529, 4.7245 × 107 ) (see Fig. 3). And we get R0 = 1.174591448 > 1 in this case indicating the presence of infection in the system. The DFE E 0 loses stability when β crosses a threshold value β[T C] and becomes unstable for β > β[T C] . Figure 4 depicts that the system exhibits a transcritical bifurcation at β = β[T C] = 5.9936 × 10−10 around E 0 . Moreover, the natural death rate at each compartment (d) can also control the system dynamics as the system around DFE becomes stable when d exceeds a threshold value d[T C] and we get an

Analysis of a COVID-19 Model Implementing Social Distancing …

231

Fig. 2 Stability of the system around DFE E 0

unstable system when d lies below d[T C] . Hence, system (1) exhibits a transcritical bifurcation around E 0 at d[T C] = 0.000235. Figure 5 analyses how the system parameters affect the virus transmission on COVID-19. The figure shows that the parameter β is most sensitive than the others such as p1 , p2 , p3 , ρ1 and ρ2 to curb the growth of the disease transmission. Increase of β, p1 , p2 and p3 increase the value of R0 and on the other hand, increasing value of ρ1 and ρ2 lead to a decrease in value of R0 . It means if the disease transmission from asymptomatically exposed, symptomatically infected, quarantined and hospitalised people increase, then the disease invade into a system with time. On the other hand, if more people from symptomatically infected class move to quarantine or hospitals without ignoring the symptoms, then the risk of contracting the disease decreases. Now if calculate the normalised forward sensitivity indices for each of β, p1 , p2 , p3 , ρ1 and ρ2 , then we get β = 1,  p1 = 0.1103,  p2 = 0.0261,  p3 = 0.0000364, ρ1 = −0.2989 and ρ2 = −0.006385. So, the sensitivity index for p1 indicates that if p1 is increased by 10%, then R0 also increased by 1.103%. On the other hand, ρ1 signifies that 10% increment in ρ1 will decrease R0 by 2.989%. The most sensitive parameter is disease transmission rate from symptomatically infected people β which gives a positive impact on R0 . In the proposed model, β is the disease transmission rate per contact by a symptomatically infected individual whereas p1 and p2 are the reduction factors of infectivity by asymptomatically exposed class (A), quarantined people (Q) compared to symptomatically infected population. Figure 6 shows that the overall infected individuals increase with the increase of either p1 or p2 . Also, if we compare Fig. 6a, b, it is observed that the count of the infected population is higher when p2 is increased instead of p1 . If we calculate the symp-

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8

x 10 14

4

x 10

3.5

12

3

10

2.5 2

S

A

8 6

1.5

4

1

2

0.5

0

0 0

1

x 10 2.5

2

3

4 t

5

6

7

8 4 x 10

8

3

0

1

x 10

2

3

4 t

2

3

4

5

6

7

8 4 x 10

5

6

7

8 4 x 10

8

2.5

2

2

I

Q

1.5

1.5

1

1

0.5

0.5

0

0

0

3

1

x 10

2

3

4 t

5

6

7

0

8 4 x 10

t

7

x 10 12 10

2

8

R

2.5

H

1.5

4

0.5

2

0 1

2

3

4 t

5

6

7

0

8 4 x 10

8

6

1

0

1

1

0

2

5

4 t

3

7

6

8 4 x 10

Fig. 3 Stability of the system around E ∗ 8

( a)

x 10 14

Susceptible Population (S)

Susceptible Population (S)

8

x 10 3.5 3.5 3.5 3.5 3.5 Stable Branch Unstable Branch 3.5 3.5 Bifurcation Threshold 3.5 3.5 3.5 3.5 1 2 3 4 5 6 7 8 9 10 −10 x 10 β

12 Unstable Branch

10 8 6 4 Stable Branch

2 Bifurcation Threshold

0 0.5

1

1.5

2

2.5

3

3.5

4

d

( b)

Fig. 4 Transcritical bifurcation around E 0 taking a β and b d as bifurcation parameters

4.5 5 −4 x 10

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2

1.6 1.5

R0

R0

1.4 1.2

1

R0 = 1

0.5

6

β

0.8

10

8

0.3

0.2

0.5

0.4

p

1

2.5

5

2

3

R0

0

0.1

−10

x 10

4

R

R0 = 1

1

1.5 2

R0 = 1

R0 = 1

1 0.1

0.3

0.2

0.4

1 0.1

0.5

2

1.3

1.8

1.2

0.4

0.5

1.1

1.6

R0 = 1

1

1.4

R0

R0

0.3

0.2 p3

p2

1.2 R0 = 1

1

0.9 0.8 0.7

0.8

0.6

0.6

0.5

0.4

0.4 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

ρ

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

ρ2

1

Fig. 5 Plots of basic reproduction number R0 with the variation of β, p1 , p2 , p3 , ρ1 and ρ2

tomatically infected population (I ) for different values of ( p1 , p2 ), then we get I ∗ |( p1 =0.1) = 42836, I ∗ |( p1 =0.5) = 117935, I ∗ |( p1 =0.9) = 157775 and I ∗ |( p2 =0.1) = 124223, I ∗ |( p2 =0.5) = 219748, I ∗ |( p2 =0.9) = 244950. So, if the transmission rates from asymptotically exposed people as well as quarantined people decrease, then the count of the overall infected population significantly reduce. On the other hand, ρ1 and ρ2 denote the rates at which symptomatically infected population move to quarantine and hospitals respectively. If more people move to quarantine or even hospitals without neglecting the severity, then the disease progression decline. Figure 7 shows the decrement of total infected individuals with time with the increase of ρi for i = 1, 2 and consequently, depicts the seriousness of being quarantined and hospitalized to reduce disease transmission. Shifting of more infected people into quarantine class (ρ1 ) or hospitals (ρ2 ) can lower the chance of disease transmission and consequently, the overall infected population reduced in the system. Also, if we calculate the symptomatically infected population (I ) for different values of (ρ1 , ρ2 ), then we get I ∗ |(ρ1 =0.001) = 187850, I ∗ |(ρ1 =0.01) = 159422 and I ∗ |(ρ2 =0.001) = 43345, I ∗ |(ρ2 =0.01) = 31639. So, it is observed that increasing ρ2 significantly reduce the infection than ρ1 . It is justifiable because, for hospitalisation, the chances of interaction with others are significantly lower. In Fig. 8, the symptomatically infected population is plotted with increasing quarantine rate (ρ1 ) for different values of p2 . In this figure, it is observed that the count of symptomatically infected individuals first increases monotonically when the value of p2 increases along with increasing ρ1 . But for a larger value of ρ1 , the infected population decreases following an inclination, i.e., I increase up to a certain level

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Fig. 6 The effect of a p1 and b p2 on the symptomatically infected population (I )

Fig. 7 The effect of a ρ1 and b ρ2 on the symptomatically infected population (I )

and then starts to decrease after reaches to its maximum. If the people of the infected class move to quarantine at a higher rate, then the probability of disease transmission becomes lower enough and it reduces the overall infection. For increasing ρ1 , a smaller value of p2 always gives a lesser number of infected population. The scenario in Fig. 8 depicts that if most of the people from symptomatically infected class (I ) move to quarantine, then ultimately the count of the infected population starts to reduce even for higher value of p2 . So, precisely, a smaller value of p2 with an increasing value of ρ1 significantly reduce the infected population in the system.

9 Optimal Control Problem We have formulated a corresponding optimal control problem in this section to analyse the impact of proper control policies to reduce the disease burden in a system. Testing and social distancing or isolation is the most useful tool to fight the current pandemic situation. It is the only way to reduce the spread and the impact of the virus. But the asymptomatic transmission of COVID-19 has made controlling the spread of the disease more difficult. The rapid spread of the virus across the World ensures the transmission from asymptomatic persons. Moreover, the virus can be transmitted

Analysis of a COVID-19 Model Implementing Social Distancing …

235

Fig. 8 Plots of symptomatically infected population (I ) with the change of quarantine rate of infected (ρ1 ) for different values of p2

from infected persons with symptoms and even from those infected people who are in quarantine. Though the probability of transmission in the case for quarantined people becomes comparatively lower. Henceforth, the social distancing of the population and the moving rate of the symptomatically infected population into quarantine or hospitals have been considered as the control policies. The analysis is performed to observe the impact of the control policies to reduce the incidence transmission of disease and also to obtain the optimal cost burden. Let us first describe the control strategies one by one. Increase the consciousness for maintaining physical distancing and hygiene: Population, while provided with the necessary information, becomes cautious about disease prevalence and its precautionary measures. Regular updates on live tracking sites and social campaigns act as an important tool to increase the awareness among the population which can impede the transmission to some extent. Government and media sources are providing the news about this pandemic fatality regularly from the very first day. In the case of coronavirus, people becomes infected from asymptomatically exposed class, symptomatic individuals class and even from quarantined class. As COVID-19 is not an airborne disease, so, maintaining physical distancing is the only way to reduce the disease transmission. It is considered that u 1 portion of the susceptible population maintains social distancing and other precautionary measures by using the face masks, implementing enough hygiene etc. Therefore, the disease can only be transmitted among (1 − u 1 )S portion of susceptible individuals due to the close contact. In system (7), u 1 denotes the intensity of maintaining social distancing to reduce the disease transmission where 0 ≤ u 1 ≤ 1. A full violation of social distancing is represented by u 1 = 0 and u 1 = 1 means full maintenance of social distancing. So, u 1 (t) is taken as one control intervention as this may change with the population awareness regarding social distancing and maintaining proper hygiene. Increasing the rate of symptomatically infected population which enters to quarantine class and moves to hospitals: The awareness programs are conducted to aware not only the susceptible population but infected people too. It is a person’s responsibility to consult a medical person or to admit into a hospital if the symptoms are shown in his/her body. Anyone should not ignore it by assuming it as a simple

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cough and cold case. Due to the regular broadcast, the infected population also takes precautionary measures by maintaining proper hygiene at a higher rate and they are advised either to move to quarantine or to admit into the hospitals depending on the physical conditions. Moving to quarantine or admitting to a hospital at an early stage can decrease the disease burden. So, instead of constant values, timedependent quarantined and hospitalisation rate functions u 2 (t) and u 3 (t) respectively are considered in the system with the restrictions 0 ≤ u 2 (t) ≤ 1 and 0 ≤ u 3 (t) ≤ 1. By these control policies, overall recovered individuals of COVID-19 cases would be benefited. Here, 1 denotes when all people move to quarantine or admit into a hospital without ignoring the symptoms and 0 denotes the case when a person becomes careless about his sickness and he neither goes for self-quarantined nor consults a doctor. The main work is to determine optimal control interventions with minimum implemented cost by Pontryagin’s Maximum Principle [40,42]. So, the region for the control interventions u 1 (t), u 2 (t) and u 3 (t) is given as:    = (u 1 (t), u 2 (t), u 3 (t)) | (u 1 (t), u 2 (t), u 3 (t)) ∈ [0, 1] × [0, 1] × [0, 1], t ∈ [0, T f ] ,

where T f is the final time up to which the control policies are executed, and also u i (t) for i = 1, 2, 3 are measurable and bounded functions.

9.1 Deduction of Total Cost Which Needs to Be Minimized (i) Cost incurred in maintaining social distancing and proper hygiene: In order to maintain the physical distancing and necessary hygiene total cost incurred is:  0

Tf



 w2 u 21 (t) dt

Here w2 u 21 (t) denotes the cost for spreading awareness about the necessity of physical distancing and maintaining proper hygiene. Some researchers analyzed the impact of the cost for the mitigation strategies such as awareness programs, self-protective measures etc. and non-linearity up to order two have been recommended [34–36]. We now analyse how physical distancing reduce the disease burden at the current pandemic situation. (ii) Cost involved during quarantine period and at the time of treatment at hospitals: Total cost associated with quarantine and treatment in hospitals for symptomatically infected individuals is given as: 

Tf 0



 w1 I (t) + w3 u 22 (t) + w4 u 23 (t) dt,

where w1 I (t) denotes the cost associated with symptomatically infected population for losing man power [35,37,38]. The term w3 u 22 (t) is the cost at the time of quarantine due to medicines, precautionary measures etc. and w4 u 23 (t) denotes the cost at hospitals due to diagnosis charges, the expenditure of hospitalization etc. The latter two terms consider the opportunity losses including productivity loss due to isolation

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at homes and admittance to hospitals. So, the non-linearity of u 2 (t) and u 3 (t) is taken up to order two [35,37,38]. The following control problem is considered based on previous discussions along with the cost functional J to be minimized:  Tf   w1 I (t) + w2 u 21 (t) + w3 u 22 (t) + w4 u 23 (t) dt (6) J [u 1 (t), u 2 (t), u 3 (t)] = 0

subject to the model system: dS dt dA dt dI dt dQ dt dH dt dR dt

=  − (1 − u 1 (t))β(I + p1 A + p2 Q)S − βp3 H S − d S, = (1 − u 1 (t))β(I + p1 A + p2 Q)S + βp3 H S − (d + σ + η)A, = σ A − u 2 (t)I − u 3 (t)I − ρ3 I − (d + μ1 )I, (7) = u 2 (t)I − (ξ1 + ξ2 )Q − d Q, = u 3 (t)I + ξ1 Q − γ H − (d + μ2 )H, = ρ3 I + η A + ξ2 Q + γ H − d R,

with initial conditions S(0) > 0, A(0) ≥ 0, I (0) ≥ 0, Q(0) ≥ 0, H (0) ≥ 0 and R(0) ≥ 0. We have already considered α0 = (d + σ + η), α2 = (d + ξ1 + ξ2 ) and α3 = (γ + d + μ2 ). The functional J denotes the total incurred cost as stated and the integrand: L(S, A, I, Q, H, R, u 1 (t), u 2 (t), u 3 (t)) = w1 I (t) + w2 u 21 (t) + w3 u 22 (t) + w4 u 23 (t) denotes the cost at time t. Positive parameters w1 , w2 , w3 and w4 are weight constants balancing the units of the integrand [35,37]. The optimal control interventions u ∗1 , u ∗2 and u ∗3 , exist in , mainly minimize the cost functional J . Theorem 10 The optimal control interventions u ∗1 , u ∗2 and u ∗3 in  of the control system (6)–(7) exist such that J (u ∗1 , u ∗2 , u ∗3 ) = min[J (u 1 , u 2 , u 3 )]. Proof In optimal system (7), let consider N = S + A + I + Q + H + R. So,

dN =  − d N − μ1 I − μ2 H dt ≤  − dN     −dt e , ⇒ 0 < N (t) ≤ + N (0) − d d

where N (0) is the total population size at the initial stage.  As t → ∞, 0 < N (t) ≤ . d

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Now the solution of (7) is bounded and also the right hand side of the system are locally Lipschitzianin functions in presence of the control variables in . Therefore, by Picar d − Lindel o¨ f theorem condition, the solution of the optimal system along with implemented optimal control strategies exist in  and is non-zero [39]. The set , in which control variables are defined, is closed and convex. Also, the equations of the stated system can be written in terms of u 1 , u 2 and u 3 along with state variables depending on coefficients. The integrand function L(S, A, I, Q, H, R, u 1 , u 2 , u 3 ) is a convex function on  as the control variables are of order two. Now, L(S, A, I, Q, H, R, u 1 , u 2 , u 3 ) = w1 I + w2 u 21 + w3 u 22 + w4 u 23 ≥ w2 u 21 + w3 u 22 + w4 u 23 Let, w = min(w2 , w3 , w4 ) > 0 and k(u 1 , u 2 , u 3 ) = w(u 21 + u 22 + u 23 ) which is a continuous function. Then, L(S, A, I, Q, H, R, u 1 , u 2 , u 3 ) ≥ k(u 1 , u 2 , u 3 ). Here, k is continuous and ||(u 1 , u 2 , u 3 )||−1 k(u 1 , u 2 , u 3 ) → ∞ whenever ||(u 1 , u 2 , u 3 )|| → ∞. Fulfillment of all the three conditions implies the existence of control variables u ∗1 , u ∗2 and u ∗3 with J [u ∗1 , u ∗2 , u ∗3 ] = min[J [u 1 , u 2 , u 3 ]] [25,26,37,40].  Theorem 11 If the optimal controls u i∗ for i = 1, 2, 3 and corresponding optimal states (S ∗ , A∗ , I ∗ , Q ∗ , H ∗ , R ∗ ) exist for the control system, then we have adjoint variables λ = (λ1 , λ2 , ..., λ6 ) ∈ R6 satisfying the canonical equations: dλ1 = λ1 [(1 − u 1 )β(I + p1 A + p2 Q) + βp3 H + d] − λ2 [(1 − u 1 )β(I + p1 A + p2 Q) + βp3 H ] dt dλ2 = λ1 [(1 − u 1 )βp1 S] − λ2 [(1 − u 1 )βp1 S − α0 ] − λ3 (σ ) − λ6 (η) dt dλ3 = −w1 + λ1 [(1 − u 1 )β S] − λ2 [(1 − u 1 )β S] + λ3 {(d + μ1 + ρ3 ) + u 2 + u 3 } − λ4 (u 2 ) dt − λ5 (u 3 ) − λ6 (ρ3 )

(8)

dλ4 = λ1 [(1 − u 1 )βp2 S] − λ2 [(1 − u 1 )βp2 S] + λ4 (α2 ) − λ5 (ξ1 ) − λ6 (ξ2 ) dt dλ5 = λ1 (βp3 S) − λ2 (βp3 S) + λ5 (α3 ) − λ6 (γ ) dt dλ6 = λ6 (d) dt

with transversality conditions λi (T f ) = 0 for i = 1, 2, ..., 6. The corresponding optimal controls u ∗1 , u ∗2 and u ∗3 are given as:      β(I ∗ + p1 A∗ + p2 Q ∗ )S ∗ u ∗1 = min max 0, (λ2 − λ1 ) , 1 , 2w2      ∗ I ∗ u 2 = min max 0, (λ3 − λ4 ) , 1 2w3      ∗ I u ∗3 = min max 0, (λ3 − λ5 ) , 1 . 2w4

Proof Proof is given in Appendix.

(9)



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10 Numerical Simulation of the Optimal Control Problem We have implemented three control strategies in the model (7) to reduce the disease burden and also to minimize the total cost incurred with the help of the optimal control paths. For COVID-19, maintaining physical distancing and hygiene is an important tool to reduce the virus transmission and this varies with time as it depends on disease prevalence. Besides, people can move to home-quarantine (isolation) or hospitals if symptoms of COVID-19 are shown and the awareness among population is also time-dependent. We are representing u 1 , u 2 and u 3 as the control variables, where u 1 is the portion of the susceptible population who maintains proper precautionary measures to keep themselves safe, u 2 , u 3 are the proportions of the symptomatically infected population who move to quarantine and hospitals respectively according to their physical conditions. The weights constants are assumed to be positive and taken as w1 = 1.5, w2 = 2, w3 = 50 and w4 = 10 [35,37]. We have slightly changed some of the parameter values here and all the parametric values are listed in Table 2. The initial population size is taken as: S(0) = 1.378×106 , A(0) = 6000, I (0) = 100, Q(0) = 4000, H (0) = 100 and R(0) = 5 to solve the control system in Eqs. (6)–(7). The numerical simulations are performed in MATLAB using forwardbackward sweep method for control interventions [41]. In this work, it is assumed that the control policies are implemented for one month, i.e., T f = 30 days. Figure 9 shows the dynamics of model (7) when there is no control policies are implemented, i.e., when u 1 = 0, u 2 = 0 and u 3 = 0. At T f = 30, the population becomes (3475637.88, 136.48, 648.80, 202.21, 1.62, 9184.18). The asymptomatically exposed population decreases in this case but with a slower rate. The symptomatically infected population increases for almost first 10 days and then starts to decrease. It is observed that the number of infected (symptomatically) individuals remains significantly higher in this case. Next, we consider the cases with a single control policy. First, we consider the case where people maintain physical distancing to avoid the infection and also to reduce the disease transmission. People in hospitals are already in strict restrictions and so, we are not considering any extra control policy for them. Figure 10 depicts the population profiles when u 1 = u ∗1 and

Table 2 Parametric values for numerical simulation of model (7) Parametric values 7.04 × 10−9



7 × 104

p1

0.3298

p2

0.005

p3

0.00002

d

0.00002

mu 1

0.02

μ2

0.000109

γ

0.8

σ

0.15

η

0.04

ρ1

0.001

ρ2

0.001376

ρ3

0.08

ξ1

0.001376

ξ2

0.1

β

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Fig. 9 Profiles of populations in absence of control policies

u 2 = u 3 = 0. At T f = 30, the population becomes (3476529.33, 32.59, 447.48, 200.47, 1.26, 8682.19). The number of susceptible individual increases with time. The asymptotically exposed population is reduced significantly in this case than in the absence of control policies. As a consequence, the symptomatically infected population, quarantined population, hospitalised population and even recovered population also decrease. The corresponding graph of optimal control intervention is depicted in Fig. 11. The control variable works with the highest intensity almost throughout the period. Next consider the case of implementing the control policy as the moving rate of symptomatically infected people to quarantine due to awareness (u 2 ). Figure 12 shows the dynamics of model (7) for u 2 = u ∗2 and u 1 = u 3 = 0. At T f = 30, the population becomes (3476345.85, 27.51, 16.18, 660.43, 1.32, 9603.61). The count of quarantined people is higher than the case when u i = 0 for i = 1, 2, 3 and this happens as people have become aware of the disease prevalence with days and move to home-quarantine if slightest symptoms are shown. The trajectory profile shows that the quarantined population first increases for 2 to 6 days after the implementation of the control strategy and then slowly decreases. This may happen as the symptomatically infected population reaches its maximum within 4 days and then starts to decrease. Figure 13 depicts corresponding graph of optimal control intervention of u 2 when u 1 , u 3 = 0. From this figure, it is observed that u 2 works with the highest intensity for almost half of a month and then decreases. Implementation of both the control policies works better for a system to control the disease burden than the case when a single control policy is applied. We are con-

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Fig. 10 Diagrams of the population in presence of optimal control u ∗1 only and u 2 = u 3 = 0

Fig. 11 Profile of optimal control u ∗1 only and u 2 = u 3 = 0

sidering the case where susceptible people takes precautions and maintains social distances to reduce the disease transmission and infected people moves to homequarantine (or, isolation) when physical symptoms are shown. Figure 14 depict the population trajectories in presence of both the control policies and at T f = 30, population becomes (3476535.92, 23.06, 14.41, 634.65, 1.27, 9448.17). The count of quarantined individuals increases significantly, whereas the count of asymptomatically exposed reduces due to social distancing in this case. The infected population and hospitalised population decrease here, and in fact, the rate of decrement is higher than the previous case. Figure 15 shows the graphs of optimal control policies u ∗1 , u ∗2 when u 3 = 0. It is observed that u 1 works with the highest intensity for almost three

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Fig. 12 Diagrams of the population in presence of optimal control u ∗2 and u 1 = u 3 = 0

Fig. 13 Profile of optimal control u ∗2 when u 1 = u 3 = 0

weeks after implementation whereas the control u 2 works for the first two weeks and then decreases slowly. If people maintain the physical distances and proper hygiene, then the virus cannot be transmitted that much. Also, if more people from symptomatically infected people move to quarantine without ignoring the symptoms, then also the probability of further virus transmission will be lower. Next, we consider that situation when the rate of moving of symptomatically infected people to hospitals (u 3 ) acts as a control strategy. Figure 16 shows the dynamics of model system (7) for u 3 = u ∗3 and u 1 = u 2 = 0. At T f = 30, the population becomes (3476351.52, 26.18, 9.08, 191.47, 4.87, 10073.39). The count of hospitalised people reaches to its maximum level in this case as people have become

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Fig. 14 Diagrams of the population in presence of optimal controls u ∗1 and u ∗2 when u 3 = 0

Fig. 15 The optimal control graphs for u ∗1 and u ∗2 when u 3 = 0

aware of the disease prevalence with days and move to hospitals if slightest symptoms are shown and their physical condition becomes worse. The trajectory profile shows that the asymptomatically exposed population and quarantined population continuously decrease while on the other hand symptomatically infected population and hospitalised people first increase up to 3–5 days and then start to decrease after reaching to its peak value. It is also observed here that the recovered population reaches its highest value than any other cases. Figure 17 depicts corresponding graph of optimal control policy u 3 when u 1 , u 2 = 0. From this figure, it is observed that

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Fig. 16 Diagrams of the population in presence of control policy u ∗3 when u 1 = u 2 = 0

Fig. 17 Profile of optimal control u ∗3 when u 1 = u 2 = 0

u 3 works with the highest intensity for more than three weeks and then decreases with time. Next, Fig. 18 shows the dynamics of model system (7) where susceptible people takes precautions, maintains physical distances to avoid further disease transmission and infected people move to hospitals after getting severely infected with COVID-19. With these two implemented control policies, population size becomes (3476532.60, 22.87, 8.30, 191.44, 4.25, 9899.60) at T f = 30. It is observed that the asymptomatically exposed class, symptomatically infected people and quarantined people decreases than the case when u i = 0 for i = 1, 2, 3 and the rates of decrement are higher than the case when only u 3 is applied. On the other hand,

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Fig. 18 Diagrams of the population in presence of optimal controls u ∗1 and u ∗3 when u 2 = 0

Fig. 19 The optimal control graphs for u ∗1 and u ∗3 when u 2 = 0

the hospitalised population and recovered population increases than the case when u i = 0 for i = 1, 2, 3 but here also the rates of decrement are higher than the case of u 3 = 0. Figure 19 depicts the optimal graphs of control policies u ∗1 , u ∗3 when u 2 = 0. The control u 1 works with the highest intensity for almost half of the month after implementation whereas the control u 3 works with the highest intensity for almost 25 days and then suddenly decreases. Maintaining physical distancing reduces the disease transmission resulting in declination of asymptomatically exposed people and so, the population in other compartments also decrease. Moreover, if more people from symptomatically infected people moves to hospitals, then also the probability of further virus transmission will be lower.

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Fig. 20 Diagrams of the population in presence of optimal controls u ∗2 and u ∗3 when u 1 = 0

Now we are considering the case where symptomatically infected people move to quarantine and hospitals according to their physical conditions. The people with mild symptoms are usually referred for self-quarantine for 14–15 days and on the other hand, people who are in critical condition are advised to admit to the hospitals. Figure 20 depict the dynamics of model system (7) for both these control policies and at T f = 30, population becomes (3476375.39, 25.83, 8.47, 380.99, 4.48, 9901.015). The count of asymptomatically exposed decreases in this case but remains higher than the cases when (u 1 , u 2 ) or (u 1 , u 3 ) is implemented. The growth rate of the infected population remains steep for first 5–6 days and then slowly decreases after reaches to the peak value. It is observed from the figure that the count of symptomatically infected people, quarantined people and even recovered people increase than the case u i = 0 for i = 1, 2, 3. Figure 21 depicts the optimal graphs of control policies u ∗2 , u ∗3 when u 1 = 0. The intensity of the control u 2 is highest for the first week of implementation and the declines slowly with time. On the other hand, u 3 works for almost three weeks with the highest intensity and then decreases. Implementation of all control strategies works better to control the disease burden than the case when a single control policy or only two control policies are applied. We finally consider the case where susceptible people takes precautionary measures and maintains social distancing to reduce the virus transmission and symptomatically infected people moves to quarantine or hospitals according to their physical conditions. Figure 22 depict the population trajectories in presence of all the control policies and at T f = 30, population becomes (3476529.46, 23.10, 7.87, 373.10, 4.04, 9759.91). The trajectory profiles reveal that the simultaneous use of all policies reduces the count of the symptomatically infected population significantly. The

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Fig. 21 The optimal control graphs for u ∗2 and u ∗3 when u 1 = 0

Fig. 22 Profiles of populations with optimal control policies u ∗1 , u ∗2 and u ∗3

asymptomatically exposed individuals are reduced than the case when u i = 0 for i = 1, 2, 3. The count of quarantined and hospitalised people also increases because of the control interventions u 2 and u 3 . Also, a proportion of quarantined and hospitalised people get recovery from the disease via natural immunity which increase the overall recovered population. Figure 23 depicts the optimal graphs of all the control policies. The intensity of u 3 remains highest for quite a long time, i.e., almost for three weeks and then declines for the rest of the period. On the other hand, u 1 and u 2 work for respectively two weeks and one week after implementation and then decrease with time.

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Fig. 23 Profiles of optimal control interventions u ∗1 , u ∗2 and u ∗3

Fig. 24 Graph of a cost distribution and b infected population (I ) in the absence and presence of implemented control interventions

Figure 24 describes the cost design analysis of the control system in absence and presence of u 1 , u 2 and u 3 . One case is considered with implemented control policies and the other case is considered with no implemented control policies. Optimal cost profiles and time series plot of symptomatically infected individuals have been shown in Fig. 24a and b respectively. In absence of control policies, the cost occurred only due to productivity loss. So, the opportunity loss is comparatively higher and count of infected population also increases here.

10.1 Effect of Weight Constants w3 and w4 on Optimal Control Policies Now we are varying two weight constants w3 and w4 to analyse their impacts on the control interventions. Figures 25 and 26 depict how the count of infected individuals

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(I ), the corresponding associated cost and optimal control policies change with time for different values of w3 and w4 . Figure 25a, e show that the symptomatically infected population (I ) and optimal cost (J ) increase for increasing value of w3 . Now from the Fig. 25b, d, it is observed that the control policies denoting social distancing of population (u 1 ) and moving rate of symptomatically infected class to hospitals for treatment (u 3 ) work with the highest intensity for a longer time period for a higher value of w3 . On the other hand, the intensity of control variable u 2 increases and in fact works with the highest intensity for a smaller value of w3 . From Fig. 25c, it is observed that u 2 works with the highest intensity for almost three weeks when w3 = 5 but for w3 = 50, the time of occurrence of the highest intensity of u 2 reduces and in fact, the intensity remains at its highest level for only one week after implementation. To draw the pictures in Fig. 26, the values of the weight constant w4 are taken as 10, 100 and 500. Here also, Fig. 26a, e show that the symptomatically infected population (I ) and corresponding optimal cost (J ) increase for increasing value of w4 . From Fig. 26b, c, it is observed that when the value of w4 is high, the control policies denoting social distancing of population (u 1 ) and moving rate of symptomatically infected class to quarantine (u 2 ) work with the highest intensity for a higher number of days. For example, when w4 = 500, u 1 works with the highest intensity for the first three weeks whereas for u 2 , the time duration is approximately two weeks. On the other hand, the intensity of control variable u 3 increases and in fact works with the highest intensity for a smaller value of w4 . From Fig. 26d, it is observed that u 3 works with the highest intensity for almost three weeks when w4 = 10 but for w3 = 100, the time of occurrence of the highest intensity of u 3 reduces and in fact, the intensity remains at its highest level for only four to five days after implementation.

11 Conclusion The World is severely affected by Coronavirus in the present day though it has first originated from China at the end of December 2019. Current reports indicate that more than 2 crores people are infected by COVID-19 worldwide. The Government of every country is trying their best to provide necessary information regarding precautionary measures to control the high infection rate. From the data of 2nd September 2020, there are 26,170,360 confirmed cases across the World [10,11]. The tracking system of Worldometers shows that almost 189,964 people have died in the US which is the highest in number among 188 countries or regions. Now according to the report of 6th September, India is in the first position among all countries in terms of per day infection and has held this position continuously for 32 days. From the present reports, there are total 3,848,968 confirmed Coronavirus cases are reported in the country till 2nd September 2020, among which 814,086 active cases, 2,967,396 recovered cases and 67,486 death cases as reported [10,11,13,14].

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Fig. 25 Profiles of a symptomatically infected population (I ), control variables b u ∗1 , c u ∗2 , d u ∗3 and e cost (J ) for various values of w3 along with w1 = 1.5, w2 = 2 and w4 = 10. Other parameters are as in Table 2

An epidemic model on coronavirus transmission is formulated here to analyse its impact on population. People from susceptible class move to asymptomatic class who are exposed to the environment after coming contact with asymptomatically exposed people, symptomatically infected people, quarantined people and even hospitalised people. The system variables remain non-negative and uniformly bounded which means the proposed model is well-posed. The system has a disease-free equilibrium point (DFE) (E 0 ) for any parametric values and an unique endemic equilibrium point for R0 > 1. Stability analysis of the equilibria proves that DFE is stable for R0 < 1 along with some parametric restrictions. Figure 6 depicts that the count of the overall infected population significantly reduces when the transmission rates from

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Fig. 26 Time series plot of a infected population (symptomatically) I , control variables b u ∗1 , c u ∗2 , d u ∗3 and e cost (J ) for various values of w4 along with w1 = 1.5, w2 = 2 and w3 = 50. Parametric values are taken from Table 2

asymptotically exposed people and quarantined people decrease. Also, if more people are quarantined or hospitalised while symptoms are shown, then the transmission of virus decreases with time. On the other hand, Fig. 7 shows that the count of infected individuals decreases with an increase of ρi for i = 1, 2. It is observed from this picture that increasing ρ2 reduces the infection more than ρ1 . It may happen because, for hospitalisation, patients are under strict restrictions and the chances of social interaction are very lower. In the latter part of the work, an optimal control problem is formulated to analyse how proper control strategies reduce the disease burden. Social distancing and main-

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taining proper hygiene are the important prevention strategies to lower the disease transmission and is considered as one of the control interventions. It is considered in system (7) that the disease transmit into (1 − u 1 )S amount of susceptible people only. Moreover, the infected individuals, when physical symptoms are shown, choose the self-quarantine or admission into hospitals according to their physical condition to reduce further transmission. The control variables u 2 and u 3 represent the rates of moving of symptomatically infected population to quarantine and hospitals respectively. Figures in section “Numerical Simulation of the Optimal Control Problem” show that when a single control strategy is implemented, the intensity with which u 1 works remains the highest almost throughout the time period whereas u 2 and u 3 work with the highest intensity for two and three weeks respectively before declining. On the other hand, when all the control policies are implemented in the system, u 3 works with the highest intensity for the longest time than the other two control interventions. u 3 works for almost three weeks with the highest intensity after implementation and slowly decrease, whereas for u 1 and u 2 , the time period is two weeks and one week respectively. Moreover, the recovered population is always higher when one or more optimal control strategies are applied. But, when only social distancing (u 1 ) is applied in the control problem, the recovered population is reduced as people already maintain proper physical distancing and hygiene in a susceptible state. Also, the highest number of quarantined population can be obtained when only u 2 is applied as a control intervention. On the other hand, the highest number of hospitalised people is obtained when only u 3 is applied and so we get the highest count of recovered individuals in this case. The figures with one or more than one control strategies clearly depict if more people move into either quarantine or to hospitals, then ultimately recovered population increases with time. It is observed that the simultaneous use of all control policies reduce the count of the symptomatically infected population significantly and consequently control current pandemic situation to some extent.

Core Messages

• An epidemic model on COVID-19 transmissions is introduced in this work. • Susceptible people move to an asymptomatically exposed class after coming in contact with the infected, hospitalized, and quarantined people. • The finding shows that moving into quarantine at a large rate reduces the count of the infected population significantly. • Social distancing and maintaining hygiene are among the main policies to control the prevalence. • Simultaneous implementation of all the control policies significantly reduces the overall infected population count.

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Appendix Characterization of Control Interventions The optimal control functions in the optimal system is obtained with the help of Pontryagin’s Maximum Principle. Consider the following Hamiltonian function: H (S, A, I, Q, H, R, u 1 , u 2 , u 3 , λ) = L(S, A, I, Q, H, R, u 1 , u 2 , u 3 ) + λ1 + λ4

dS dA dI + λ2 + λ3 dt dt dt

dQ dH dR + λ5 + λ6 dt dt dt

So, H = w1 I + w2 u 21 + w3 u 22 + w4 u 23 + λ1 [ − (1 − u 1 (t))β(I + p1 A + p2 Q)S − βp3 H S − d S] + λ2 [(1 − u 1 (t))β(I + p1 A + p2 Q)S + βp3 H S − α0 A] + λ3 [σ A − u 2 (t)I − u 3 (t)I − ρ3 I − (d + μ1 )I ] + λ4 [u 2 (t)I − α2 Q] + λ5 [u 3 (t)I + ξ1 Q − α3 H ] + λ6 [ρ3 I + η A + ξ2 Q + γ H − d R]

(A1)

Here the adjoint variables are denoted by λ = (λ1 , λ2 , λ3 , λ4 , λ5 , λ6 ). In order to minimize the cost function, the Hamiltonian function need to be minimized by Pontryagin’s Maximum Principle. Theorem 11 (Proof) Proof For the control system (7), let us consider u i∗ for i = 1, 2, 3 are the applied optimal control interventions along with optimal state variables S ∗ , A∗ , I ∗ , Q ∗ , H ∗ , R ∗ . Then there exist adjoint variables λi for i = 1, 2, .., 6 satisfying the canonical equations: ∂H dλ1 =− , dt ∂S

dλ2 ∂H =− , dt ∂A

dλ3 dt

=−

∂H , ∂I

dλ4 ∂H =− , dt ∂Q

dλ5 ∂H =− , dt ∂H

dλ6 ∂H =− . dt ∂R

So, we have dλ1 = λ1 [(1 − u 1 )β(I + p1 A + p2 Q) + βp3 H + d] − λ2 [(1 − u 1 )β(I + p1 A + p2 Q) + βp3 H ] dt dλ2 = λ1 [(1 − u 1 )βp1 S] − λ2 [(1 − u 1 )βp1 S − α0 ] − λ3 (σ ) − λ6 (η) dt dλ3 = −w1 + λ1 [(1 − u 1 )β S] − λ2 [(1 − u 1 )β S] + λ3 {(d + μ1 + ρ3 ) + u 2 + u 3 } − λ4 (u 2 ) dt − λ5 (u 3 ) − λ6 (ρ3 ) dλ4 = λ1 [(1 − u 1 )βp2 S] − λ2 [(1 − u 1 )βp2 S] + λ4 (α2 ) − λ5 (ξ1 ) − λ6 (ξ2 ) dt dλ5 = λ1 (βp3 S) − λ2 (βp3 S) + λ5 (α3 ) − λ6 (γ ) dt dλ6 = λ6 (d) dt

(A2)

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with the transversality conditions λi (T f ) = 0, for i = 1, 2, 3, 4, 5, 6.    ∂ H  ∂ H  ∂ H  From optimality conditions: = 0, = 0 and = 0. ∂u 1 u 1 =u ∗ ∂u 2 u 2 =u ∗ ∂u 3 u 3 =u ∗ 1

So, u ∗1 =

2

3

β(I ∗ + p1 A∗ + p2 Q ∗ )S ∗ I∗ (λ2 − λ1 ) , u ∗2 = (λ3 − λ4 ) and u ∗3 = 2w2 2w3

I∗ (λ3 − λ5 ). 2w4 So, in , we have ⎧ ∗ ∗ ∗ ∗ ⎪ 0, if β(I + p1 A2w+2 p2 Q )S (λ2 − λ1 ) < 0 ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ u 1 = β(I + p1 A2w+2 p2 Q )S (λ2 − λ1 ) , if 0 ≤ β(I + p1 A2w+2 p2 Q )S (λ2 − λ1 ) ≤ 1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ∗ ∗ ∗ ∗ ⎩ 1, if β(I + p1 A2w+2 p2 Q )S (λ2 − λ1 ) > 1

u ∗2 =

u ∗3 =

⎧ ⎪ 0, ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ ∗ I

2w3 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ 1,

⎧ ⎪ 0, ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ ∗

I ⎪ 2w4

⎪ ⎪ ⎪ ⎪ ⎪ ⎩1,

which is equivalent as (9).

if

I∗ 2w3

(λ3 − λ4 ) , if 0 ≤

(λ3 − λ4 ) < 0 I∗ 2w3

(λ3 − λ4 ) ≤ 1

if

I∗ 2w3

(λ3 − λ4 ) > 1

if

I∗ 2w4

(λ3 − λ5 ) < 0

(λ3 − λ5 ) , if 0 ≤ if

I∗ 2w4

I∗ 2w4

(λ3 − λ5 ) ≤ 1

(λ3 − λ5 ) > 1 

Optimal System The optimal system involving optimal control variables u ∗1 , u ∗2 and u ∗3 along with ∗ minimized Hamiltonian H at (S ∗ , A∗ , I ∗ , Q ∗ , H ∗ , R ∗ , λ1 , λ2 , λ3 , λ4 , λ5 , λ6 ) is given as following:

11 Conclusion

d S∗ dt d A∗ dt dI∗ dt d Q∗ dt d H∗ dt d R∗ dt

255

=  − (1 − u ∗1 )β(I ∗ + p1 A∗ + p2 Q ∗ )S ∗ − βp3 H ∗ S ∗ − d S ∗ , = (1 − u ∗1 )β(I ∗ + p1 A∗ + p2 Q ∗ )S ∗ + βp3 H ∗ S ∗ − α0 A∗ , = σ A∗ − u ∗2 I ∗ − u ∗3 I ∗ − ρ3 I ∗ − (d + μ1 )I ∗ , = u ∗2 I ∗ − α2 Q ∗ , = u ∗3 I ∗ + ξ1 Q ∗ − α3 H ∗ , = ρ3 I ∗ + η A∗ + ξ2 Q ∗ + γ H ∗ − d R ∗ ,

with non-negative initial conditions and corresponding adjoint system is:

(A3)

dλ1 = λ1 [(1 − u ∗1 )β(I ∗ + p1 A∗ + p2 Q ∗ ) + βp3 H ∗ + d] − λ2 [(1 − u ∗1 )β(I ∗ + p1 A∗ + p2 Q ∗ ) + βp3 H ∗ ] dt dλ2 = λ1 [(1 − u ∗1 )βp1 S ∗ ] − λ2 [(1 − u ∗1 )βp1 S ∗ − α0 ] − λ3 (σ ) − λ6 (η) dt dλ3 = −w1 + λ1 [(1 − u ∗1 )β S ∗ ] − λ2 [(1 − u ∗1 )β S ∗ ] + λ3 {(d + μ1 + ρ3 ) + u ∗2 + u ∗3 } − λ4 (u ∗2 ) dt − λ5 (u ∗3 ) − λ6 (ρ3 ) dλ4 = λ1 [(1 − u ∗1 )βp2 S ∗ ] − λ2 [(1 − u ∗1 )βp2 S ∗ ] + λ4 (α2 ) − λ5 (ξ1 ) − λ6 (ξ2 ) dt dλ5 = λ1 (βp3 S ∗ ) − λ2 (βp3 S ∗ ) + λ5 (α3 ) − λ6 (γ ) dt dλ6 = λ6 (d), dt

(A4)

with transversality conditions λi (T f ) = 0, for i = 1, 2, ..., 6 and the control strategies u i∗ for i = 1, 2, 3 are same as in (9).

References 1. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia J, Yu T, Zhang X, Zhang L (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet 395(10223):507–513 2. Li R, Pei S, Chen B, Song Y, Zhang T, Yang W, Shaman J (2020) Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2). Science. https://doi.org/10.1126/science.abb3221 3. Chen W, Horby PW, Hayden FG, Gao GF (2020) A novel coronavirus outbreak of global health concern. The Lancet 395(10223):470–473 4. Juan, D. Wuhan wet market closes amid pneumonia outbreak. https://www.chinadaily.com. cn/a/202001/01/WS5e0c6a49a310cf3e35581e30.html, 2020 5. Coronaviridae Study Group of the International Committee on Taxonomy of Viruses (2020) The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV

256

6. 7.

8.

9. 10.

11. 12. 13. 14. 15.

16. 17.

18.

19.

20. 21.

S. Saha and G. P. Samanta and naming it SARS-CoV-2. Nat Microbiol 5(4):536–544. https://doi.org/10.1038/s41564020-0695-z Richman, D.D., Whitley, R.J. & Hayden, F.G. Clinical virology. John Wiley & Sons, 2016 Gaythorpe, K., Imai, N., Cuomo-Dannenburg, G., Baguelin, M., Bhatia, S., Boonyasiri, A., Cori, A., Cucunubá, Z., Dighe, A., Dorigatti, I., FitzJohn, R., Fu, H., Laydon, D., NedjatiGilani, G., Okell, L., Riley, S., Thompson, H., van Elsland, S., Wang, H., Wang, Y., Whittaker, C., Xi, X., Donnelly, C. A. & Ghani, A. (2020) Report 8: Symptom progression of COVID-19, p. 10. Online: https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/ report-8-symptom-progression-covid-19/ Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, Wu Y, Zhang L, Yu Z, Fang M, Yu T, Wang Y, Pan S, Zou X, Yuan S, Shang Y (2020) Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. The Lancet Respiratory Medicine. https://doi.org/10.1016/S2213-2600(20)30079-5 Centers for disease control and prevention: 2019 novel coronavirus. Online: https://www.cdc. gov/coronavirus/2019-ncov, 2020 Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Available online: https://coronavirus. jhu.edu/map.html; https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html# /bda7594740fd40299423467b48e9ecf6, 2020 Worldometers Live Tracking: https://www.worldometers.info/coronavirus/country/india/ John Hopkins University & Medicine: Coronavirus Resource Centre. https://coronavirus.jhu. edu/data/mortality India covid-19 tracker. https://www.covid19india.org/, 2020 Ministry of health and family welfare government of india. https://www.mohfw.gov.in/, 2020 Dorigatti, I., Okell, L., Cori, A., Imai, N., Baguelin, M., Bhatia, S., Boonyasiri, A., Cucunubá, Z., Cuomo-Dannenburg, G., FitzJohn, R., Fu, H., Gaythorpe, K., Hamlet, A., Hong, N., Kwun, M., Laydon, D., NedjatiGilani, G., Riley, S., van Elsland, S., Wang, H., Wang, R., Walters, C., Xi, X., Donnelly, C. & Ghani, A. (202) Report 4: Severity of 2019-novel coronavirus (nCoV). Online: Report 4: Severity of 2019-novel coronavirus (nCoV), https://www.imperial.ac.uk/ mrc-global-infectious-disease-analysis/news--wuhan-coronavirus/ Famulare, M. 2019-nCoV: preliminary estimates of the confirmed case-fatality-ratio, and infection-fatality-ratio and initial pandemic risk assessment, 2020 Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, Cuomo-Dannenburg G, Thompson H, Walker PGT, Fu H, Dighe A, Griffin JT, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunubá, Z., FitzJohn, R., Gaythorpe, K., Green, W., Hamlet, A., Hinsley, W., Laydon, D., Nedjati-Gilani, G., Riley, S., Elsland, S.V., Volz, E., Wang, H., Wang, Y., Xi, X., Donnelly, C.A., Ghani, A.C. & Ferguson, N.M. (2020) Estimates of the severity of coronavirus disease 2019: a model-based analysis. The Lancet Infectious Diseases. https://doi.org/10. 1016/S1473-3099(20)30243-7 Wu, J.T., Leung, K., Bushman, M., Kishore, N., Niehus, R., Salazar, P.M.d., Cowling, B.J., Lipsitch, M. & Leung, G.M. (2020) Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nat Med, 26, 506-510. https://doi.org/10.1038/s41591020-0822-7 Ferguson, N., Laydon, D., Gilani, G.N., Imai, N., Ainslie, K., Baguelin, M., Bhatia, S., Boonyasiri, A., Perez, Z.C., Cuomo-Dannenburg, G. et al. (2020) Report 9: Impact of non-pharmaceutical interventions (npis) to reduce covid19 mortality and healthcare demand. Online: https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gidafellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020 Tang B, Bragazzi NL, Li Q, Tang S, Xiao Y, Wu J (2020) An updated estimation of the risk of transmission of the novel coronavirus (2019-ncov). Infectious Disease Modelling 5:248–255 Tang B, Wang X, Li Q, Bragazzi NL, Tang S, Xiao Y, Wu J (2020) Estimation of the transmission risk of the 2019-ncov and its implication for public health interventions. Journal of Clinical Medicine 9(2):462

Analysis of a COVID-19 Model Implementing Social Distancing …

257

22. Quilty, B.J., Clifford, S., CMMID nCoV working group2, Flasche, S. & Eggo, R.M. (2020) Effectiveness of airport screening at detecting travellers infected with novel coronavirus (2019-nCoV). Euro Surveill. 25(5), 2000080. https://doi.org/10.2807/1560-7917.ES.2020. 25.5.2000080 23. Shen, M., Peng, Z., Xiao, Y. & Zhang, L. (2020) Modelling the epidemic trend of the 2019 novel coronavirus outbreak in china. bioRxiv. doi: https://doi.org/10.1101/2020.01.23.916726 24. Ndaïrou, F., Area, I., Nieto, J.J. & Torres, D.F.M. (2020) Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos, Solitons and Fractals 135:109846 25. Saha, S., Samanta, G.P. & Nieto, J.J. (2020) Epidemic model of COVID-19 outbreak by inducing behavioural response in population. Nonlinear Dyn, 102, 455-487, https://doi.org/ 10.1007/s11071-020-05896-w 26. Saha, S. & Samanta, G.P. (2020) Modelling the role of optimal social distancing on disease prevalence of COVID-19 epidemic. International Journal of Dynamics and Control. https:// doi.org/10.1007/s40435-020-00721-z 27. Wu JT, Leung K, Leung GM (2020) Nowcasting and forecasting the potential domestic and international spread of the 2019-ncov outbreak originating in wuhan, China: a modelling study. The Lancet 395(10225):689–697 28. Singh, R. & Adhikari, R. (2020) Age-structured impact of social distancing on the covid-19 epidemic in india. arXiv preprint arXiv:2003. 12055 29. Hale JK (1977) Theory of functional Differential Equations. Springer-Verlag, Heidelberg 30. Van den Driessche P, Watmough J (2002) Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Mathematical Biosciences 180(1):29–48 31. Arriola L, Hyman J (2005) (2005) Lecture notes, forward and adjoint sensitivity analysis: with applications in Dynamical Systems. Linear Algebra and Optimisation Mathematical and Theoretical Biology Institute, Summer 32. LaSalle J (1976) The stability of dynamical systems. Regional conference series in applied mathematics, SIAM, Philadelphia 33. Castillo-Chavéz C, Song B (2004) Dynamical models of tuberculosis and their applications. Math Biosci Eng 1:361–404 34. Behncke H (2000) Optimal control of deterministic epidemics. Optimal Control Applications and Methods 21(6):269–285 35. Kassa S, Ouhinou A (2015) The impact of self-protective measures in the optimal interventions for controlling infectious diseases of human population. Journal of Mathematical Biology 70(1–2):213–236 36. Castilho C (2006) (2006) Optimal control of an epidemic through educational campaigns. Electronic Journal of Differential Equations 125:1–11 37. Gaff H, Schaefer E (2009) Optimal control applied to vaccination and treatment strategies for various epidemiological models. Mathematical Biosciences and Engineering 6(3):469–492 38. Joshi H, Lenhart S, Li M, Wang L (2006) Optimal control methods applied to disease models. Contemporary Mathematics 410:187–208 39. Coddington E (1955) & Levinson. N. Theory of ordinary differential equations, Tata McGrawHill Education 40. Fleming W, Rishel R (1975) Deterministic and stochastic optimal control, vol 1. Springer, New York 41. Kirk, D. Optimal control theory: an introduction, Dover Publications, 2012 42. Pontryagin, L. Mathematical theory of optimal processes, CRC Press, 1987

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S. Saha and G. P. Samanta Sangeeta Saha is pursuing Ph.D. research work at the Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, India, under the supervision of Prof. G. P. Samanta. She has a total of eight publications from 2018– 2020, among which five are in reputed SCI Scopus journals such as International Journal of Bifurcation and Chaos, International Journal of Biomathematics, Physica A, and Nonlinear Dynamics, and rest are in Scopus journals such as International Journal of Dynamics and Control, Letters in Biomathematics and Energy, Ecology and Environment.

G. P. Samanta is a Professor, Higher Administrative Grade, in the Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, India. He has been involved in teaching and research for more than 33 years. He has published more than 160 research papers in various international journals of repute. Several students are at present working for Ph.D. under his supervision. His fields of research work are nonlinear dynamics, mathematical biology, mathematical epidemiology, operations research, and statistical modeling.

11

Impact of Policy Implementations on the Propagation of COVID-19 Before Vaccine Tapen Sinha

It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair, we had everything before us, we had nothing before us…. Charles Dickens, A Tale of Two Cities.

Summary

Do policy differences matter in terms of outcomes of COVID-19 in different countries and different regions? This is the central question we tackle here. We explore how the policy choices affect the number of cases and deaths in the absence of a vaccine.

T. Sinha (&) Chennai Mathematical Institute, Chennai, Tamil Nadu, India e-mail: [email protected] Unit 41, 25 Union Street, Nundah, QLD 4012, Australia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_11

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Graphical Abstract/Art Performance

Lockdown rules and the propagation of COVID-19 (Adapted with permission from the Association of Science and Art (ASA), Universal Scientific Education and Research Network (USERN); Made by Sheida Javdaneh).

The code of this chapter is 01101100 01110000 01100101 01001001 01101111 01100101 01110011 01101110 01101101 01110100 01100001 01110100 01101001 01101101 01101110. Keywords
















Australia COVID-19 New Zealand Pandemic Policy choices Queensland

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Impact of Policy Implementations on the Propagation …

1

Introduction

261

The coronavirus disease, COVID-19, has created havoc across the world. By the end of September 2020, it had been known to have infected 33 million people around the globe, with a million known deaths from it [1]. In the past one thousand years, the most devastating respiratory pandemics have come from different variants of influenza. Figure 1 shows recorded influenza epidemics during 1100–2000. One clear takeaway from Fig. 1 is that the frequency has increased. Over the millennia, people began living together in bigger groups in cities. Therefore, close contact between groups of people led to periodic explosions of fast-moving pandemics. In addition, they interacted more due to faster modes of transportation. There are clear policy lessons from these observations: to reduce the impact of a respiratory pandemic, the length of interaction and the frequency of interaction between people need to be reduced. Figure 2 shows how international air traffic has grown after World War II. This figure tells us how global interaction has speeded up until 2020. Today it is entirely feasible to go across the world in less than 24 h under two thousand United States dollars. This level of connectivity is the catalyst for the fast transmission of a respiratory pandemic. The remarkable aspect of Fig. 2 is not just the rise of the passengers—it shows how it has fallen during 2020. ICAO estimates the fall in international traffic of upwards of 60%. We will come back to the issue of restrictions on travel as a strategy below. In what follows, we will examine how such attempted policies of reducing the length and the frequency of interaction among people to reduce the spread of coronavirus have worked. To do that, we have chosen two countries with close contacts and similar histories and customs. In doing so, we have to worry less about other factors (such as cultural or ethnic factors) that might affect our conclusions [2].

2

A Helicopter View of Australia and New Zealand

Australia is a country of 25 million people in a land of 7.7 million square kilometres —slightly smaller than the United States (without counting Alaska). The population density1 is less than four persons per square kilometer. That measure is deceptive for more than 60% of the population living in the six biggest cities. This fact becomes relevant when we discuss policy formulation—the density of the population becomes a central issue in a contagious pandemic. Per capita income in Australia is around the United States $54,000 in 2020 with a highly developed centralized healthcare system that covers all Australians.

1

The author wishes to thank Prof. Nima Rezaei for encouraging the contribution and Dr. Amene Saghazadeh for her help with editing. However, all responsibilities lie with the author.

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Fig. 1 Frequency and severity of influenza 1100AD-2000AD. 1: epidemic, 2: probable pandemic, 3: pandemic (Reproduced with permission from [11])

Fig. 2 Global airline passenger movement (Prepared with data from https://www.icao.int/ sustainability/Documents/COVID-19/ICAO_Coronavirus_Econ_Impact.pdf, accessed September 24, 2020)

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New Zealand has 5 million people in a country of 270,000 km2. The population density is 18 persons per square kilometer. Like in Australia, the top seven cities account for more than 60% of the total population. The per capita income of New Zealand is around the United States $41,000 in 2020, with a highly developed centralized healthcare system that covers all New Zealanders. Both Australia and New Zealand were British colonies before becoming self-governing dominions retaining strong ties with the United Kingdom. Both countries have retained the Queen as their (nominal) head of state. Both countries are governed by parliamentary systems modeled after the British one. As a result, both Australia and New Zealand have a similar legal system [3]. In summary, both countries have: • sparse but clusters of the population in the largest cities; • their income levels are high with relatively low inequality with a large social safety net; • stable and centralized healthcare systems; and • both countries have inherited the British Common Law. These facts have clear policy implications for a contagious pandemic like the coronavirus. A sparse but clustered population means policies geared to testing and tracing are highly effective. Social distancing is important. A high-income country with social safety net implies lockdowns over a period of months does not rob people of basic needs to live on. A stable and centralized healthcare system implies that if the national government goes on war footing to the supply of hospital beds, ventilators, and other needs for coronavirus, there are no resource constraints at regional levels [4]. Given the clusters of large cities where the population is concentrated, distribution is not an issue. The Common Law structure gives more flexibility on making special provisions to implement policies easier. COVID-19 first happened in late December of 2019 in China, mainly in Wuhan. On January 23, the Chinese government introduced an intensive program of testing, contact tracing. It led to quarantining of people in Wuhan and Hubei and later to other provinces. To stop the disease spread, travel to other provinces was curtailed. Over the coming weeks, industrial activities were reduced and recreational and social gatherings were dramatically cut. Schools were shut to prevent the interfamily spread of infection.

3

The Policy Response in Australia

In Australia, the number of cases rose to five hundred cases a day in late March 2020. After that, it rapidly declined to single-digit numbers a month later. Around the same time, in many countries of Europe and North America, the numbers exploded, overwhelming their healthcare systems.

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3.1 Five-Phase Response in Australia Australia’s response came in different phases.

3.1.1 Phase 1: Containment The very first case of coronavirus was recorded in Australia on January 25, 2020. Early cases were reported in Wuhan, China, a month earlier. Most such cases were reported from international travelers, but less than ten percent came from community transmission. During the initial phase of infection in Australia, the Chief Medical Officer of the Commonwealth of Australia took charge of managing the spread. All state and territory chief public health officers participated in the Australian Health Protection Principal Committee. Testing of genetic material from the patients revealed one fact: all infections came from Wuhan, China. There was no community transmission (yet). The Commonwealth Government rigorously screened arrivals from Wuhan. To stem community transmission, the Australian government evacuated vulnerable Australians out of the Chinese state of Hubei. These evacuees were then quarantined in remote parts of Australia (for example, in Christmas Island and in Darwin in the Northern Territories). When there were more than 10,000 confirmed cases in China by February 1, Australia banned all mainland China visitors from coming into the country. Australians returning to Australia from mainland China had to stay inside for two weeks. People of other nationalities were required to be quarantined in designated isolated locations—usually in specific hotels. A large number of other countries around the globe, from Afghanistan to Vietnam, did the same for people coming in from China around the same date. 3.1.2 Phase 2: Period of Indecision Early March was a confusing time for Australians. It became clear that simply putting restrictions on China and not restricting entrance from other countries did not work. The Commonwealth Government of Australia downplayed the risks to Australians. The Prime Minister was reassuring Australians in early March with statements like Australians could “go about their daily business.” He declared that he was “looking forward to going to places of mass gathering such as the football.”2 That was not to be. It did not reassure the Australians at all. A sense of panic had set in. Australians started emptying the shelves in the supermarket, buying toilet paper, cleaning supplies, and canned goods in early March. Until the middle of March, there were less than one hundred cases per day. Two things shook the confidence of the Australian population in the actions of the Commonwealth Government. First, reports of community spread started across various states. Until then, almost all cases could be linked to people returning from abroad. Second, a cruise ship called the Ruby Princess docked in Sydney, Australia. Despite reports that some of the passengers tested positive on the ship, the entire 2

https://www.health.gov.au/ministers/the-hon-greg-hunt-mp/media/update-on-covid-19-inaustralia-community-transmission.

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group of over 2,700 passengers was allowed to walk out of the ship without quarantine. They were simply requested to self-quarantine themselves. These passengers went across Australia, spreading the disease across the country. On March 13, Australia formed a “National Cabinet” of ministers. It included not just the Commonwealth Government officials but also government officials from the state and territories to provide a unified approach to the pandemic. On March 15, Australia had recorded 295 cases and five deaths.

3.1.3 Phase 3: Large Scale Testing But Weak Enforcement of Quarantine Australian Commonwealth Government embarked on a large-scale testing regime. The tests per thousand became on par with Europe, but the incidents were far lower in Australia. Self-isolation became mandatory for all international arrivals. Unfortunately, there was no follow-up. The government introduced contact tracing systems with the use of a cell phone app to reduce the risk of community transmission. Critically, at this stage, tests and tracing responsibilities were handed over to the states as it became clear that the transmission has been very uneven across the country. To handle the problem at the state level, the Commonwealth Government promised the states and territories to fund all expenses related to this infection. Most gatherings were severely restricted. For example, on March 18, 2020, indoor gatherings of more than 100 people were prohibited. How much impact did all of this have on the activities of the population? It is one thing to legislate restrictions, and it is another matter to enforce the restrictions to make sure that people comply. Did the people comply? How do we measure such compliance? One technological development made it possible to monitor compliance: the use of cellular telephones. Over 70% of Australians were using smartphones at the end of 2019. As people carry their devices with them almost at all times, it became possible to monitor driving, transit, and walking activities en masse. Here we report such movements using Apple Mobility Data. Figure 4 shows mobility data in three dimensions: driving, the use of mass transit, and walking. Note that by the middle of March, the three forms of mobility measures have already fallen by 30%. 3.1.4 Phase 4: Stricter Measures Imposed Drastic measures of curtailing movements were announced in the last two weeks of March. Within two weeks, all states in Australia introduced a full shut-down. Social distancing measures in public places, stores, restaurants, and other places were put in place. Bans on travel beyond certain distances from home, contact tracing, and compulsory quarantine were introduced. There was a debate during the second week of March whether measures should aim to “slow the spread of the pandemic” or “stop the spread of the pandemic.” The main motivation was the fear that was trying to slow the pandemic might lead to a strain on the healthcare system resulting from hospitalized COVID-19 patients. Until the middle of March, the focus was to reduce the entry of COVID-19 through incoming travelers to Australia. To that end, the government first

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introduced a ban on foreigners coming to Australia. The risk of community spread was not considered to be of importance. The cancellation of the Grand Prix in Melbourne slated for 13–15 March was a signal of future restrictions to come. States had started taking responsibility for making local decisions. The National Cabinet, with all state representations, took the national stage. Restrictions on activities in bars, restaurants, clubs, cinemas, places of worship, casinos, and gyms were restricted starting the week of March 23 all across Australia. The restrictions came in later than in many other countries. For example, if we examine restaurant reservation data of the United States and Australia (Fig. 5), we observe that by March 18, Australian restaurant reservations were down by 50%, whereas in the United States, they were down by 100%. Given the terrible toll that the United States subsequently suffered, this was an unexpected result. After the disembarking fiasco of the Ruby Princess, we noted earlier; it became clear that many returning Australian travelers were flouting self-isolation rules. Additional border control measures were introduced on March 27. In particular, a mandatory quarantine in designated facilities for all arriving passengers regardless of citizenship became the norm. States and territories with lower levels of reported cases closed their interstate borders. Tasmania started with border closure on March 20. Then, the Northern Territory, Western Australia, South Australia, and Queensland followed suit. The two most populous states, New South Wales and Victoria, were conspicuously absent. States and territories in Australia worked to increase their public hospital intensive care unit (ICU) capacity. A study showed that within a month, the ICU capacity could be increased by 100%. The Commonwealth Government started a national stockpile. Then it allocated personal protective equipment (PPE) as each state requested the PPE. It ordered more from national and international suppliers, not just the PPE but also ventilators. The PPE and the ventilators were produced domestically at a rapid pace.3 The Commonwealth increased its public health funding by another several billion dollars, including resources for mental health care.4 On the healthcare front, Australia has a mix of private and public healthcare providers—half of the facilities are public, and the other half is private. At the end of March, the National Cabinet put on hold all non-urgent elective surgery. This made room for COVID-19 patients as well as helped to keep PPE that was in limited supply. To compensate for losses, the Commonwealth Government paid the private hospitals during the elective surgery shut-down. States and territories bought the use of private hospital beds. As a result, many COVID-19 patients at the public hospitals were moved to private hospitals.

3

https://www.industry.gov.au/news-media/covid-19-news/australian-made-ventilators-deliveredquickly, accessed September 24 2020. 4 https://www.mja.com.au/journal/2020/212/10/surge-capacity-intensive-care-units-case-acuteincrease-demand-caused-covid-19, accessed September 24 2020.

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It has been known for months that COVID-19 infects the young at a higher rate, but the mortality rate for older people is much higher [5]. In Australia, the infected population under 50 was 17,808, and those above 50 were 9,004.5 However, out of the 852 deaths, just 9 of them were under 50. Therefore, the young (under 50) are infected in large absolute numbers. While some of them required hospitalization and ventilators, the vast majority (over 90%) who required hospitalization were over age 65. Thus, the principal medical problem for Australia due to COVID-19 came from the elderly. There is a special problem for the elderly in Australia: in the OECD countries, Australia has the largest proportion of people living in aged care facilities.6 This means that specific strategies for those institutions were required. As a result, the number of visitors was restricted in the aged care facilities along with the movement of the residents. The staff was required to be vaccinated against influenza.

3.1.5 Phase 5: Starting a “New Normal” By the end of April 2020, Australia started reopening facilities but with new guidelines. The new normal entailed social distancing, an aggressive cleaning regime, and close monitoring. It introduced a CovidSafe app for mobile telephones to monitor and control community transmission. In theory, using this app means that if someone has COVID-19, health officials can quickly trace their contacts if all of them have the CovidSafe app. However, downloading the app was voluntary. It required at least 40% of Australians to download the app. While initial uptake was rapid, it tapered off to 6.4 million downloads by July. That amounted to less than 27% of all Australians with the app. What are the lessons from the policy measures we discussed? We examine the successes and failures below. The “National Cabinet” made a difference to provide a unified approach to the pandemic. The states and territories conducted public hospitals and emergencies, while the Commonwealth managed income and business support programs. Thus, coordination between the federal and state levels was critical. The formation of the National Cabinet was instrumental in coordinating policies across the country. Starting with nationwide shut-down of non-essential activities in March, to distributing medical supplies to hospitals, to coordinating the CovidSafe app along with economic measures such as extending unemployment benefits, all were possible because of the national coordination. The second most important step to success was closing international borders. First, the arrival from China was restricted, then foreigners arriving in Australia were restricted. It was swiftly followed by all arrivals being quarantined in specific locations as self-quarantine did not work well. Too many flouted the norm. In the first wave (Fig. 3), over 90% of the cases were either overseas arrivals or people who were directly in contact with the arriving passengers. Eventually, Australians 5

As of September 27, 2020, data from https://www.health.gov.au/resources/covid-19-cases-byage-group-and-sex, accessed September 28, 2020. 6 https://www.mja.com.au/journal/2020/213/4/australia-over-reliant-residential-aged-care-supportour-older-population#tbox1, accessed September 28, 2020.

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Fig. 3 Two waves of COVID-19 in Australia (Prepared with data from Australian Healthcare database)

abroad were allowed back in but in limited numbers so that the hospital facilities were not overwhelmed. “Escalated national action” was, therefore, started in Australia.7 Australia’s daily case numbers fell rapidly after all foreigners were prohibited from entering. Even for returning Australians, a weekly cap was introduced. The third measure, social distancing, became the norm very quickly. This was a key to reducing community transmission. However, the adoption of the use of face masks was a mixed bag. In certain states (notably New South Wales and Victoria), the flagrant violation of the mandatory use of face masks led to the imposition of fines to deter people from flouting the norms. As a result, in the first wave, the community transmission had been kept to a minimum. A fourth measure, the use of video to consult with health care professionals, played an important role in reducing community transmission. It reduced visiting health clinics and offices of medical centers. Given that mostly sick people visit them, these areas are naturally at high risk of transmission. In the first month of implementation, between March 15 and April 15 of 2020, 4.3 million medical and health services were rendered to three million patients.8 7

https://grattan.edu.au/news/australias-covid-19-response-the-story-so-far/. https://www.health.gov.au/ministers/the-hon-greg-hunt-mp/media/australians-embracetelehealth-to-save-lives-during-covid-19, accessed September 29, 2020.

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Fig. 4 Mobility data, Australia, March–September 2020 (Prepared with data from Google Mobility Database)

Fig. 5 Restaurant reservations compared—the United States versus Australia (Prepared with data from OpenTable database)

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In fact, a Royal Australian College of General Practitioners survey of 1,180 general practitioners (GPs) found that 99% of GP practices were now offering telehealth services. The same survey showed that 97% of the doctors continued to offer face-to-face consultations.9 We will now discuss the clear sets of failures Australia experienced. The first failure was allowing 2,744 passengers from the cruise liner Ruby Princess to disembark in Sydney on March 19 without quarantine, despite over a hundred passengers having COVID-19 symptoms. In the initial wave, the cruise ship was Australia’s biggest source of infection. Eventually, 663 cases (about 10% of Australian COVID-19 cases in the first wave) and 28 deaths (about 20% of deaths from the first wave) were directly linked to the ship. On April 15, the state government of New South Wales launched a Special Commission of Inquiry to investigate what went wrong. On August 15, the Special Commission submitted its report.10 The report determined many serious mistakes that were deemed inexcusable. It noted the following [italics are mine]: • NSW Health should have ensured that cruise ships were aware of the change to the definition of a ‘suspect case’ for COVID-19 made on March 10. This would have resulted in the identification of such cases on the Ruby Princess; • NSW Health should also have ensured that such persons were isolated in cabins. These were serious mistakes; • The risk rating system used by NSW Health, which saw the Ruby Princess classed as low risk, which meant no action was needed, was “inexplicable as it is unjustifiable” and “a serious mistake;” • No evidence provided to this Commission, or given by witnesses in the public hearings, comes even reasonably close to satisfactorily explaining how a decision to ‘do nothing’ by means of precaution was adequate, or rational; • a directive to allow passengers to travel interstate and internationally, against public health orders was inexcusable; and • under the terms of the Public Health Order, the State Government should have arranged suitable accommodation for all passengers who were not residents of the state. The report suggested adjustments to human biosecurity. Specifically, it invited the state and federal departments to better understand their roles and responsibilities. In summary, the biggest failure in the first wave in Australia turned out to be a lack of coordination between state and federal governments: the immigration department of the Commonwealth Government did not coordinate their actions with the state health department of the New South Wales state government. 9

https://www.racgp.org.au/gp-news/media-releases/2020-media-releases/may-2020/racgp-surveyreveals-strong-take-up-of-telehealth, accessed August 24, 2020. 10 Partially available online at the following location: https://www.smh.com.au/interactive/hub/ media/tearout-excerpt/293/Report-of-the-Special-Commission-of-Inquiry-into-the-Ruby-Princess. pdf, accessed August 29, 2020.

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The second failure occurred with the border closures. Once again, it was the confusing signals and lack of coordination of different measures that created the major problem. Australia quickly banned foreign nationals coming from China. What followed was anything but clear. After the first measure of banning arrivals from China, it followed up with bans on arrivals from South Korea, Italy, and Iran. The choices of the countries were not logical at the time. In particular, it was clear on March 10 that two other important sources of foreign travelers to Australia, namely the United States and the United Kingdom, had unfettered access. The government introduced self-isolation for all international arrivals starting on March 15. There was no follow-up monitoring of the arriving passengers. Testing and tracing did not get priority. In the beginning, some patients with symptoms went to community GP clinics or hospitals first. This action put other patients and healthcare workers at risk. On March 11, the Commonwealth Government decided to open 100 testing clinics. It took two months to complete that task. By then, the peak of the first wave had passed. The testing regime remained restricted during March and April, prior to the acquisition of new testing kits. Thus, many persons who had symptoms could not be checked. This, in turn, increased the chances of community transmission. Community testing on a bigger scale did not begin until April. Australia did not have enough PPE to meet demand timely. The 12 million P2/N95 masks and 9 million surgical masks in the country’s stockpile were not sufficient to meet the demand. General practitioners were not given a priority of getting PPE. Only after March 26, elective surgery was severely curtailed. This action allowed front-line health workers dealing with the pandemic to receive badly needed PPEs. The entire process of procuring supplies of masks and other materials turned into a zero-sum game on a global scale. Australian states started bidding for supplies from overseas manufacturers. In the short run, it was simply driving up the price without any increase in supplies. The Australian government did not have a clear mission for tackling the pandemic. For example, the Prime Minister kept talking about playing (Aussie Rule) football, holding the Melbourne Grand Prix as scheduled even though the reality was very different on the ground. In April (after the Easter holidays), most states fully or partially closed their public schools, but private schools were allowed to make their own choices. The Commonwealth Government repeatedly declared that the children were not at risk. The Prime Minister strongly encouraged parents to send their children to school rather than letting children learn remotely through the internet. The National Cabinet repeatedly reiterated that they were trying to “flatten the curve” rather than trying to eliminate the virus in the country. At the same time, many states and territories showed no local transmission at all for months, proving that elimination can be achieved. Between April 20 and June 23, 2020, Australian daily new infection totals never exceeded two dozen cases. Then came the shockwave of the second wave (Fig. 3). By the end of April, regulations were in place. Arrivals from overseas were quarantined in hotels across the country, along with people from outbreak clusters.

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Two members of a family of four in Dydges Hotel in Melbourne were diagnosed positive on May 15. Three employees were infected as a result. From June 1, 2020, restrictions on movement in Victoria were eased; meanwhile, the chain reaction from the Rydges cases moved to the community. In Melbourne, three quarantined individuals were considered the source of infection. Then, several security guards contracted the virus. These two small incidents started a chain reaction that led to the second wave of COVID-19 outbreaks across the state of Victoria. It was enabled by the removal of restrictions on June 1. From the inquiry launched by the government, it has become clear that the privately employed security at these facilities was very inadequate. The security guards did not receive any meaningful training to deal with coronavirus. They were hired through poorly paid subcontractors. In a submission to the inquiry, it was noted that the “staff members in hotel quarantine breaching well-known and well-understood infection control protocols.”11 On June 24, the state government of Victoria requested the Australian Defence Force to enforce further lockdown only to rescind the order a day later. What followed was a series of desperate acts to clamp down the spread by imposing travel restrictions within various parts of the city along with random (but voluntary) testing in those areas identified as hotspots. All of this was done using privately hired personnel. The result was completely chaotic. People refused to comply with travel restrictions. Fully a third of the people refused to be tested. As shown in Fig. 3, the entire second wave in Australia was driven purely by the state of Victoria. Charles Alpren, an epidemiologist from Victoria’s Department of Health and Human Services, examined genomic fingerprints for three specific groups of travelers that were similar to almost all the subsequent Victorian cases.12 In the aftermath, the Chief Minister of Victoria apologized for the 768 deaths and 18,000 new infections caused by the mistakes. The Health Minister of Victoria resigned.

4

The Policy Response in New Zealand

On February 2, the Prime Minister of New Zealand, Jacinda Ardern, announced a ban on travelers from mainland China. However, the first case was identified only on February 28. Local transmission occurred in the same family the following week. All border entries were closed to non-New Zealand residents on March 15, and those arriving were required to self-isolate for 14 days. By March 25, the country put all of New Zealand into a full lockdown. The only trips for essential needs were allowed. The essential workers are the only people allowed to leave home for work. The number of cases rose rapidly. The country reached over 1000 cases by April 5. Additional measures were taken on April 10: all people arriving in 11

https://www.abc.net.au/news/2020-07-01/victoria-coronavirus-hotel-quarantine-how-did-weget-here/12408256, accessed September 28, 2020. 12 https://www.nature.com/articles/s41591-020-1000-7, accessed September 28, 2020.

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New Zealand had to undergo a 14-day supervised quarantine. By April 15, the daily new cases dropped dramatically to a single digit. The government moved cautiously, dropping restrictions from Alert Level 4 to Alert Level 3 after a month. On June 5, 14 consecutive days of no new COVID-19 cases were recorded. New Zealand celebrated most of June and July by removing all the restrictions within the country. It kept itself closed from the rest of the world. However, after 100 days of zero community transmission, the local “Auckland August Cluster” appeared. Genome sequencing analysis has shown that it started with one single individual in one family. Eventually, it led to some 150 new cases between August and September of 2020. It is instructive to study the questions that we raised in the case of Australia. How much impact did all of this have on the activities of the population? Did the people comply? The two figures below answer those questions. One is the use of public transit in New Zealand. Once the stay-at-home orders (Phase 4) were issued, the use of public transport fell by 90%. For the next four weeks, it stayed at that level. Only when the lockdown was relaxed somewhat to Phase 3 that there was a slight movement up. It gradually built up only to fall again when the second wave hit. Since the second wave did not merit a nationwide lockdown, the fall did not go to the previous low. In New Zealand (as in Australia), the main mode of transportation is not public transit but the use of private cars. Hence, we examine the data on driving to see how effective the lockdowns were. This is presented in Fig. 8. It presents results very similar to the public transit use we examined. Driving fell by 85%; it stayed down in pretty much the same way as the public transit. It rose slowly after the restrictions were gradually eased, only to be clamped down again during the second wave.

4.1 Analysis of the New Zealand Response The Director-General of Health of New Zealand, Dr. Ashley Bloomfield, declared that the strategy was “based on speedy testing, contact tracing and isolation, while rigorously adhering to public health guidance.”13 The Prime Minister of New Zealand set the tone by declaring that New Zealand will fight the virus with “A Team of Five Million”—a reference to the population of the country which exceeded 5,000,000 for the first time in its history in 2020. In early March, the national response was not coherent. The trace and test program was completely overwhelmed by demand by March 10. They were not able to trace half of the contacts.14 Around that time, New Zealand dusted off their 2017 Pandemic Report that had a series of recommendations:

13

https://www.who.int/westernpacific/news/feature-stories/detail/new-zealand-takes-early-andhard-action-to-tackle-covid-19, accessed August 23, 2020. 14 https://www.health.govt.nz/system/files/documents/publications/contact_tracing_report_verrall. pdf.

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Plan For It—through planning and preparedness; Keep It Out—through border control; Stamp It Out—through cluster control; Manage It—through well-stocked hospitals and supplies; Manage It Post-Peak—through vigilance; and Recover From It—through economic measures.15

However, after seeing the quick spread in Italy, the team of the Prime Minister decided to go full scale stamping out policy. On March 18, the Prime Minister implemented an alert system similar to the six-scale alert system that New Zealand had developed for natural disasters like volcanic eruptions. Only a few months earlier, New Zealand was subjected to that system after the volcanic eruption of the White Island. For the economic response to a lockdown, the government promptly deposited money in the bank accounts of all employees if they were not paid by their employers at all. The first thing that the government ensured was that the response was unified across all government departments across all channels of communication. For example, the department of health took the lead in announcing the progress of the pandemic. At a glance, one can find the latest state of affairs16; it gives a summary, managed isolation and quarantine, total cases by District Health Board (DHB), total cases in hospital by DHB, epidemic curve, total cases by age/gender/ethnicity, transmission mode (e.g., community or overseas), laboratory testing results, and number of tests conducted for each day over the entire period. It is a model for any country or region to follow in terms of clarity of information. One can even get information about every single case identified in New Zealand that includes the location, age, sex, and ethnicity. In summary, the communication was clear and concise. The response was unified across all levels of the government of New Zealand. It was also completely open. Very few countries in the world can match that. For all New Zealanders, all information is contained in one website,17 including different subsections: COVID-19 alert system, travel and border, health and well-being, business, work and money, everyday life, and updates and resources. The information is available in two dozen languages spoken in New Zealand. Particularly, in the subsection called Business, work, and money, it gives: “Advice and support for businesses and employees, including what to do if you are experiencing financial distress.” It then has various subcategories. For example, there is one called “Financial Support.” It lays out what kind of support is available for different categories of people. 15

https://www.health.govt.nz/system/files/documents/publications/influenza-pandemic-planframework-action-2nd-edn-aug17.pdf, accessed June 24, 2020. 16 https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus, accessed September 30, 2020). In the following page, one gets the entire panorama: https://www. health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-currentsituation/covid-19-current-cases, accessed September 30, 2020. 17 https://covid19.govt.nz/.

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New Zealand experienced a small second wave in August of 2020. It started in Auckland. A one-person infection led to nearly one hundred new cases. It was stamped out quickly. However, it is still not clear how this new infection started. One suspicion: there was a breach of quarantine. A second hypothesis is it arrived with the handling of imported frozen food by the originator of this cluster. The hard response of New Zealand definitely produced the desired result of making the country COVID-19 free. However, there is a cost: it was achieved through seven weeks of stay-at-home order for all in New Zealand.

5

Should We Compare Pandemic Propagation Between New Zealand and Australia?

The obvious point of comparison for New Zealand has been Australia. After all, they have shared a common legal and economic British legacy. They are similar in many ways. Nevertheless, Australia has five times as many people as New Zealand. Australia is almost thirty times as big in terms of area. In many ways, New Zealand is more comparable to Queensland, the northeastern state of the Commonwealth of Australia. Queensland is similar to New Zealand in many ways. Both have a population of just over five million. An estimated 800,000 New Zealanders live in Australia—a third of them live in Queensland. In Queensland, over a third of the population live in the capital city of Brisbane. In New Zealand, almost a third of the population lives in the capital of the country, Auckland. In both, the rest of the population is scattered in smaller clusters. For the propagation of pandemics, clusters are important. Therefore, we compare New Zealand with Queensland in what follows for COVID-19.

6

Transmission in Queensland

On January 21, a man returning from Wuhan, China, to Brisbane was tested for the pandemic. The state’s chief health officer sent notification to all General Practitioners, emergency departments in all hospitals, and clinical groups in Queensland. The first confirmed case in Queensland was reported on January 28, 2020: a 44-year-old man was put into isolation at Gold Coast University Hospital. In addition, the Chinese women’s soccer team was quarantined in a Brisbane hotel as a precaution. A Brisbane boarding school at Toowong decided to quarantine ten students returning from China. The following day, the Health Department in Queensland declared a public health emergency. On February 1, Queensland Government started tracing nearly four thousand school students who had returned from Wuhan. By the middle of March, only Australian citizens and residents were allowed into Queensland. On March 25, Queensland took the decision to close its

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Fig. 6 Daily cases in New Zealand and Queensland, Australia (Prepared with data from Australia and New Zealand Healthcare databases)

border to other states of Australia with the medical emergency provision. The growth of cases in Queensland that followed can be seen in Fig. 6. The numbers came in slowly in February, took a jump in March. Then it tapered off by early April. By the third week of April, the state had over a thousand cases, but over 90% of them came from abroad. There are two important observations for Queensland: i. first, it declared a medical emergency early. This declaration allowed the state to stop other Australians from other states from coming in; and ii. second, most of the cases detected in the state came from abroad. There was no local transmission in the early stages. These two together allowed the state to monitor cases, test and trace cases with great precision. Several potential clusters appeared. First, in May, a nurse in an aged care center in Rockhampton tested positive, sparking the fear that she might have infected residents. It turned out to be a false alarm. In August, a guard in a Youth Detention Centre in Wacol (in capital Brisbane) tested positive. It quickly spread to some inmates there. Fortunately, it did not detonate into a larger outbreak. Before the outbreak of COVID-19, researchers from the University of Queensland have been examining wastewater in different parts of Brisbane to detect the use of different kinds of drugs in communities. After the outbreak, it became clear that strands of ribonucleic acid (RNA) from this coronavirus could be detected

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by the same method. A large-scale effort was launched in different cities across Queensland to find the virus in the sewage systems. This detection is then used to put a surveillance program in place. For example, in the wake of the Wacol outbreak in the suburb, the sewage test produced high viral loads in certain suburbs of Brisbane. Temporary test centers were set up in those areas to contain the propagation. In New Zealand too, experiments were conducted in Wellington for detection. However, the outbreak was stamped out early. So, the method was not actually used as a practical matter [6].

7

Comparing Queensland and New Zealand for COVID-19 Propagation

From Fig. 3, it is clear that the Australian story became the story of the state of Victoria with the second wave. Queensland followed the trajectory of New Zealand, with no second wave materializing by the end of September 2020. Both New Zealand and Queensland started putting restrictions early (in February). The rise of cases mostly came from overseas returnees. There was hardly any local transmission. Therefore, the strategy followed by both New Zealand and Queensland was the same: quarantine. The policy works as long as they are enforced rigidly. For example, we know now that voluntary self-quarantine often does not work because a number of people do not comply without any penalty associated with noncompliance. To solve that problem, they can be quarantined in designed places—like in a hotel. Then, many questions arise: Who will bear the cost of the stay? Should the people quarantined be forced to pay? What happens if they cannot pay for financial reasons? None of these questions can easily be answered. By the middle of March, New Zealand embarked on an elimination strategy.18 To make that a reality, New Zealand enacted hard border control with quarantine, identification with testing, tracing, and isolation of cases. It required physical distancing with a hard lockdown to restrict movement outside the residence. It needed a high frequency of handwashing and wearing masks anywhere outside homes. Clear communication about the implemented strategies was vital. For Queensland, it required not just stopping international arrivals. It required interstate travel restrictions. International borders are already patrolled by the Australian Border Force. There is no such thing as restricting interstate travel. Moreover, in South East Queensland, Gold Coast is indistinguishable from the 18

https://www.nzma.org.nz/journal-articles/new-zealands-elimination-strategy-for-the-covid-19pandemic-and-what-is-required-to-make-it-work.

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Fig. 7 Public transit use in New Zealand and Queensland (Prepared with data from Google Mobility Database)

Tweed Heads area—except that the Tweed Heads is part of New South Wales, not Queensland. Gold Coast Airport is half in Queensland (the other half in New South Wales). Thus, special provisions had to be made for the bordering areas. Restricting arrivals to Queensland became a much bigger logistical problem than arrivals in New Zealand. The impact of these restrictions on travel by cars and by public transport for New Zealand and Queensland can be seen in Figs. 7 and 8. The public transit index in Queensland fell less dramatically, rebounded much slower than New Zealand, and stayed above it after the second hard lockdown in New Zealand. Similarly, the driving index in Queensland fell less than that of New Zealand, bounced back faster to the pre-COVID level afterward. To summarize, the lockdown was much less disruptive in Queensland than in New Zealand—both entities with roughly the same population size. In Fig. 9, we illustrate the cumulative number of cases in New Zealand and in Queensland between March and July of 2020. The striking feature of Fig. 9 is that Queensland had a less drastic lockdown with 12% lower cases throughout the first wave of the pandemic. If we examine the number of deaths, we find that Queensland had six deaths and New Zealand had 22 deaths during the same period. These observations raise the following policy question: How did Queensland manage to have a lower number of cases and lower number of deaths with less severe and less disruptive policies? Were such harsh policies necessary in New Zealand after all?

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Fig. 8 Driving index in New Zealand and Queensland (Prepared with data from Google Mobility Database)

Fig. 9 Cumulative identified number of Covid cases in New Zealand and Queensland (Prepared with data from Australia and New Zealand Healthcare databases)

There has been a great divergence of COVID infection and death within Australia. The second wave in Australia was almost singularly a Victorian phenomenon. Queensland was no part of it at all. Even being an integral part of the same country, Queensland managed to isolate itself sufficiently to not get the contagion.

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Spill Over from COVID-19

How did health policies spillover from COVID-19 in Australia and New Zealand? The pandemic induced the population to stay at home for longer than they would normally have. They were practicing social distancing much more, they wore masks, and most importantly, took more flu shots.19 In the first four months of 2020, 60% more people got flu shots in Australia. Correspondingly, in New Zealand, 100% more people got flu shots in the first four months of 2020 than in 2019. Figures 10 and 11 show the results of the impact of flu shots, social distancing, wearing masks, washing hands, and other precautions taken by the populations of Australia and New Zealand in 2020 compared with 2019. There has been an overwhelming drop in the incidence of influenza-like illness (ILI). The trajectories tell us that they are not chance events. In February, with no such measures and no extra flu shots, the numbers of people with ILI were not different from the past. However, the divergence becomes very pronounced in both of these countries by April. Influenza is largely a winter disease. It is usually severe between February and August in the Southern Hemisphere. In 2020, there has been an 80% reduction in the burden of influenza in Australia and New Zealand. One counterargument can be made against this evaluation of a large reduction of ILI: people do not normally visit medical facilities if they get ILI. Perhaps because of COVID, the patients are avoiding visiting doctors, thereby reducing the observed frequency of ILI. That is a possibility. Figure 12 gives us evidence that such a scenario is unlikely. It presents the number of deaths from ILI in Australia. Unlike the incidence of influenza, causes of death from ILI are certified by a medical officer. Hence, it is a more reliable indicator of the underlying cause. Figure 12 shows that the numbers of deaths from ILI in Australia in January, February, and March of 2020 were similar to 2019. Nevertheless, by April of 2020, the number of deaths due to ILO fell to almost zero, whereas in 2019, the corresponding figures were in their hundreds. The clear impact of a higher rate of flu vaccinations in 2020, along with social distancing, sanitary measures, and wearing masks, has dramatically reduced the death rates from ILI observed in other years. One advantage of using the data from Australia for ILI to examine the indirect effect of COVID-19 is this: being in the Southern Hemisphere, the rise of COVID-19 midyear coincides with winter. It is well-known that ILI rises sharply in winter every year. Thus, the reduction of deaths from ILI can clearly be demonstrated. This would be difficult to do in the countries of the Northern Hemisphere. There, the summer would normally produce a reduction in ILI anyway. As a result, the positive spillover effect (a positive externality) from COVID-19 on ILI is easily demonstrated for Australia and not for another country in the Northern Hemisphere (say, Canada).

19

https://www.health.gov.au/ministers/the-hon-greg-hunt-mp/media/record-flu-vaccines-in-2020to-protect-australians.

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Fig. 10 Incidence of Influenza-Like Illness (ILI) in Australia, 2019 and 2020 (Prepared with data from Australia and New Zealand Healthcare databases)

Fig. 11 Incidence of Influenza-Like Illness (ILI) in New Zealand, 2019 and 2020 (Prepared with data from Australia and New Zealand Healthcare databases)

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Fig. 12 Monthly deaths from Influenza-Like Illness (ILI) in Australia, 2019 and 2020 (Prepared with data from Australia and New Zealand Healthcare databases)

8.1 Impact on Crime Certain crimes are reduced because of a lockdown. Evidence has been accumulating across the globe. For example, Halford et al. have demonstrated theft saw a dramatic fall in the United Kingdom in March and April of 2020 [7]. Similar evidence has been produced for Los Angeles by Campedelli et al. [8] and Italy by Travaini et al. [9]. The following graph shows the impact of the lockdown in Queensland for stealing from dwellings [7–9] (Fig. 13). Spillover effects from COVID-19 are not all positive. Most impacts are negative. Take the case of domestic violence. There has been a notable increase in violence against women during lockdowns. It has also been observed in Australia and New Zealand.20 GPs in Australia report an increase in the frequency and severity of violence against women. COVID-19 has increased the frequency and intensity of violence against women, according to 59% and 50% of respondents, respectively. Because of the lockdown, it became difficult for many women to seek help.

20

https://bridges.monash.edu/articles/Responding_to_the_shadow_pandemic_practitioner_views_ on_the_nature_of_and_responses_to_violence_against_women_in_Victoria_Australia_during_ the_COVID-19_restrictions/12433517.

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Fig. 13 Stealing from dwellings (Prepared with data from Queensland police database)

9

Public Policy and Elections

When politicians decide on public policies in democracies, they choose paths that are likely to increase their chances of winning the next election. This consideration has played a large role in determining the course of action during a pandemic. In Australia and New Zealand, we have clearly seen the consequences of the pandemic in the election results. In section “The Policy Response in New Zealand”, we documented how in New Zealand, the ruling Labour Party chose a hard lockdown policy early in February 2020. Politically, it was a risky decision. The Labour Party was heading a minority government with the support of a nationalist party called New Zealand First. Many political observers thought such a harsh policy would erode the popularity of the government and cost the forthcoming national election later in 2020. In October 2020, the general election in New Zealand took place. The opposition was very critical of Labour’s harsh lockdown. They had bet that the stance was not popular with the electorate. It turned out to be completely wrong. Labour won a comfortable majority—the first time in nearly three decades after proportional representation was introduced in 1993.

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The election in Queensland in late October 2020 also showed the same results. Harsh lockdowns made the ruling Labor Party more popular. The party won the state election. Even more dramatic results ensued in another state in Australia: Western Australia. The ruling Labor Party undertook massive lockdown measures cutting the state off from the rest of the world and the rest of Australia to prevent the propagation of the pandemic in the state. While tourism was badly affected, it made the Labor Government so popular that it ended up winning 53 out of 59 seats in the Parliament of Western Australia in March 2021—a feat never achieved by any party since Australian independence in 1901.

10

Conclusion

Lockdowns and restrictions work to stamp out the local transmission of COVID-19. This is amply demonstrated in our discussion above. Similar examples have been seen in Taiwan, Singapore, and Vietnam in Asia [10]. It makes politicians and policymakers popular. In some ways, it is somewhat similar to what happens when a country goes to war. Therefore, the lesson is a win–win for the well-being of the country and the politicians who rule the country. Core Messages

• Australia took a zero COVID approach during the phase with no vaccine. • The state of Queensland succeeded in that process and managed to minimize deaths. • The success of Queensland is even more striking because it managed to do so where neighboring states failed. • This gives us an indication of how successful pandemic policies can be implemented in the future. • Policies with a vaccinated population can be very different.

References 1. Rezaei N et al (2021) Introduction on coronavirus disease (COVID-19) pandemic: the global challenge. In: Rezaei N (ed) Coronavirus disease—COVID-19. Advances in experimental medicine and biology, vol 1318. Springer, Cham. https://doi.org/10.1007/978-3-030-637613_1 2. Leung C (2021) The incubation period of COVID-19: current understanding and modeling technique. In: Rezaei N (ed) Coronavirus disease—COVID-19. Advances in experimental medicine and biology, vol 1318. Springer, Cham. https://doi.org/10.1007/978-3-030-637613_5

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3. Blakely EJ, Hu R (2019) The lucky country still? In: Crafting innovative places for Australia’s knowledge economy. Palgrave Macmillan, Singapore. https://doi.org/10.1007/ 978-981-13-3618-8_2 4. Claessens M (2021) A microscopic killer. In: The science and politics of Covid-19. Springer, Cham. https://doi.org/10.1007/978-3-030-77864-4_1 5. Yanez ND, Weiss NS, Romand JA et al (2020) COVID-19 mortality risk for older men and women. BMC Public Health 20:1742. https://doi.org/10.1186/s12889-020-09826-8 6. Hansen SJ, Stephan A, Menkes DB (2021) The impact of COVID-19 on eating disorder referrals and admissions in Waikato, New Zealand. J Eat Disord 9:105. https://doi.org/10. 1186/s40337-021-00462-0 7. Halford E, Dixon A, Farrell G et al (2020) Crime and coronavirus: social distancing, lockdown, and the mobility elasticity of crime. Crime Sci 9:11. https://doi.org/10.1186/ s40163-020-00121-w 8. Campedelli GM, Aziani A, Favarin S (2021) Exploring the immediate effects of COVID-19 containment policies on crime: an empirical analysis of the short-term aftermath in Los Angeles. Am J Crim Just 46:704–727. https://doi.org/10.1007/s12103-020-09578-6 9. Travaini G, Caruso P, Merzagora I (2020) Crime in Italy at the time of the pandemic. Acta Bio-Medica: Atenei Parmensis 91(2):199–203. https://doi.org/10.23750/abm.v91i2.9596 10. Wang CJ, Ng CY, Brook RH (2020) Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. JAMA 323(14):1341–1342. https://doi.org/10.1001/ jama.2020.3151 11. Condon BJ, Sinha T (2008) Introduction to the economic, financial, political and legal implications of global pandemics. In: Global lessons from the AIDS pandemic: economic, financial, legal and political implications, pp 1–24

Tapen Sinha is a visiting professor, Chennai Mathematical Institute, Chennai, India. He is also a Special Professor at the School of Business, University of Nottingham, UK. He has a Ph.D. from the University of Minnesota. Founder and director of the International Center for Pension Research (http://icpr.itam. mx) at ITAM, Mexico, and an Associate of the Centre for Risk and Insurance Studies at the University of Nottingham. He has published over one hundred fifty scientific papers and a dozen books. He has been a consultant for a number of international organizations, multinational companies, and governments on different continents.

12

Environmental Factors Associated with Global Pandemic Transmission and Morbidity Nadim Sharif and Shuvra Kanti Dey

“In its most primitive form, life is, therefore, no longer bound to the cell, the cell which possesses structure and which can be compared to a complex wheel-work, such as a watch which ceases to exist if it is stamped down in a mortar. No, in its primitive form life is like fire, like a flame borne by the living substance;-like a flame which appears in endless diversity and yet has specificity within it;-which can adopt the form of the organic world, of the lank grass-leaf and of the stem of the tree.” Martinus Beijerinck

Summary

The recent pandemic, coronavirus disease 2019 (COVID-19), has been transmitted globally within a very short time. At present, researchers are trying to identify the elements and possible ways to reduce the COVID-19-associated health burden. Meteorological parameters have an important impact on the outcome of a pandemic. This study investigated the effects of environmental factors: temperature (maximum, minimum, and average), relative humidity, UV index, wind velocity, and rainfall on the global epidemic in six continents. Also, it included an evaluation of the possible correlation of environmental factors, population density, and social and religious gatherings with COVID-19 transmission and mortality. Population density and the cases of COVID-19 had the highest correlation. Among environmental factors, temperature, UV index, and relative humidity most profoundly impacted the pandemic. In this

N. Sharif
S. K. Dey (&) Department of Microbiology, Jahangirnagar University, Savar Dhaka-1342, Bangladesh e-mail: [email protected] N. Sharif e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_12

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manner, this study creates a clear concept on the correlation between weather and the ongoing pandemic of COVID-19, providing a guideline for both the local and international authorities to make proper decisions on reducing the health burden of the ongoing pandemic. Graphical Abstract/Art Performance

Impact of various factors on transmission and morbidity of COVID-19 pandemic

The code of this chapter is 01101110 01,101,111 01,110,100 01,101,110 01,101,001 01,110,010 01,101,100 01,100,001 01,000,101 01,101,101 01,100,101 01,110,110 01,101,110. Keywords













COVID-19 Pandemic Population density Relative humidity Temperature UV index

1




Introduction

In the last few decades, infectious diseases, both emerging and reemerging, have become the leading health burden worldwide [1, 2]. Global warming is driving climate change. Though the reasons and required measures are well defined, sadly, the ultimate solutions remain debated and political [1, 2]. Climate change brings about various environmental, social, and economic manifestations that can cause numerous adverse risks to human and animal health [1–4]. Increases in average

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temperatures, UV index, rainfall, and the phenomenon of extreme weather events will have direct and indirect consequences on the environment and habitat in which pathogens, vectors, and hosts thrive and interact [5, 6]. Changes in various environmental factors can support the growth and spread of pathogens that ultimately increase the severity, frequency, and lethality of diseases [1, 4–6]. Among numerous global-scale changes, human-manipulated climate change is contributing to the emergence of numerous diseases. Human actions, namely, unplanned urbanization, deforestation, destruction of habitats of wild animals, and contamination of environmental resources by harmful medical wastes, are increasing the exposure of pathogens to humans quickly [1–7]. Altered and increased interaction of animals, vectors, and humans has resulted in numerous outbreaks, endemics, and pandemics of infectious diseases in recent centuries. Coronaviruses are infectious pathogens of animals and humans [7]. In recent times, a novel coronavirus belonging to the family Coronaviridae has created a health emergency worldwide [7, 8]. The ongoing COVID-19 pandemic’s causative pathogen is severe acute respiratory coronavirus-2 (SARS-CoV-2), emerged in December 2019 [7, 8]. Previously, local outbreaks due to infection with other members (SARS, MERS) of the Coronaviridae family occurred in China and the Middle East, respectively [9]. Generally, coronaviruses infect the upper respiratory tract in humans and multiple organs in other animals [9, 10]. The first two strains of human coronavirus (229E and OC43) were isolated from human respiratory tract infection in the early 1960s [9, 10]. COVID-19 pandemic occurred first in Wuhan, China, with man-to-man transmission capability. The estimated basic reproduction (R0) value ranged from 2 to 5 for COVID-19 [7–10]. As of September 01, 2020, there are more than 25,000,000 cases and 850,000 casualties of COVID-19 reported over 211 regions and countries [11]. Without any prevention and remedy, incidence and casualties associated with COVID-19 are increasing rapidly and have become a major health burden worldwide in the twenty-first century [12, 13]. Clinical manifestation of an infectious disease is the most important part of understanding the health burden. COVID-19 has characteristic clinical symptoms that are puzzling to identify and interpret [7]. The frequency of asymptomatic COVID-19 patients varies from 20 to 70% worldwide. About 70% of patients develop mild symptoms, and infected individuals with less severe to no symptoms have a higher healing frequency [7, 11, 12]. Common clinical features of COVID-19 include increased body temperature (pyrexia), pharyngitis, dry cough, and breathlessness. Other symptoms, including coldness, feelings of shivering, ageusia, anosmia, neuralgia, erythema, and aches, have been reported from patients with COVID-19 [7, 11]. At a severe stage, patients develop symptoms like bronchial pneumonia, respiratory system failure, breathing problem, low blood oxygen levels, kidney failure, and multiple organs failure as well [14, 15]. The presence of comorbidity is associated with most of the COVID-19 fatalities [15]. Many infections emerge and reemerge during winter globally [16, 17]. Viruses, namely, adenovirus, coronaviruses, influenza virus, norovirus, rotavirus, West Nile viruses, and other viruses, contribute to seasonal outbreaks [18–20]. Various meteorological parameters such as temperature (°C), humidity (%), rainfall (mm),

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UV index, wind speed (km/h), and dew point have significantly affected the outcome of the COVID-19 pandemic [19, 21]. Meteorological parameters have occupied significant roles in the viability, duration of viability, and transmission rate of viruses [14–20]. Till now, well-determined transmission modes include direct and indirect contact transmission, aerosol, and non-living objects transmission [24–27]. Meteorological parameters can affect the spread of COVID-19 by controlling its survival duration in droplet nuclei and fomite [24–28]. SARS-CoV-2 is a positive sense, non-segmented, single-stranded RNA (ssRNA) virus with a genome length of * 30,000 bases [7, 16]. Inside host cells, the virus genome can act as mRNA to produce essential viral proteins and a template to produce negative-stranded mRNA. Well-characterized genome of SARS-CoV-2 contains about ten open reading frames (ORFs) (ORF-1ab, ORF-3a, ORF-3b, ORF-6, ORF-7a, ORF-7b, ORF-8a, ORF-8b, and ORF-9b). The first two ORFs, namely ORF1ab, comprise *20,000 bases in the 5′ end and encode for most non-structural proteins (nsps). About 16 nsps (nsp1 to nsp16) are being characterized with defined functions in coronaviruses. Another * 10,000 bases near the 3′ end contain open reading frames encoding morphological proteins [7, 8, 16]. Origin and maintenance of mutations in the coronavirus genome are affected by various regulators like host factors, virus capability of survival, and meteorological parameters. Mutations at spike protein are crucial for both the pathogenesis and host immune responses against coronaviruses. The extreme UV index, higher temperature, amount of moisture, and extreme environmental conditions are responsible for originating mutant viruses among meteorological factors. Besides, human aspects, including immunity, comorbidity, sexuality, and peer group, affect the mutational landscape of infections with viruses, like coronaviruses [7, 8]. The principle objective includes creating a well understanding of the regulators of transmission, mortality, and outcome of the COVID-19 pandemic. For this, we investigate the association between weather parameters and COVID-19 fatalities and transmission through Spearman’s analysis. Another aim of this research work is to address whether population density affects COVID-19-related measures.

2

Methods

2.1 Time and Regions Included in This Study The study emphasized determining the impact of weather, social and religious gatherings, population density, and the total population on COVID-19 in six continents, excluding Antarctica (Table 1). This study included five countries with the most number of COVID-19 incidence from every continent. The study period consists of February 07, 2020, to September 01, 2020.

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Table 1 List of top five COVID-19 affected countries from each continent with the total population, population density, mortality rate, total cases, and fatalities Continent Country Africa

Asia

Australia

Europe

North America

South America

Total population

Population density (P/km2)

South Africa 59,436,725 49 Egypt 102,659,126 103 Morocco 36,985,624 83 Nigeria 206,984,347 226 Ethiopia 115,434,444 115 India 1,382,345,085 464 Iran 84,176,929 52 Bangladesh 164,972,348 1,265 Saudi 34,905,942 16 Arabia Turkey 84,339,067 110 Australia 25,550,683 3 New 5,002,100 18 Zealand Guam 168,775 313 Papua New 8,947,024 20 Guinea Papua New 8,935,000 15 Guinea Russia 145,945,524 9 Spain 46,757,980 94 UK 67,948,282 281 France 65,298,930 119 Italy 60,446,035 206 USA 331,002,651 36 Mexico 129,166,028 66 Canada 37,799,407 4 Panama 4,326,296 58 Dominican 10,866,667 225 Republic Brazil 212,821,986 25 Peru 33,050,211 26 Colombia 50,976,248 46 Argentina 45,267,449 17 Chile 19,144,605 26

Case number

Fatality number

Mortality rate (%)

628,259 99,115 63,781 54,008 53,304 3,853,406 378,752 317,528 317,486

14,263 5,440 1,184 1,013 828 67,486 21,797 4,351 3,956

2.27 5.5 1.85 1.87 1.55 1.75 5.75 1.37 1.24

265,515 24,916 1,408

6,245 652 22

2.35 2.61 1.56

1,287 453

10 5

0.77 1.10

425

4

0.95

1,005,000 17,414 455,621 29,011 331,644 41,486 267,077 30,596 265,409 35,472 6,293,595 190,037 599,560 64,414 128,948 9,126 92,982 2,002 94,715 1,710

1.73 6.37 12.50 11.45 13.36 3.01 10.74 7.07 2.15 1.80

3,950,931 122,596 657,129 29,068 624,026 20,050 428,226 8,919 413,145 11,321

3.10 4.42 3.21 2.08 2.74

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2.2 Data Sources of the Study We extracted the pandemic data from the World Health Organization (https://rb.gy/ ixce4t) and Bing (https://rb.gy/qyuf9z), weather data from meteoblue (https://rb.gy/ ggvgzh) and other databases, and data on different social and religious gatherings in study countries from different national newspapers’ daily news reports.

2.3 Data Testing by Statistics Tools The collected data were categorized and investigated by unbiased statistical methods. We calculated the mean value of cases and fatalities and the case fatality rate. Mean values for average ambient temperature, maximum (Max) temperature, minimum (Min) temperature, wind velocity, UV intensity, relative humidity, and rainfall were calculated during this study for all the countries (Fig. 1). The correlation between weather and COVID-19 was calculated using Spearman’s test (rs) [21]. Further, the impact of social events, religious events, and host factors on the COVID-19 pandemic was deduced by the same equation. Correlation coefficients are applicable for the data without normal distribution. The coefficient equation is given below; P

rs ¼ 1  6

di2 nðn2  1Þ

Here, ‘n’ is the alternative number, and ‘di’ denotes the difference between the variables’ ranks.

3

Results

3.1 Evaluation of Incidence and Casualties Associated with COVID-19 As of September 01, 2020, COVID-19 has infected 26 million people with a case fatality of 3.36% worldwide. The first case of COVID-19 was reported from Wuhan, China. Within a short time, COVID-19 has been transmitted throughout the world. The most affected continent is North America, followed by Asia, Europe, South America, and Australia (Table 1). Both the cases and fatalities of COVID-19 are increasing with time and place. The case fatality rate varied from 0.5% to 13% globally. Though the actual number of incidence and fatalities of COVID-19 is thought to be higher than those are presented, the limited number of diagnostic tests cannot represent the actual burden of COVID-19. Among all the countries, USA had the most number of COVID-19 cases (6,293,595) and fatalities (190,037), followed by Brazil (3,950,931 and 122,596), India (3,853,406 and 67,486), and

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Fig. 1 Flow chart presenting the overall methodology of the study

meteorological

on meteorological

Russia (1,005,000 and 17,414), till the start of September (Table 1). The highest case fatality rate was detected in Italy (13.4%), followed by the UK (12.5%), France (11.5%), Mexico (11%), and Canada (7%). The pandemic is ongoing, so cases and fatalities can change rapidly country-wise, but the fatality rate balances with other factors.

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3.2 Evaluation of Environmental Factors The weather has a major regulatory role in the transmission of COVID-19. Temperature is regarded as the most notable environmental parameter worldwide. In this study, temperature minimum (min), temperature maximum (max), and temperature average (avg) were considered. The temperature of six continents was observed for seven months. The lowest minimum temperature (−18 °C) was recorded in North America, followed by Europe, and the highest maximum temperature (54 °C) was recorded in Asia, followed by Africa. During this COVID-19 pandemic, the highest average temperature value was detected in Asia, followed by South America. The lowest value of average temperature was detected in North America, followed by Europe. The record number of COVID-19 transmissions occurred in Europe and North America within 10 °C to 22 °C avg temperature. On the contrary, the frequency of COVID-19 in Asia and South America was highest at an average temperature of 20 °C to 30 °C (Fig. 2). UV, an important environmental factor, can affect the spread and mutational events in SARS-CoV-2. The UV index varied from 1.4 to 13.2 on a scale of 0 to 14 in six continents during the pandemic. The UV index was recorded between an average of 4 to 8 in Africa, Australia, Europe, North America, and South America. In Asia, the average UV index ranged from 8 to 12. Most of the COVID-19 cases have been transmitted during the months with a UV index of 4 to 8 globally (Fig. 3).

Fig. 2 Distribution of average temperature and COVID-19 cases during the pandemic in six continents

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Fig. 3 Distribution of COVID-19 cases in relation to the average UV index during the pandemic in six continents

Another important environmental factor is relative humidity that can affect the outcome of infectious and emerging diseases. The mean of relative humidity was calculated, and its association with COVID-19 was analyzed. The average relative humidity (RH) varied from 45 to 85% globally. Among the six continents, the mean RH was highest in Africa (Fig. 4). Wind speed and rainfall can regulate the transmission of viruses directly through droplet nuclei. The mean wind speed detected was as low as 1 km/h and as high as 32 km/h. The highest mean of wind velocity was detected in Africa (32 km/h), subsequently in Asia (27 km/h), Europe (21 km/h), South America (18 km/h), North America (17 km/h) and Australia (17 km/h), respectively. The environmental data of the selected countries from six continents revealed that South America (87 mm) had the highest amount of average monthly rainfall, followed by Asia (65 mm), Africa (57 mm), Australia (43 mm), North America (41 mm), and Europe (39 mm) during COVID-19 pandemic.

3.3 Determination of Association Between Weather and COVID-19 Spearman’s coefficient was determined between parameters of weather and COVID-19, and the value of the correlation is given in Tables 2 and 3. As described, major meteorological parameters, namely, RH, UV intensity, wind velocity, rainfall, and temperatures, were analyzed for their association with

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Fig. 4 Distribution of COVID-19 cases in relation to the relative humidity in six continents

COVID-19 cases and fatalities. Other determinants, including total population, population density, frequency of social events, and religious events, were also analyzed. Three frames of time, namely on the day, before seven days, and before 14 days, were selected to evaluate these factors. The highest association of total cases and the max temperature was detected in North America (rs = −0.665), followed by Asia (rs = −0.602) on the day of the case report. The association of min temperature and cases was highest in Europe (rs = −0.602), followed by Asia (rs = −0.511) and South America (rs = −0.506). The highest association between avg temperature and cases of COVID-19 was found in Europe (rs = −0.615). Second, RH was strongly correlated (rs = −0.503) with the case number of COVID-19 in South America on the day. Its correlation in all continents reduced in the course of days. Third, the impact of the UV intensity on the cases was strongest in South America (rs = −0.665), followed by Asia (rs = −0.609) and Africa (rs = −0.564). There was also a negative correlation between RH and UV intensity with COVID-19 and related mortality risk. Fourth, the wind velocity had the strongest impact in North America and South America (rs = −0.512) on the 14th day. The population density of Asia had the strongest impact on cases in Asia (rs = 0.678), and the total population of South America had the highest impact (rs = 0.642) on the COVID-19 cases in South America. Among social and religious events, regional and global gatherings are mainly involved in the COVID-19 pandemic. In Asia, both the religious (rs = 0.542) and social gatherings (rs = −0.706) showed the strongest correlation with increased cases (able 2).

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Table 2 Spearman correlation coefficients of environmental factors, total population, population density, social and religious gatherings, and COVID-19 cases in three frames of time throughout six continents Environmental factors

Africa

Asia

Max. temperature on –0.425* –0.602* the day Max. temperature –0.411* –0.478* 7 days ago Max. temperature –0.379 –0.411 14 days ago Min. temperature on the –0.424** –0.511** day Min. temperature –0.375 –0.374 7 days ago Min. temperature –0.242 –0.277 14 days ago Avg. temperature on the –0.435* –0.598* day Avg. temperature –0.408* –0.475* 7 days ago Avg. temperature –0.388 –0.346 14 days ago RH on the day –0.345** –0.412** RH 7 days ago –0.311 –0.300 RH 14 days ago –0.278 –0.149 UV on the day –0.564* –0.609* UV 7 days ago –0.405 –0.499 UV 14 days ago –0.288** –0.389** 0.223** 0.247** WS on the day WS 7 days ago 0.288* 0.189* WS 14 days ago 0.334 0.099 Population density 0.462* 0.678* Total population 0.545* 0.578* Social events 0.342 0.542 Religious events 0.452 0.706 ** indicates a 1% level of significance * indicates 5% level of significance

Australia Europe

North America

South America

–0.112

–0.511*

–0.665*

–0.564*

–0.101

–0.408*

–0.521*

–0.488*

0.012

–0.321

–0.501

–0.411

0.054

–0.602** –0.489**

–0.506**

–0.032

–0.465

–0.455

–0.401

0.031

–0.265

–0.389

–0.306

–0.154

–0.615*

–0.604*

–0.603*

–0.122

–0.501*

–0.512*

–0.512*

–0.134

–0.446

–0.421

–0.451

–0.231 –0.164 0.103 –0.221 –0.201 0.146 0.021 0.001 0.032 0.321 0.413 0.321 0.421

–0.496** –0.420 –0.239 –0.401* –0.389 –0.275** 0.146** 0.212* 0.402 0.421* 0.511* 0.465 0.521

–0.415** –0.341 –0.211 –0.436* –0.353 –0.331** 0.345** 0.421* 0.512 0.402* 0.512* 0.356 0.421

–0.503** –0.421 –0.367 –0.665* –0.546 –0.412** 0.475** 0.422* 0.512 0.386* 0.642* 0.432 0.521

Spearman’s correlation analysis was also conducted for COVID-19 related casualties in six continents. The findings of correlation analysis are presented in Table 3. Significant correlations were detected between fatalities and temperature, fatalities and RH, fatalities and social events, and fatalities and religious events (Table 3).

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Table 3 Spearman correlation coefficients of environmental factors, total population, population density, social and religious gatherings, and COVID-19 fatalities in three frames of time throughout six continents Environmental factors

Africa

Asia

Max. temperature on –0.225* –0.512* the day Max. temperature –0.211* –0.398* 7 days ago Max. temperature –0.179 –0.351 14 days ago Min. temperature on the –0.324** –0.471** day Min. temperature –0.275 –0.384 7 days ago Min. temperature –0.242 –0.287 14 days ago Avg. temperature on the –0.405* –0.498* day Avg. temperature –0.353* –0.405* 7 days ago Avg. temperature –0.311 –0.386 14 days ago RH on the day –0.245** –0.352** RH 7 days ago –0.254 –0.280 RH 14 days ago –0.128 –0.109 UV on the day –0.289* –0.429* UV 7 days ago –0.231 –0.321 UV 14 days ago –0.195** –0.247** 0.203** 0.198** WS on the day WS 7 days ago 0.288* 0.215* WS 14 days ago 0.324 0.224 Population density 0.402* 0.568* Total population 0.565* 0.597* Social events 0.402 0.501 Religious events 0.442 0.573 ** indicates a 1% level of significance * indicates 5% level of significance indicates not significant

Australia Europe

North America

South America

–0.021

–0.457*

–0.585*

–0.504*

–0.011

–0.398*

–0.431*

–0.478*

0.012

–0.305

–0.391

–0.387

0.064

–0.521** –0.456**

–0.514**

–0.454

–0.387

–0.367

–0.321

–0.347

–0.247

–0.124

–0.614*

–0.547*

–0.589*

–0.112

–0.547*

–0.478*

–0.498*

–0.004

–0.457

–0.401

–0.341

–0.131 –0.024 0.008 –0.019 –0.012 0.024 – – 0.021 0.142 0.248 0.243 0.387

–0.487** –0.321 –0.211 –0.401* –0.398 –0.207** 0.025** 0.187* 0.345 0.325* 0.478* 0.521 0.546

–0.424** –0.241 –0.168 –0.424* –0.373 –0.181** 0.049** 0.245* 0.375 0.442* 0.465* 0.468 0.547

–0.514** –0.378 –0.287 –0.476* –0.413 –0.384** 0.89** 0.214* 0.422 0.486* 0.502* 0.475 0.412

–0.032 –

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3.4 Frequency of Distributions of Age and Gender of COVID-19 Cases Among host factors, gender and age crucially affect COVID-19 transmission. Both the distribution of age and gender of COVID-19 cases were determined (Fig. 5). The percentage of male patients was highest in Asia (65%), and the percentage of female patients was highest in Europe (68%). Both male and female patients were distributed into six age groups. In North America, 68% and in Europe, about 45% of cases were in patients aged above 65 years, but in Asia, about 60% of the cases were in patients aged 20–49 years.

3.5 Analysis of Different Factors Affecting the Outcome of COVID-19 All possible factors impacting the outcome of an emerging disease, i.e., COVID-19, were analyzed to understand the pandemic in detail. Besides environmental factors, other factors are involved in the dynamic and outcome of the pandemic. Environmental factors regulate the outcome of a pandemic with direct and indirect mechanisms of action. Environmental factors with direct effects include temperature, UV, rainfall, wind speed, relative humidity, and extreme conditions like volcano eruption, storm, flood, etc. Environmental factors with indirect effects are mainly human-made disasters, namely, deforestation, unplanned urbanization, water pollution, air pollution, increased interaction with host animals. These environmental factors create a condition where humans’ exposure to hosts and

Fig. 5 Age and gender distribution of the COVID-19 patients in thirty countries during COVID-19 pandemic

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vectors of infectious pathogens increases. After successfully transmitting pathogens to human hosts, the spread of an outbreak like COVID-19 is influenced by other important human activities. In direct man-to-man transmission, the close contact of an infected person with healthy individual increases the risk of a larger and persistent pandemic. Events like social gatherings, religious gatherings, and gatherings at public places have increased the cases of COVID-19 in huge numbers. Intervention strategies taken to reduce the spread of COVID-19, both locally and internationally, have not been useful to minimize the health burden globally. Unless effective preventive measures like vaccines and therapeutics are available, the spread of COVID-19 cannot be controlled by imposing laws alone. The burden of such a contagious pandemic will be reduced to a certain degree if an effective health guideline is imposed internationally and people abide by the laws (Fig. 6).

4

Discussion

With an R0 of 2 to 5, the COVID-19 pandemic has transmitted and infected millions of people throughout the world [7, 29, 30]. Numerous factors are responsible for such an outbreak. Researchers have defined the possible sources and ways of transmission and intervention and preventive measures [7, 8]. An important part of the investigation is analyzing the effects of weather on disease spread and its outcome in an epidemiological study. Numerous research works have detected the impact of meteorological parameters on the COVID-19 pandemic [21, 31]. This study detected a significant impact of minimum temperature, average temperature, and the max temperature on COVID-19 incidence and fatalities, partially similar to studies conducted in China, the USA, and Indonesia in 2020 [21, 24 31]. Şahin (2020) and Tosepu et al. (2020) detected a significant association between COVID-19 incidence and temperature in a single country. Still, we reported a more intense impact of temperature on the risk of COVID-19 in thirty countries on six continents. Instead of four frames of time of Şahin M (2020), this study considered three frames of time for both incidence and casualties. Tosepu et al. (2020) reported no significant correlation between COVID-19 and RH, amount of rain, wind velocity, and precipitation, but in Turkey, Şahin (2020) found a strong impact of weather on COVID-19. Accordingly, we also found a significant impact of RH, UV, rainfall, and velocity of wind on the incidence of COVID-19 globally. No recent studies had detected COVID-19 and UV association, but this study found a significant impact of UV on case numbers in South America and Asia. On the day of the incidence, the impact of weather parameters on COVID-19 was strongest except for the wind speed, which is similar to Şahin M (2020). The impact of weather on pandemic severity was determined by the fatalities associated with COVID-19. Spearman correlation was also calculated to evaluate the impact of meteorological parameters on fatalities of the pandemic globally. We found a notable impact of environmental parameters on the fatality of COVID-19. In

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Fig. 6 Flow chart depicting all possible factors that can contribute to the outcome of infectious disease like COVID-19

particular, temperature influenced the frequency of casualties during COVID-19 in South America, North America, Europe, and Asia. Other contributing factors in the COVID-19 pandemic were total population, population density, social gatherings, and religious events. Total population and population density multiplied both the cases and fatalities in Asia, South America, Africa, and North America. As direct contact transmission is the primary mode of spread of COVID-19, so high population density contributed to increasing the pandemic’s severity. Among other factors, mass gatherings during social and

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religious events have worsened the pandemic by increasing contact with infected persons with healthy suspects. Social and religious events have amplified the pandemic more strongly in Asia than in other continents. Many recent works have detected a noticeable association of COVID-19 with the weather [33, 34]. Previously, Chan et al. (2011) found that increased temperature shortened coronaviruses’ viability in the environment. Significant reduction of coronaviruses survival time was detected above 38 °C in both laboratory and environmental studies [35–37]. Wang et al. (2020) specified that temperature and RH greatly impact the COVID-19 cases in China. Recently, Zhu and Xie (2020), Bashir et al. (2020), Şahin M (2020), and Tosepu et al. (2020) have identified the impact of weather on the COVID-19 pandemic in Asia, America, and Europe. This study analyzed and detected the significant impact of meteorological factors, population density, and social and religious events on the COVID-19 pandemic in thirty countries, with most cases in six continents. This study described the possible impact of weather on the outcome of COVID-19 globally. There was a strong correlation among factors of weather, incidence, and casualties of COVID-19. A complete picture of the major factors affecting the pandemic caused by emerging infectious agents has been depicted in this study. Along with environmental influences, human-made imbalances in climate have significant influences in originating and regulating global pandemic. The changing meteorological factors shape the emergence and re-emergence of both infectious and noninfectious diseases.

5

Conclusion

This study reported a significant impact of weather parameters on cases and fatalities of COVID-19 in three frames of time in six continents. It considered all other possible factors that may affect infectious disease outcomes. Among the three frames of time, on the day of the incidence/casualties, the weather had the highest impact on the outcome of COVID-19. The density of population and incidence of COVID-19 had the strongest association. The size of the population and outcome of the pandemic was also significantly correlated. Movement and gatherings of people are worsening the burden of COVID-19. Though numerous factors varied globally, environmental factors had significant influences on COVID-19 transmission in all continents. Unless immediate contiguity is avoided, physical distancing and personal sanitation are sustained, crowding and gatherings are avoided, and proper health guidelines are followed. Environmental factors cannot contain the spread of the COVID-19 pandemic. COVID-19 might be a demo of the outbreaks that may emerge in the future due to climate changes. It is based on evidence that environmental factors will control both the local and global outbreaks of infectious and noninfectious diseases in the future. This study will provide practical implications for both the policymakers and society to lay hold of appropriate decisions in lessening the disease burden of the COVID-19 pandemic and other infectious diseases in the future.

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Core Messages

• Environmental factors can regulate the emergence of infectious diseases in direct and indirect ways. • Easy mobility and large gatherings of people amplify the COVID-19 pandemic. • Analysis of six continents data reveals temperature, relative humidity, and UV index as correlated with COVID-19 cases and fatalities. • Weather change and human activities can trigger both local and global endemics like COVID-19 in the future.

References 1. El Chakhtoura NG, Bonomo RA, Jump RL (2017) Influence of aging and environment on presentation of infection in older adults. Infect Dis Clin 31(4):593–608 2. Waits A, Emelyanova A, Oksanen A, Abass K, Rautio A (2018) Human infectious diseases and the changing climate in the Arctic. Environ Int 121:703–713 3. Dennis S, Fisher D (2018) Climate change and infectious diseases: the next 50 years. Ann Acad Med Singapore 47(10):401–404 4. Brenner F, Marwan N, Hoffmann P (2017) Climate impact on spreading of airborne infectious diseases. Eur Phys J Spec Top 226(9):1845–1856 5. Smith E (2019) The effect of potential climate change on infectious disease presentation. J Nurse Pract 15(6):405–409 6. Martinez ME (2018) The calendar of epidemics: Seasonal cycles of infectious diseases. PLoS Pathog 14(11):e1007327 7. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Yu T (2020) Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet 395(10223):507–513 8. Cui J, Li F, Shi ZL (2019) Origin and evolution of pathogenic coronaviruses. Nat Rev Microbiol 17(3):181–192 9. Peeri NC, Shrestha N, Rahman, MS Zaki R, Tan Z, Bibi S, Baghbanzadeh M, Aghamohammadi N, Zhang W, Haque U (2020) The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned?. International journal of epidemiology. Published online February 22, 2020. https:// doi.org/10.1093/ije/dyaa033 10. Sohrabi C, Alsafi Z, O’Neill N, Khan M, Kerwan A, Al-Jabir A, Iosifidis C, Agha R (2020) World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). Int J Surg. Published online February 26, 2020. https://doi.org/10. 1016/j.ijsu.2020.02.034 11. World Health Organization, 2020. Coronavirus Disease 2019 (COVID-19) Weekly Epidemiological Update. Retrieved from www.who.int/docs/default-source/coronaviruse/ situation-reports/20200831-weekly-epi-update 3.pdf?sfvrsn=d7032a2a_4 12. Onder G, Rezza G, Brusaferro S (2020) Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. JAMA 323(18):1775–1776 13. Rajgor DD, Lee MH, Archuleta S, Bagdasarian N, Quek SC (2020) The many estimates of the COVID-19 case fatality rate. Lancet Infect Dis 20(7):776–777

304

N. Sharif and S. K. Dey

14. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan. China. The lancet 395(10223):497–506 15. Lescure FX, Bouadma L, Nguyen D, Parisey M, Wicky PH, Behillil S, Gaymard A, Bouscambert-Duchamp M, Donati F, Le Hingrat Q, Enouf V (2020) Clinical and virological data of the first cases of COVID-19 in Europe: a case series. Lancet Infect Dis. Published online March 27, 2020. https://doi.org/10.1016/S1473-3099(20)30200-0. 16. Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, Wang W, Song H, Huang B, Zhu N, Bi Y (2020) Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. The Lancet 395(10224):565–574 17. Vandini S, Corvaglia L, Alessandroni R, Aquilano G, Marsico C, Spinelli M, Lanari M, Faldella G (2013) Respiratory syncytial virus infection in infants and correlation with meteorological factors and air pollutants. Ital J Pediatr 39(1):1. https://doi.org/10.1186/18247288-39-1 18. Nickbakhsh S, Ho A, Marques DF, McMenamin J, Gunson RN, Murcia PR (2020) Epidemiology of seasonal coronaviruses: Establishing the context for COVID-19 emergence. J Infect Dis. Published online January 27, 2020. https://doi.org/10.1093/infdis/jiaa185. 19. Sharif N, Parvez AK, Haque A, Talukder AA, Ushijima H, Dey SK (2020) Molecular and epidemiological trends of human bocavirus and adenovirus in children with acute gastroenteritis in Bangladesh during 2015 to 2019. J Med Virol. Published online April 01, 2020. https://doi.org/10.1002/jmv.25812. 20. Shek LP, Lee BW (2003) Epidemiology and seasonality of respiratory tract virus infections in the tropics. Paediatr Respir Rev 4(2):105–111 21. Bashir MF, Ma B, Komal B, Bashir MA, Tan D, Bashir M (2020) Correlation between climate indicators and COVID-19 pandemic in New York, USA. Science of The Total Environment. Published online April 20, 2020. 138835. https://doi.org/10.1016/j.scitotenv. 2020.138835. 22. Casanova LM, Jeon S, Rutala WA, Weber DJ, Sobsey MD (2010) Effects of air temperature and relative humidity on coronavirus survival on surfaces. Appl Environ Microbiol 76 (9):2712–2717 23. Tan J, Mu L, Huang J, Yu S, Chen B, Yin J (2005) An initial investigation of the association between the SARS outbreak and weather: with the view of the environmental temperature and its variation. J Epidemiol Community Health 59(3):186–192 24. Xie J, Zhu Y (2020) Association between ambient temperature and COVID-19 infection in 122 cities from China. Sci Total Environ 724:138201. https://doi.org/10.1016/j.scitotenv. 2020.138201 25. Guo YR, Cao QD, Hong ZS, Tan YY, Chen SD, Jin HJ, Tan KS, Wang DY, Yan Y (2020) The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak–an update on the status. Mil Med Res 7(1):1. https://doi.org/10.1186/s40779-02000240-0 26. White MS, Omer M, Mohammad GN (2020) Knowledge, attitude and practice on prevention of airborne and droplet infections during the outbreak of corona virus among the College Students in University of Bisha, Saudi Arabia. Int J Contemp Res Rev 11(04):20773–20776 27. Yuan J, Yun H, Lan W, Wang W, Sullivan SG, Jia S, Bittles AH (2006) A climatologic investigation of the SARS-CoV outbreak in Beijing. China. Am J Infect Control 34(4):234–236 28. Bahl P, Doolan C, de Silva C, Chughtai AA, Bourouiba L, MacIntyre CR (2020) Airborne or droplet precautions for health workers treating COVID-19?. J Infect Dis. Published online April 16, 2020. https://doi.org/10.1093/infdis/jiaa189. 29. Cucinotta D, Vanelli M (2020) WHO declares COVID-19 a pandemic. Acta bio-medica: Atenei Parmensis 91(1):157–160 30. Wang C, Horby PW, Hayden FG (2020) A novel coronavirus outbreak of global health concern. The Lancet 395(10223):470–473

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31. Tosepu R, Gunawan J, Effendy DS, Lestari H, Bahar H, Asfian P (2020) Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. Science of The Total Environment. Published online April 04, 2020. 138436. https://doi.org/10.1016/j.scitotenv.2020.138436. 32. Şahin, M (2020) Impact of weather on COVID-19 pandemic in Turkey. Science of The Total Environment. Published online April 20, 2020. 138810. https://doi.org/10.1016/j.scitotenv. 2020.138810. 33. Rabenau HF, Cinatl J, Morgenstern B, Bauer G, Preiser W, Doerr HW (2005) Stability and inactivation of SARS coronavirus. Med Microbiol Immunol 194(1–2):1–6. https://doi.org/10. 1007/s00430-004-0219-0 34. Sajadi MM, Habibzadeh P, Vintzileos A, Shokouhi S, Miralles-Wilhelm F, Amoroso A (2020) Temperature and latitude analysis to predict potential spread and seasonality for COVID-19. Published online March 05, 2020. Available at SSRN 3550308. https://dx.doi. org/https://doi.org/10.2139/ssrn.3550308. 35. Chan KH, Peiris JS, Lam SY, Poon LL, Yuen KY, Seto WH (2011) The effects of temperature and relative humidity on the viability of the SARS coronavirus. Adv Virol. https://doi.org/10.1155/2011/734690 36. Duan SM, Zhao XS, Wen RF, Huang JJ, Pi GH, Zhang SX, Han J, Bi SL, Ruan L, Dong XP (2003) Stability of SARS coronavirus in human specimens and environment and its sensitivity to heating and UV irradiation. Biomed Environ Sci: BES 16(3):246–255 37. Van Doremalen N, Bushmaker T, Munster VJ (2013) Stability of Middle East respiratory syndrome coronavirus (MERS-CoV) under different environmental conditions. Eurosurveillance 18(38):20590. https://doi.org/10.2807/1560-7917.ES2013.18.38.20590

Nadim Sharif is the Research Associate at the Laboratory of Clinical Microbiology and Immunology and Lecturer at the Department of Microbiology, Jahangirnagar University. He has completed both MS and B.Sc in Microbiology with first-class first positions from the Department of Microbiology, Jahangirnagar University. He has been awarded the Government Merit Scholarship for Outstanding performance in B.Sc. He has been working in molecular epidemiology of DNA and RNA viruses, public health, infectious and emerging diseases. He has more than 28 research articles in international scientific journals and four seminar papers. Now he is working with RNA viruses, namely, rotaviruses, noroviruses, chikungunya viruses, dengue viruses, SARS-CoV-2, and DNA viruses, e.g., adenoviruses and human bocaviruses. He is continuously contributing to the field of virology and public health. He is an editorial board member and serving as an academic editor in PLOS ONE journal. He is also an editorial member of Frontiers in Microbiology and Frontiers in Virology journals.

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N. Sharif and S. K. Dey Shuvra Kanti Dey Ph.D., is the Chairman and Professor at the Department of Microbiology, Jahangirnagar University. Before joining Jahangirnagar University as an assistant professor, he has worked in icddr, b. He has completed his MPH and Ph.D. from Tokyo University, Japan. He has been awarded the JSPS fellowship and another postdoc fellowship at Maryland University, USA. He has been working with epidemiology, public health, virology, infectious diseaseand emerging diseases for a long time. He has been an author and co-author of more than 70 articles and more than 40 seminar papers. He has been a peer reviewer of many well-known journals. Now he is working with emerging infectious RNA viruses, namely, rotaviruses, noroviruses, chikungunya viruses, dengue viruses, SARS-CoV-2, and DNA viruses, e.g., adenoviruses and human bocaviruses, and other bacterial pathogens with antibiotics resistant properties. He is continuously contributing to the field of virology, epidemiology, infectious diseases, and public health.

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Urbanization and the Epidemiology of Infectious Diseases: Towards the Social Framing of Global Responses Edlyne E. Anugwom and Kenechukwu N. Anugwom

Water, air, and cleanness are the chief articles in my pharmacy. Napoleon Bonaparte

Summary

The chapter examines the influence of rapid urbanization on susceptibility to epidemics of infectious diseases. Amid the most recent global pandemic, COVID-19, the need for evolving global social responses to epidemics is felt more than ever in the world’s urban areas. In this sense, urban areas, given their defining characteristic agglomeration of populations and human density, proliferate conditions conducive to the generation and spread of infectious diseases. Urbanization is apprehended here as mushrooming living conditions, environmental situations, and lifeways that generally predispose inhabitants to infectious diseases, especially viral infections. However, given that the spread of pandemics overcome spatial boundaries and social differences, in the same manner that rapid urbanization is a global phenomenon, there is a need to evolve social responses to such pandemics and epidemics that are global in nature and definition. Epidemics are not just the subject of biomedical responses but equally demand social framing of responses and options that tackle structural,

E. E. Anugwom (&) Department of Sociology and Anthropology, University of Nigeria, Nsukka, Nigeria e-mail: [email protected]; [email protected] E. E. Anugwom
K. N. Anugwom Integrated Science Association (ISA), Universal Scientific Education and Research Network (USERN), Nsukka, Nigeria K. N. Anugwom Department of Social Work, University of Nigeria, Nsukka, Nigeria © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_13

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behavioral, and attitudinal conditions that affect epidemic risks, especially among urban populations. It calls a need to look beyond biomedical options and equally focus on global social responses that improve urban cities’ health context and enhance human preparedness against pandemics and infections worldwide. Such global social responses followed by concerted actions should go beyond behavior modification and urban renewal to include planned urbanization, enhanced livelihood strategies, sustained health promotion, regular surveillance, people-oriented gentrification, socio-political reforms, and proactive health and social provisioning and support. Graphical Abstract/Art Performance

Framework for a global social response to pandemics in urban cities

The code of this chapter is 01110000 01100100 01101001 01000101 01100101 01101001 01101111 01101111 01101100 01100111 01111001 01101101. Keywords
















Cities Epidemics Global responses Infectious diseases Pandemics Social framing Urbanization




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Introduction

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“Infectious diseases are a major threat in many cities due to population density, overcrowding, lack of safe water and sanitation systems, international travel and commerce, lack of provision of health care services, and poor health care access, particularly in slums” [1, p. ix] (Table 1). The chapter argues that unplanned exponential urban growth has produced squalid housing that will inevitably increase urban residents’ susceptibility to epidemics. However, more crucially, it argues that given the manifold transportation, social, and business interconnections between even the most remote and prominent parts of the world, urbanization has emerged as a common and universal channel for global epidemics and infections. As has been posited, “indirectly, cities affect the health of broader populations through spreading disease pandemics via densely populated bus and train stations, large international airports and seaports” [1, p. x]. Thus, urbanization is not just a universal development catalyst, but also urban areas have become the nodes for the spread and transmission of infectious diseases on a global scale. Because of the largely incontrovertible facts mentioned above, it is only logical that response to global epidemics and pandemics, especially their prevention, be perceived and activated from a global prism that sees no part of the world as truly insulated and untouchable by infectious diseases from any other part. The need to reframe health goals and outcomes within social and environmental indicators is of particular importance. In this sense, healthy lifestyles, health decisions, and interventions are seriously mediated by social realities and environmental events. As a result, even tackling pandemics and positioning the urban community to combat and avoid infectious diseases must take cognizance of the social realities of urbanization and urban living. Thus, it would be wrong for the Table 1 Some urban infectious diseases and spatial origins Disease

High prevalence area/origin

Features/symptoms

Severe acute respiratory syndrome (SARS) Tuberculosis

East and Southeast Asia Africa and Asia Sub-Saharan Africa Sub-Saharan Africa

Atypical pneumonia and even death; fever; dry cough; headaches; breathing difficulties

Monkey pox Malaria (plasmodium falciparum) West Nile virus Coronavirus

East Asia; North Africa Asia (China)

Multiple drug resistance; mild symptoms (blood-tinged cough; weight loss; fever) Animal to human transmission by mosquitoes; acute fever; headaches Secondary transmission by mosquitoes; acute fever; headaches; loss of appetite; nausea; weakness High fever; tremors; stupor; convulsion; loss of vision Respiratory issues; fever; dry cough; loss of taste or smell; suspected origin from animals

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drive towards good health and tackling pandemics to be overtly or dominantly focused on bio-medical solutions and disease-focused measures as much as these are very important. There is no doubt that health policies, even in the so-called rapidly urbanizing societies, have been unduly influenced by a disease-focused solution that neglects the social environment and may explain why health problems and health inequities persist despite interventions [2]. Be the above as it may, the chapter aims to add to our knowledge about the role of the urban environment or urbanization in epidemics and pandemics and extend knowledge on the nexus between urban social ecology and infectious disease risk. As has been argued much earlier, examining the relationship between risks of diseases and urban ecology, particularly regarding the dynamics of urban environmental, behavioral and social life, remains poorly understood [3]. However, while there have been efforts towards understanding the connection between urbanization and health or infectious diseases [4–6], there is no gainsaying the fact that enough might not be known. Thus, there is a need for more knowledge and insights on how urbanization and its social dynamism remain critical to unravel and tackle global pandemics and infections. Apart from the reference to an urban spatial environment, urbanization implies equally major social and economic shifts in society: as social norms and values evolve consistent with urban living or urbanism, so also the economy does by being marked mainly by a shift away from agriculture and agro-based endeavors to mass industry, technology, information and communication technologies (ICTs), and service-oriented economic activities. However, the more urbanized the world appears, the higher the risk of pandemics and global epidemics. The current global battle with coronavirus disease (COVID-19) demonstrates the conveyor belt of epidemics, which urban centers can easily assume, and how despite the undoubted better health and social facilities these urban areas bring about, they can become bastions of infections. According to Fauci et al. [7], about 26% of global mortality in 2002 was related to infectious and parasitic ailments. At almost the same period, the WHO [8] reports that 24% of the global burden of disease, as assessed by Disability Adjusted Life Years (DALY), is attributable to infectious diseases. Therefore, as has been contended, despite the significant advance in microbiological research, infectious diseases continue to persist and yield significant disease burden globally [9]. Moreover, the re-emergence of infectious diseases and the recent global ravage of the coronavirus pandemic have all meant that these diseases continue to exert significant and often overwhelming burden worldwide. Incidentally, urbanization and resultant forms of human density and environmental contexts are invariably conducive to the spread of infectious diseases and equally pose significant challenges to stemming these afflictions’ deleterious impacts. The chapter relied on documentary data and an analysis of literature on pandemics and infectious diseases globally. The literature was retrieved using key terms, such as global pandemics, infectious diseases, new and re-emerging infectious diseases, urbanization, urbanism, urbanization and health, and urban health determinants. However, literature with parochial local and national focus and

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extremely dated literature were excluded. Data sources included the University of Nigeria electronics library collection, WHO/UN-Habitat, UNDP, PLOS, Lancet, Elsevier health, and BMC Public Health.

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Urbanization: Urban Living and Health

According to the United Nations (U.N.) [10], urbanization connotes a shift in human population from dispersion across small rural enclaves or settlements to concentration in large dense settlements called urban areas. While rural settlements are predominantly agrarian and feudal, urban areas’ main characteristics are industrialization and service industries. From a sociological standpoint, most rural areas or settlements are equally characterized by socio-cultural homogeneity and a high degree of informality and interpersonal relationships, mainly on a face-to-face basis. However, the urban area reflects socio-cultural heterogeneity, fluid normative patterns, and a high degree of formality and interpersonal relationships mediated by formal ethos and technological devices. In the eighteenth century, the first industrial revolution created the objective context for the emergence of the urban city as the hub of productive and economic life. Since that time, cities’ growth has continued almost unabated, surviving social-political upheavals and health threats like the first and second world wars and several global pandemics. However, cities’ growth or rapid urbanization has equally thrown up challenges and problems ranging from social pathologies like crime to health challenges generated from these cities’ excess population, especially from the urban area’s conditions and environmental context. As has been argued, the increased human population should be considered a principal factor in the emergence of very rapid urban growth; changes in land use and agriculture, as well as the impact of globalization and all these, are critical and interconnected in the re-emergence of infectious diseases and epidemics globally [11]. However, from a socio-cultural perspective, the emergence of urban cities or areas is a relatively new phenomenon in human social history [12]. There is, however, the feeling that we may not yet know all we need to know about the full potentials and dynamism of urbanization. Nevertheless, no doubt exists about how urbanization has transformed and remodeled human social existence so that it has become almost the most rapidly developing phenomenon and catalyst to human social development in the last century, given its relatively late entry into human social history. This speed of development and growth has meant more complexity and attendant social problems and challenges ranging from common social problems to health challenges and risks. Some of these problems have emanated not only from the fast-paced lifestyle of the typical urban city but fundamentally from the ‘packing’ of people into small spatial spaces and intense human contacts emanating from this. There is also the generation of squalor and environmental decay as the quest for economic survival and desire to realize the perceived opportunity of human life relegate consideration

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of hygiene and sanitation to the background simultaneously as social amenities become overstretched beyond their capacities. The dense human agglomeration in the typical city and the government’s consequent inability to respond with appropriate and commensurate amenities and facilities generate city hovels and slums where the down on their heel urban dwellers and citizens on the lower rungs of the social ladder reside. These urban squalor enclaves are the bastions of social pathologies and health problems, especially epidemics and infectious diseases. Taking a cue from Lampard [13], one can rightly see urbanization as reflecting three interrelated and defining dimensions. These are the behavioral (distinct behavioral patterns and attitudes seen as consistent with urbanization and the urban life); structural (normative patterns of the urban area and the patterned activities and dispositions of the urban population); and demographic (a reference to the large size and more crucially density of the urban population). The above dimensions distinguish the urban area or urbanization from the rural area. However, urbanization as a social reality denotes a dynamic movement towards becoming urban and, in most instances, implies a movement of populations from rural to urban areas and equally the significant growth in the number of urban areas and the increase in human population in existing urban areas (resulting from some social and economic factors guiding fecundity, reproduction, family size preference, culture, and religion). Even though nations nowadays seek to define or set parameters for determining an urban area. The population or number of people who occupy a given area has emerged as a consistent factor. In addition to population, other factors such as population density, the number of people employed in non-agrarian sectors, square kilometers of the spatial location, and government regulations and actions (laws favorable to urban growth and cities created by government fiat or action) are utilized in denoting a human settlement urban or not. It is important to recognize that the interest in and study of the urban city have been approached from multiple angles over the years, especially among scholars. However, the city or urban area remains critical to the apprehension of social organization’s nature and problems and change in human society [14]. While the city’s study has been approached from multiple lenses ranging from the city’s morphology, its multiple challenges or problems to the city as embodying distinct lifeways, or the notion of urbanism, there is no denying that the city is primarily a social construct and reality. To this end, a city is a form of human community and social organization [15]. As a result, the city portends peculiar symbiotic and commensal exchange and interchange processes between human beings within institutions and forms of organizations seen as distinctively characteristic of urban life. As has been argued, “the story of urbanization of human population is the story of change and transformation–change not only in the settlement pattern but change in the normative as well as institutional spheres of social life” [16, p. 23514]. Even in the present contemporary era, the city has become the choice abode of citizens while the countryside and rural areas are depicted as retirees’ bastions or those transiting to the urban areas. Urban centers offer boundless opportunities for self-discovery and self-actualization and places full of economic opportunities

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where the so-called movers and shakers of the society and social amenities and physical facilities are at their prime and pristine. As a result, moving to the urban areas has become a luring prospect for those not there already, and migration to these places worldwide has meant that urban populations have outstripped the carrying capacity of urban infrastructure and have generated enormous pressure on social and health provisioning. One can conveniently argue that barring very few exceptions, there is hardly any thriving urban center in the world where there is a desirable fit between the population and existing amenities/infrastructure. The above results from what has been an almost continued exponential growth of urban areas globally in the last century. According to the U.N. [17], an estimated 4.2 billion or 55% of the world’s population live in urban areas, a jump from 751 million in 1950. These urban centers and urbanization have generated and sustained economic growth, promoting development and general improvement in people’s quality of life. The rate of urbanization reflects the growth and development of a given nation in today’s world, especially since the most developed nations are probably the most urbanized. However, rapid urbanization and excessive human congestion create health risks and make urban inhabitants very susceptible to infectious diseases and epidemics. As has been rightly argued, urban cities’ growth can invariably be concomitant with a higher risk of infectious diseases emerging mainly from the environment, housing, and even human congestion. Thus, the rise of new modern cities and the increasing growth of old ones can create potential risks and challenges regarding emerging infectious diseases [4]. In other words, different risk factors that can be identified easily in the urban environment mainly include inadequate housing, squalid environment, poor sanitary conditions, and human congestion. All these are conducive to the generation and sustenance of insect and rodent vector diseases. Urbanization, especially in the rapid trend during the last five decades, invariably breeds overcrowding and the inability of social amenities to cope. The combination of overcrowding, inadequate access to drinking water, and poor sanitary conditions make inhabitants very susceptible to soil-transmitted helminths [18]. Incidentally, the overburdening of available health facilities ultimately mean limited or severely inadequate response to these diseases by those in charge. Thus, such urban environments do not just breed or proliferate diseases and infections but more critically militate against effective biomedical and even social or behavioral responses. Critical is that the risk of infections in urban settings results from mixed conditions that adversely affect health outcomes and jeopardize even modest healthcare provisioning efforts. Many urban cities, especially inner cities and slum areas, are simultaneously characterized by a dirty, squalid environment, insufficient water supply, poor housing, the dearth of or non-functioning drainage systems, poor sanitation, and human and physical congestion. All of these make it easy for the breeding and proliferation of rodents and insects and for these rodents and insects to be effective carriers and transmitters of pathogens and various infections. As the novel coronavirus has shown, the pathogen gap between humans and animals seems to be shrinking or closing over time. However, often overlooked in urban planning and urban redevelopment efforts is that physical congestion of houses or

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buildings can limit overall ventilation in these areas and expose inhabitants to the risk of respiratory tract infections. Also, the dearth of portable water affects the individual’s ability to maintain good hygiene and a sanitary home environment critical to keeping diseases and infections at bay. Some urban areas may suffer from the ravages of using unsuspected contaminated water. Such water can produce and spread diseases, especially diarrheal diseases. However, crucial to recognizing the nexus between urbanization and health is the phenomenon of slum dwellings in urban cities. Urban growth has equally meant the rapid growth of urban slums. Urban slums are the epicenters of urban decay and squalor, and given the recent global experience of pandemics, these slums meet the criteria of likely hotspots for global pandemics. In other words, globally, rapid urbanization has gone together with the rapid growth of slums as blighted areas of the city and the main domain of members of the society at the lower socio-economic ladder. These slums with broadly similar characteristics across the globe may provide universal and uniform conditions for pandemics. Often, those who reside in the slum work, school, or engage in various other activities that expose them to other society members from better areas of the urban environment. Thus, the slum, even though a distinct geographical location, is neither insulated nor exclusive of other areas of the urban city. Human movement and interaction in the urban areas are thus the building blocks of diseases and infection transmission.

3

Theorizing the Nexus Between Urbanization and Infectious Diseases and Pandemics

We would approach our concern from a predominantly social science perspective anchored on the theoretical nexus between urbanization and social pathologies, including health. In this sense, we treat urbanization as a process that is both driven and generative of urbanism, i.e., the cultural values, orientations, and attitudes (or the so-called ‘city culture’) linked to urban life. Given the above, we see the environmental risk transition thesis or theory [19] as very germane to our concern to apprehend the nexus between urbanization and health (epidemics) and, from that perspective, outline global social responses for tackling such health challenges. One way of theoretically understanding the connection between urbanization and pandemics or global epidemics is to adopt the environmental risk transition thesis [19]. Smith [19] categorized environmental factors that lead to ill health into mainly modern or traditional. In this sense, the major environmental sources of traditional ill health are issues associated with the household or at the household level (e.g., sanitation, water, ventilation, air pollution). As these traditional sources are tackled through development, there emerge environmental causes of contemporary health ailments at the community level. Equally, as the modern diseases which operate at the community level are tackled, another transition occurs, which increases the influence of environmental hazards at the global or world level [20, 21]. In other words, this theory sees a three-stage transition in the lead to the

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evolvement of global epidemics. However, urban factors and the typical urban area’s challenging health environment are culpable in the movement towards global epidemics and infectious diseases. As Smith and Ezzati [22] contend, there has been little effort to apply this theory’s insights to understanding new and re-emerging infectious diseases (EIDs) produced by such human activities as tourism, trade, terrorism as well as human interactions with the natural environment. Perhaps, travels and tourism anchored on the crisscrossing of urban areas worldwide as critical nodes of human movement are crucial in transforming urban diseases into global epidemics. This perception’s plausibility is borne out slightly in the SARS case and more eloquently in the recent COVID-19 pandemic taken by travelers and human hosts from Wuhan to far-flung and even remote parts of the world. The manifestation of the urban areas as epidemic hubs is enabled by the combination between population concentration and high human density with predictable fragility of urban social and physical carrying capacities, i.e., urban infrastructure and social amenities including healthcare delivery are eroded and overstretched by human agglomeration and excessive population. The above picture is further worsened or made more precarious when rapid urbanization generates environmental squalor and vector/rodents’ friendly human settlements.

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Urbanization and the Spread of Infectious Diseases Within and Across Cities

The large population in the typical urban area invariably means high density, i.e., where many people are packed together in residential areas. Such a high human density situation is also concomitant with very close contact between people conducive to the rapid spread of diseases, especially viruses. The ravages of such diseases as the avian flu, Ebola, severe acute respiratory syndrome (SARS), and COVID-19 can be linked largely to close contact between people. The emphasis on ‘social’ distancing in response to COVID-19 derives from the above recognition. However, even the best attempts at such recommended social distancing are often sabotaged by human density and the typical urban city’s usual hustle and bustle. As one can expect, rapid and especially unplanned urbanization exerts negative impacts and pressure on such critical amenities as potable water, energy, housing, sewage, social and physical environments, and, more crucially, public health. In other words, such urbanization portends grave consequences on health and, by implication, exposes urban dwellers to high risks of infections and epidemics. While the above situation may be rightly generalized for a good number of urban centers in the world, there is no contesting the fact that these problems and pressures are very acute and complicated in the various urban slums, hovels, and neglected inner-city residential areas that have become definitive features of the developing world’s urban environment. In such places with clear potency to generate and proliferate infectious diseases, public health goals are jeopardized, and

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almost non-existent as low socio-economic status and government neglect make health a heavily privatized and inaccessible commodity. Therefore, even within urban cities, the area of the city an individual resides in may influence the individual’s health risk. It would generally seem that those who live in urban slums with their characteristic squalid environments and the dearth of social amenities have a higher health risk, infection wise than other residents. However, the other urban city areas may not be insulated from infections in the slums, given the movement and multiple interactions in a typical city. Even though the city may be portrayed as the center of opportunities and better amenities than the rural areas, diseases may find favorable outlets in the urban environment, and infectious diseases can spread rapidly in these urban areas with very high disease burden and overall consequences. In other words, “looking beyond the bustling marketplaces, skyscrapers and big city lights, today’s cities across the world contain hidden cities, masking the true lives and living conditions of many city dwellers. Certain city dwellers suffer disproportionately from poor health, and these inequities can be traced to differences in their social and living conditions. No city is immune to this problem” [23, p. iv]. Therefore, there has been rapid urbanization and the rapid proliferation of conditions injurious to health, especially in the developing parts of the world. Thus, “in the past 50 years the global human population has exploded, and nearly all of that growth has occurred in the cities of the developing world. Here, the majority of the urban population typically lives in substandard housing with no electricity, water, waste management, or sewage systems. It creates ideal conditions for increased mosquito-, rodent-, water and food-borne infectious diseases, as well as for sexually transmitted and communicated diseases” [3, p. 114]. The slum is the prime breeding ground for disease-bearing rodents and insects. In Africa and other developing tropical zones of the world, malaria’s ravage is related to the squalid urban environment and poor sanitation in such an environment. Beyond this, the slums are areas with a high rate of infectious diseases and epidemics. Thus, diarrheal infections like cholera have been associated in so many countries with urban areas with high population densities, as typified in the slum [5, 6, 24, 25], especially in the developing world. On the other hand, such an infectious disease like tuberculosis has equally featured as an urban menace related to high population density and overcrowded housing even in the developed world [26–28]. In other words, urbanization and the serious erosion of the living environment and dearth of amenities consequent upon it seem more conducive to infections and epidemics than the typical rural environment. While health threats do not discriminate spatially, the human congestion, pollution, poor environmental and personal hygiene in the typical urban area make it readily susceptible to infections and epidemics. In effect, “slums are productive breeding grounds for tuberculosis, hepatitis, dengue, pneumonia, cholera and diarrhoeal diseases, which spread easily in highly concentrated populations” [1, p. 28]. Without a doubt, living in squalid environments compounded by the dearth of or lack of basic amenities like water exposes the inhabitants to the risk of infectious diseases and recurrent epidemics. Therefore, as the urban growth rate increases, so

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do the risk of epidemics and infectious diseases. The above scenario is not helped by the dearth of public health facilities and the inaccessibility of available ones to many urban residents in squalid environments due to poor socio-economic status and lack of government social provisioning. The coronavirus pandemic has served as a wake-up call to governments and their agents globally over the dilapidation and gross inadequacy of public health systems. These problems have been more acute and heightened in the developing regions of the world, which, even before the pandemic, were struggling with institutional and structural problems limiting public health response and effectiveness [29]. However, recognizing a given problem, even though critical, is not tantamount to solving that problem. Hence, there is a need for urban growth to be matched with adequate housing provision and basic social amenities. Incidentally, urban deterioration and squalor are not characteristic of only the urban areas of the developing parts of the world but are conditions that can be globally generalised. Thus, “physical and social-spatial segregation, increasing congestion and pollution, and shortages of adequate housing are conditions found in both developed and developing countries, though in varying levels and intensities” [30, p. 5]. As we are experiencing in the COVID-19 pandemic, infectious diseases do not just breed and remain in one section or area of the urban city just as much as they do, not also only remain in one urban city of the world. Infectious diseases that originate in one urban quarter easily get transported or circulated to other areas of that city. In other words, pathogens with origin in a given urban residential area circulate to all areas through the various transportation linkages in any modern city that connect all the different quarters. More than the above, human beings as social beings maintain interactional linkages and bonds that often belie social status and reinforce primordial and labor linkages. In the above sense, the poor or those who find a residence in plighted urban areas do not just interact with other ‘unfortunate’ residents or poor people because of social status. These people often share primordial linkages with the more fortunate city residents. Moreover, such people are often the source of the typically exploited labor that service the opulence and privilege of those who live in the city’s better and socially upward parts. These people, for example, are the gardeners, domestic servants, drivers employed by these residents, and even city councils for a myriad of services. However, mere cursory observation and reflective experience reveal that people often travel or move around with their infections. This realization is not helped by the fact that some infections remain largely asymptomatic and have incubation periods that may elude the carrier or even routine medical examination. The above strongly suggests that there is a high probability (and actual cases as exemplified in the near pandemic of SARS in 2003 and COVID-19 pandemic in 2019–2022) that infectious disease in one remote urban neighborhood or quarter can spread rapidly throughout that city and even across state and national boundaries. Both infectious diseases emerged in China’s urban areas (Guangzhou and Wuhan, respectively) but had gone far beyond these cities and China within weeks and months.

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Therefore, the realization that infectious diseases do not get locked down in a particular urban quarter or a country makes it necessary that responses and actions be inclusive and global. Despite undoubted differences in social and physical development, no part of the world, as we have recently seen, is either immune or free from any serious infection, no matter the spatial origin or source. The above-generalized risk has become even more heightened. As the cases of SARS and COVID-19 have shown, the increasing linkage between humans and animals is creating or exposing conditions or cases of typical zoonotic pathogens that crossed the species barrier to find human hosts. The above cases and increasing likelihood of more cases while not necessarily validating the claims of animal rights activists and vegetarian campaigners call attention, all the same, to the relationship between man and animal. It speaks to the need to ensure ecological balance and a more conscientious but health-informed relationship between the species.

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Urbanization: Infectious Diseases and Global Pandemic Risks

There is no denying that the mixture between high human density and inadequate social and physical amenities, poor public sector provisioning, and the dearth of/or inaccessibility of medical services, as one confronts in a typical urban city, constitute a pungent environment very conducive to the spread of infections. Such urban abodes expose the inhabitants to a high risk of infectious and parasitic diseases. It is the contention that “the risk for urban infectious disease outbreaks is greatest not only where the population density is highest, but also where people, public infrastructure, and public services are poor, and where access to medical care and basic health programs does not keep pace with population growth” [3, p. 19]. Curiously adding to the potent mix of urban congestion and the mobility of infections is the rapid increase in international travel and tourism in the last three decades. There is little doubt that the urban areas worldwide are the epicenters and main nodes of such travels and tourism. According to the World Tourism Organization [31], international tourism and arrivals are expected to increase, estimated to be around 1.8 billion by 2030. This projection was without bias to the impact of the COVID-19 pandemic on international travel and tourism. However, the severe restriction on such movements globally in 2020 as a response to the pandemic should be expected to have a negligible impact on people’s desire, need, and appetite for travel and tours. Thus, “a unique feature of the 20th-century re-emergence of infectious diseases has been the rapid global spread of some infectious agents, such as SARS, avian influenza, West Nile Virus, and dengue. This global spread is tied directly to modern transportation and globalization, both of which are directly dependent on the major urban centers of the world” [3, p. 114].

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The likelihood for urban areas to function as catalysts for the spread of infectious diseases is anchored on the potent combination between human density and the deteriorating environment and housing conditions in these urban areas. Thus, increasing urban population, human density, and inadequate planning and reform policies may easily enable urban areas to fulfill large-scale epidemics criteria. Urban areas around the globe, even while spatially separated, are equally interlinked through manifold channels of transport-driven travels and tours that expose humanity to pandemic risk almost daily. Viral infections are gradually bridging the gap between animals and humans. As we have largely seen in the most recent case of the coronavirus with zoonotic origins in Wuhan, China, infectious diseases may travel as widely and as frequently as their human hosts. It was the same scenario with the SARS, which occurred earlier. SARS was suspected of having originated from SARS-like coronavirus (SCoV) of bats but eventually reached human hosts in Guandong, China, due to humans hunting and trading in bats for food [32]. Despite how these infections reached human hosts, they were easily moved from China to other parts of the world with such speed that underscores the influence of travels and urbanization in global pandemic and disease risks. Given that rapid urbanization has emerged as a common characteristic of the globe and the growth of slums, squalor, and filthy environments, it seems logical that the risks of infectious diseases and epidemics are closely tied together. In other words, rapid urbanization, the way it has gone so far, with little imposed restraint, exposes the world to generalized epidemics or pandemics. However, there is the opinion that efforts should not be geared towards restraining or controlling urban growth or urbanization per se, but rather efforts should be geared towards productive cities and support local and national governments [33]. Despite the above, the rate at which the coronavirus spread throughout the world between late 2019 and early 2020 is evidence of the power of infections to override national or state boundaries easily. In such a situation, response-preparedness should be pursued from a global perspective, which, while cognizant of the distinct social differences between regions and countries, is equally conscious of rapid urbanization well as the proven ability of infections to overcome spatial boundaries. In a manner of putting it, while globalization has been a factor in the spread of infectious diseases [34, 35] and there has been talks about de-globalization as the route for the world to post-COVID-19; there is a need to recognize that undeniable similar social and developmental realities like urbanization and rapid human mobility make de-globalization as an antidote to pandemics untenable. Therefore, the world needs to adopt responses that combine both the particular and universal in readiness to avoid the befuddlement and helpless bewilderment that characterized the COVID-19 response in the first three months.

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Urbanization, Deglobalization, and Stemming Pandemics

While it may be attractive and largely incontrovertible to argue that the scourge of rapid urbanization, especially the lifestyle it breeds and the associated infectious diseases risks imbued in this, may be more heightened in the developing world [3], there is no gainsaying that the negative influence of urbanization is much more global than a regional or isolated spatial fact. In other words, while the developing world may portray a dire picture of the above, the recent experience of infectious diseases like SARS and COVID-19 has demonstrated that a global perspective in the understanding and response to these infections may be more fruitful. The emergence of urban areas as easy hubs and nodes of transmission of these diseases underlines urbanization’s global ramifications on infectious diseases. Despite wherever these diseases originate from, the potency of urbanization to facilitate their quick spread remains undoubted and should feed into our perception and responses to them. Moreover, given the overwhelming choice of destinations in the global North as tourist and settlement domains, the urban centers in this part of the globe are at risk of global pandemics as the urban centers in the global South. Urban areas now serve as international travel nodes and are interconnected through crisscrossing flight routes and the sea. The above indicates that these urban areas, beyond the risk of infections generated within them, also have the undoubted capacity to distribute such diseases and infections literally. Perhaps, the notion of a global village finds purchase in the global pandemic risks associated with urbanization. Globalization has thus not only boosted the interconnectedness and manifold nexus between different parts of the world but has equally boosted the status of urban cities as conduits in the spread of infectious diseases on a global scale. Without a doubt, globalization and the literal shrinking of the world consequent upon it has opened up massive interconnections and linkages between people and places. As a result, there is hardly any part of the globe that is truly remote and isolated. At least, not in the sense of these words about three to four decades ago. Therefore, we live in an age where tremendous improvements in transportation and technology have powered an equally exponential growth in human mobility. Mobility is driven by personal, business, and leisure purposes. It is so rapidly intense and expansive that so many hitherto remote areas of the world have become swarmed by tourists, economic opportunists with interests in mining, oil, lax financial regulations, grey speculations, and even the occasional fugitives and political villains. Even the proverbial islands and land-locked nations of old have found favor among leisure travelers, businesspeople, revelers, and retirees looking for new environments and quiet abodes. There is ample evidence that many diseases and infections have been transported across international boundaries through travel [36–38]. In other words, human beings, when they travel, usually travel with their diseases and infections. However, while known infectious diseases can be targeted through health checks, targeted restrictions, and certification (e.g., Yellow Fever vaccination cards) at points of

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departure and entry, new epidemics, mutations of known diseases, and asymptomatic carriers prove difficult to detect. The above situation, especially in the aftermath of the pandemics and epidemics, may breed fear and momentarily limit international travels, which, as we have seen in the case of the COVID-19, has severe deleterious impacts on the economic growth and social well-being of people. However, the world’s coming together is also conducive to the ravages of pandemics with origins in far-flung places. This reality has generated panicky de-globalization fervor. Nevertheless, there is a need to recognize the obvious futility of such desires. In other words, it is improbable to return to a de-globalized world where international movements are severely curtailed, and nations seek to go it alone. This impossibility stems from the inexorable entanglement of nations built over centuries of international connections and movements. Therefore, the way forward would seem to be in using globalization or the metaphor of a ‘uniform world’ it embodies to reverse engineer the ravages of pandemics and improve preparedness through global responses or practices and options that include both biomedical advances and social framing of the ubiquitous reality of urbanization in today’s world. In the same manner that hand sanitization, masking, and social distancing have been globally promoted as antidotes to the spread of COVID-19, so can urbanization practices and strategies that limit the risk of infections and epidemics be universally promoted or fostered across the globe.

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Framework for Global Social Responses to Infections and Pandemics

There can be no gainsaying that the looming threat of infectious diseases in urban areas also demands a well-thought-out urban epidemics’ preparedness. In other words, political leaders and city planners, and administrators globally must factor in the likelihood of the emergence or spread of infectious diseases in these centers of high human agglomeration. Preparedness would, among other things, include the need to close the financial gap between the estimated impact of pandemics and the cost of preparedness. Therefore, it is prudent to recognize that pandemics’ huge financial burden, as we have seen with the coronavirus disease 2019, demands that financial resources needed for preparedness match the estimated pandemic cost. Despite the stated deleterious impact of urban areas on infectious diseases, these cities also portend new opportunities and windows for pandemic preparedness and tackling health emergencies [39]. In this sense, the urban milieu presents the best opportunity for securing the involvement and commitment of political and policy leaders and the utilization of the manifold nexus in the typical urban area in building reliable health networks that can facilitate prevention, detection, and early response. In other words, one apparent disadvantage of the urban milieu can be reversed and utilized in tackling pandemics and diseases. The relatively improved socio-economic status of the average urban dweller makes it possible for them to develop sensitivity to health emergencies, easily succumb to extensive quarantine

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and isolation, and function as family and peer health educators. There is no doubt that those who are better educated and socially exposed would perceive both health threats and the need for action at the individual and household levels much more readily than those on the opposite side of the socio-economic ladder. Be the above as it may, the following strategies can be adopted not only in achieving the social framing of responses but in embedding pandemics aversion and preparedness into the core of city administration globally and in deepening effective conventional and best practices universally:

7.1 Inclusive Urban Governance It entails recognizing the social capacity and agency of city or urban dwellers and keying into these to evolve health responses and strategies that address both real and apparent pandemics drivers. There is a need for a global compact and agreement on urban governance’s inevitability to ensure a healthy urban population globally. Without a doubt, urban governance is very imperative to the quest for good health and general well-being of urban dwellers and privileges the ability of individuals to deploy their endowments and skills in the quest for improved social and economic conditions in these areas. Therefore, the process of both urban planning and administration should be inclusive, open, and focused on enabling city dwellers to improve their health and social well-being across different levels of the socio-economic ladder of the city.

7.2 Inclusive Urban Health and Housing Policies Squalid living environments portend deleterious health and social impacts. Such environments as experience and recent history show are the epicenters of infections and epidemics. Health and housing policies should target inner-city residential areas and slums to arrest and decrease the urban population’s susceptibility to epidemics. Such policy thrust should go beyond mere gentrification to target the provision of matching physical and social needs of urban dwellers, focusing especially on low-income and marginal urban society members’ health needs.

7.3 Effective and Proactive Surveillance While disease surveillance is a conventional practice in public health, it will seem that urban pandemics’ reality demands new forms of surveillance. In this case, surveillance should be deepened and targeted at all forms of emerging and re-emerging infectious diseases and shun the outright dismissal of observation of new forms of infections by public health professionals. The pandemic of COVID-19 would have been avoided or rendered an epidemic limited in geographical scope if the initial alarm sounded by a certain Wuhan medical practitioner

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had not been treated with levity. However, beyond the above, there is a need to share surveillance information among nations regarding infectious diseases. Adopting the sort of universal cooperation and compact that exist in the fight against narcotics and crime (money laundering and cybercrime), surveillance can be used to provide early warning and stem the tide of urban infectious diseases on a global scale.

7.4 Dynamic Health Promotion Health promotion has been an old pillar of public health and still promises to be a good route towards tackling urban infectious diseases and even global epidemics. There is, therefore, an obvious need to upscale or improve health promotion to address emerging and re-emerging infectious diseases. Thus, health promotion should embody a sustained global drive to improve public knowledge about infectious diseases and their manifestations as well as preventive practices and attitudes [29]. One good lesson the world has learned from COVID-19 is the importance of washing hands regularly and the need for sanitization of hands that we unconsciously use in touching sensitive body organs and food. These practices that border on behavior modification can be sustained through global knowledge creation and awareness as equally good antidotes against future pandemics and epidemics.

7.5 The Global Compact on Health Equity In this sense, urban planning and administration should prominently embody strategies and programs for achieving and sustaining health equity. In this way, cities should not be places where health inequities determine individuals’ survival and longevity, but where no one is systematically deprived of attaining their full health potentials. Without a doubt, health inequities are systematically and socially produced and reproduced in the urban setting. While economic conditions may matter for health access, there is no denying that health should ideally be made part and parcel of social goods. This means that city and political authorities have a fundamental part to play to ensure that people’s health pathways are neither impaired nor totally denied based on economic and social considerations. The growth of cities should be commensurate, in this case, with growth in access to health and equity in health across cities around the world.

7.6 Global Social Compact on the Marginalized and Poor There is a need for a universal pursuit of social programs and actions that focus on alleviating the marginalized and poverty status of many urban dwellers. Such programs would target affordable and good housing, basic amenities, environmental

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remediation, and inclusive health policies. The above in terms of specific actions would include the following: • Owner-occupier housing schemes; • Water and sanitation programs, which go beyond previous efforts that were heavily politicized and thus achieved little; • Slum redevelopment responsive to the need for better housing and a healthy environment and not driven by the desire to dispose people of their houses and accommodation (or even right to city living); • Open and public shelters–for the homeless and street vagabonds that are run through public funds and provide two hot meals daily; • Public health policies to reflect a bias towards the needs of those who occupy city hovels and slums; • Small scale clinics and health centers determined by both population density and accessibility; and • Provision of accessible/affordable social amenities and basic infrastructure, including good schools, day-care centers, security/humane policing, sports, and recreation facilities. Like Korea, urban areas can develop complex simulation exercises to ascertain the most potent public health response to stem the tide of infectious diseases within the urban town or city [40]. Research and even evidence show that digital and web-based information can be deployed in developing systems responsive to infectious diseases in urban settings [41]. There is also no doubt that a good number of simulated responses, modeled patterns of interventions in health emergencies, systems for improved surge capacity, various contingency plans exist in different urban cities globally, especially in the West. However, what has been lacking is the desire to share best practices and experiences globally. The need for these cannot be over-emphasized, given what the world has undergone recently. Therefore, there is a need to perceive pandemics and infectious diseases as global concerns and, as a result, frame response on a globally concerted scale.

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Conclusion

“Environmentally, there are advantages to be gained from urban agglomeration and compact urban forms, but some of the most urban advantages require urban infrastructure, policies and planning that support the transition to more resilient, healthy and sustainable cities” [33, p. 5]. Undoubtedly, the city offers both salubrious and deleterious effects on health and well-being. In other words, “cities offer both the best and worst environments for health and well-being. Multiple determinants converge to influence the health status of city dwellers, and positive and negative influences tend to cluster according to the specific neighborhood or “place” within the city” [1, p. 12].

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In line with the postulations of the environmental risk transition thesis, the urban areas are critical transition routes and nodes in the movement towards global pandemics. Therefore, the itinerant nature of the human species, interconnecting and crisscrossing urban travel routes, and globalization, in general, have further aided the likelihood of urbanization breeding and sustaining infectious diseases and pandemics on a global scale. Thus, a critical issue in the city or urban areas globally is how the living pattern (housing) and the urban environment affect health and well-being. It would seem largely incontrovertible that urban housing provision in most parts of the developing world is deplorable and begs for remediation as it has multiple influences on health and social living. Perhaps, it was in realization of the above fact that the UN’s Sustainable Development Goals (SDGs) number 11 intends to make cities inclusive, safe, resilient, and sustainable. Especially pertinent to health, particularly regarding epidemics, are issues of inclusiveness, safety, and sustainability. Achieving these aims would entail building and repositioning city housing and urban environments to be a stimulus for good health while drastically reducing the risk of infectious epidemics. Without a doubt, recurrent epidemics and other health challenges undermine cities’ sustainability and endanger citizens rather than foster safety, development, and general human social improvement, often touted as selling points of global urbanization. The chapter makes a case for the social framing of global responses to infectious diseases. It is borne out of the recognition that while varied socio-political and economic structures can differentiate different parts of the world, social problems and challenges are similar even when manifesting differently. In other words, while the expenditure of resources on biomedical treatment and research remains very viable, efforts should be equally put into understanding the social drivers of health risks in urban areas and how such insights could function as the basis of widespread social response to health emergencies like pandemics. It is reasonable to expect that improved sanitation and personal hygiene can have a salubrious impact on infectious diseases, as global experience recently and in the past has shown. However, the above requires behavior modification and change and more critically systematic resocialization and policy-backing to be widely adopted and sustained over time. Core Messages

• The chapter argues for reframing health goals and outcomes within social and environmental indicators. • Growth in urbanization has equally implied an increase in risks of pandemics globally. • Urban squalor enclaves are bastions of social and health pathologies consistent with epidemic and pandemic risks. • Urbanization posits a three-stage transition in global epidemics’ evolution in urban areas. • There is a need for urban planning and policies to accommodate urban pandemic risks and foster healthy cities globally.

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References 1. WHO/UN Habitat (2010) Hidden cities: unmasking and overcoming health inequities in urban settings. WHO and UN Habitat, Geneva 2. Bjork-Klevby I (2010) Forewords. In: Hidden cities: unmasking and overcoming health inequities in urban settings. WHO and UN Habitat, Geneva, p v 3. Wilcox BA, Gubler DJ, Pizer HF (2007) Urbanization and social ecology of emerging infectious diseases. In: Mayer KM, Pizer HF (eds) The social ecology of infectious diseases. Elsevier, Boston, MA, pp 113–137 4. Neiderud CJ (2015) How urbanization affects the epidemiology of emerging infectious diseases. Infect Ecol Epidemiol 5(1):1–9 5. Banu S, Rahman MT, Uddin MK, Khatun R, Ahmed T, Rahman MM et al (2013) Epidemiology of tuberculosis in an urban slum of Dhaka City, Bangladesh. PLOS One 8: e77721 6. Penrose R, de Castro MC, Werema J, Ryan RT (2010) Informal urban settlements and cholera risk in Dar es Salaam, Tanzania. PLOS Negl Trop Dis 4:e631 7. Fauci AS, Touchette NA, Folkers GK (2005) Emerging infectious diseases: a 10–year perspective from the National Institute of Allergy and Infectious Diseases. Emerg Infect Dis 11(4):519–525 8. World Health Organization (2004) The world health report 2004—changing history. WHO, Geneva 9. Birmingham M, Stein C (2003) Global burden of disease: part A. The burden of vaccine-preventable disease. In: The vaccine book. Elsevier Science, US, pp 1–21 10. United Nations (2015) World urbanization prospects: the 2014 revision. United Nations, New York 11. Gubler DJ (1998) Dengue and dengue haemorrhagic fever. Clin Microbiol Rev 11(3):480– 496 12. Davis K (1965) The urbanization of the human population. Sci Am 213:26–27 13. Lampard E (1966) Historical aspects of urbanization. In: Hauser P, Schmore L (eds) The study of urbanization. Wiley, London, pp 519–554 14. Meadows P, Mizruchi E (1969) Urbanism, urbanization and change: comparative perspective. Addison–Wesley Pub, California 15. Hatt P, Reiss A (1961) Cities and society. The Free Press, New York 16. Hussain M, Imitiyaz I (2018) Urbanization concepts, dimensions and factors. Int J Recent Sci Res 9(1):23514–23523 17. United Nations (2018) 2018 Revision of world urbanization prospects. UN Department of Economics and Social Affairs, New York 18. De Silva NR, Brooker S, Hotez PJ, Montressor A, Engels D, Savioli L (2003) Soil-transmitted helminth infections: updating the global picture. Trends Parasitoal 19:547–551 19. Smith KR (1990) The risk transition. Int Environ Aff 2:227–251 20. Smith KR, Akbar S (2003) Health–damaging air pollution: a matter of scale. In: McGranathan G, Murray F (eds) Health and air pollution in rapidly developing countries. Earthscan, London, p 1 21. Holden JP, Smith KR (2000) Energy, the environment, and health. In: Baker J, Ba-N’Daw S, Khatib H, Popescu A (eds) World energy assessment. UNDP, New York 22. Smith KR, Ezzati M (2005) How environmental health risks change with development: the epidemiologic and environmental risks transitions revisited. Annu Rev Environ Resour 30:291–333 23. Chan M (2010) Forewords. In: Hidden cities: unmasking and overcoming health inequities in urban settings. WHO and UN Habitat, Geneva, p iv 24. Lawoyin TO, Ogunbodede NA, Olumide EA, Onadeko MO (1999) Outbreak of cholera in Ibadan, Nigeria. Eur J Epidemiol 15:367–370

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25. Osei FB, Duker AA (2008) Spatial and demographic patterns of cholera in Ashanti region-Ghana. Int J Health Geor 7:44 26. Hayward AC, Darton T, Van-Tam JN, Watson JM, Coker R, Schroebbel V (2003) Epidemiology and control of tuberculosis in Western European cities. Int J Tuberc Lung Dis 7:751–757 27. Lienhardt C (2001) From exposure to diseases: the role of environmental factors in susceptibility to and development of tuberculosis. Epidemiol Rev 23:288–301 28. Burzynski J, Schlunger NW (2008) The epidemiology of tuberculosis in the United States. Semin Respir Crit Care Med 29:492–498 29. Anugwom EE (2020) Health promotion and its challenges to public health delivery system in Africa. In: Anugwom EE, Awofeso A (eds) Public health in developing countries— challenges and opportunities. Intech Open, London 30. UN Habitat (nd) The implementation of the principles of planned urbanization: a UN-Habitat approach to sustainable urban development. Working Paper. UN Habitat, New York 31. World Tourism Organization (2014) UNWTO tourism highlights, 2014. WTO, Madrid 32. Li W, Shi Z, Yu M, Ren W, Smith C, Epstein J et al (2005) Bats are natural reservoirs of SAR-like coronaviruses. Science 310:676–679 33. McGranahan G, Satterthwaite D (2014) Urbanization concepts and trends. Working Paper, International Institute for Environment and Development, IIED, London 34. Mirski T, Bartoszcze M, Bielawska-Drozd A (2011) Globalization and infectious diseases. Przegl Epidemiol 65(4):649–655 35. Gushulak BD, MacPherson DW (2004) Globalization of infectious diseases: the impact of migration. Clin Infect Dis 38(12):1742–1748 36. Wilder-Smith A (2012) Dengue fever infections in travellers. Paediatr Int Child Health 32:28– 32 37. Chroboczok T, Boisset S, Rasigade JP, Meugnier H, Akpaka PE et al (2013) Major West Indies MRSA clones in human beings: do they travel with their hosts? J Travel Med 20:283– 288 38. Kennedy K, Collington P (2010) Colonisation with Escherichia coli resistant to “critically important” antibiotics: a high risk for international travellers. Eur J Clin Microbiol Infect Dis 29:1501–1506 39. Lee VJ, Ho M, Kai CW, Aguilera X, Heymann D et al (2020) Epidemic preparedness in urban settings: new opportunities. Lancet Infect Dis 20(5):527–529 40. Ahn I, Heo S, Ji S et al (2018) Investigation of nonlinear epidemiological models for analysing and controlling the MERS outbreak in Korea. J Theor Biol 437:17–28 41. Tang L, Bie B, Park SE, Zhi D (2018) Social media and outbreaks of emerging infectious diseases: a systematic review of literature. Am J Infect Control 46:962–972

Edlyne Augwom (Ph.D.) is a Professor of Sociology and African Development currently with the Department of Sociology and Anthropology at the University of Nigeria. His research interests include political sociology of African development, labor and industrial sociology, social dimensions of public health, and climate change in Africa. Edlyne is also the current Secretary-General of the Pan African Anthropologists Association (PAAA). Apart from being a reviewer for several reputable journals, he is also the current Editor of the journal “African Anthropologist” published by CODESRIA and recently edited a volume on public health afflictions and challenges in the developing world. He has held fellowship/teaching positions in Leiden, Edinburgh, Birmingham, Wassenaar, Mainz, and Bridgewater, the U.S.A, among others.

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E. E. Anugwom and K. N. Anugwom Kenechukwu N. Augwom (Ph.D.) is a senior lecturer and social gerontologist with the Department of Social Work, Faculty of the Social Sciences, University of Nigeria, Nsukka. Her research has focused on retirement, community development, women’s access to HIV/AIDS prevention and treatment services, reproductive health of older women, genocide and trauma, widowhood practices, and institutionalization of the elderly in Africa. In addition to academic research, teaching, and supervision, she is an active member of the Youth Reproductive Health Research Group (YoRHReG) and Public Health and Environmental Sustainability Research Group (PHES) concerned with climate change and the environmentally friendly practices.

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Pandemics and Mass Casualties: Cornerstones of Management Federico Coccolini, Enrico Cicuttin, Dario Tartaglia, Camilla Cremonini, and Massimo Chiarugi

Thinkest thou then that the world was made for thee? It is time thou knewest that in my designs, operations, and decrees, I never gave a thought to the happiness or unhappiness of man. If I cause you to suffer, I am unaware of the fact; nor do I perceive that I can in any way give you pleasure. What I do is in no sense done for your enjoyment or benefit, as you seem to think. Giacomo Leopardi1

Summary

In these uncertain times, everyday normality has experienced a drastic change by the rise of an actual pandemic and increasing awareness of the underestimated yet announced risk of periodic diffusion of new infectious diseases. Even considering the enormous amount of technical and scientific improvements conquered during the last decades, medicine itself fronts the complex task to be,

1

Translated by Charles Edwardes.

F. Coccolini (&)
E. Cicuttin
D. Tartaglia
C. Cremonini
M. Chiarugi General, Emergency and Trauma Surgery Department, Pisa University Hospital, Via Paradisa 1, 56100 Pisa, Italy e-mail: [email protected]; [email protected] D. Tartaglia e-mail: [email protected] M. Chiarugi e-mail: [email protected] F. Coccolini
E. Cicuttin
D. Tartaglia
C. Cremonini
M. Chiarugi Integrated Science Association (ISA), Universal Scientific Education and Research Network (USERN), Pisa, Italy © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_14

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at the same time, practical, accessible, cost-effective and tailored to different needs. Nevertheless, how can a system be at the same time complex and flexible? Is it possible to reach a 360-degree preparedness? Is elasticity compatible with affordable public spending? Mass casualty is, by its meaning, something that can drastically stress the ability of a system to maintain proper responses and level of care for an undetermined period, especially if happening during a pandemic. The present chapter aims to overview the key points of management of a mass casualty event during a pandemic. Graphical Abstract/Art Performance

A healthcare professional in mass causalties (Adapted with permission from the Health and Art (HEART), Universal Scientific Education and Research Network (USERN); Painting by Vacha Patel)

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The code of this chapter is 01100001 01,100,101 01,110,101 01,110,011 01,101,100 01,110,100 01,000,011 01,110,011 01,100,001 01,101,001. Keywords

Catastrophe

1


Earth
Epidemic
Future
Infection
Management
World

Introduction

In these uncertain times, everyday normality has experienced a drastic change by the rise of the actual severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and the increasing awareness of the underestimated and yet announced risk of periodic diffusion of new infectious diseases. Climate change, economic crisis, the sustainability of industrial development, and a general sensation of instability throwed the new millennium man in a dark area, far away from the comfort zone of unlimited progress at zero cost. Even considering the enormous amount of technical and scientific improvements conquered during the last decades, medicine itself fronts the complex task to be, at the same time, effective, accessible for as many people as possible, cost-effective, and tailored to a myriad of different needs. In a well-structured society, the organizational effort and the economic expenses mirror this complexity, being the amount of public money spent for health care is an immediate evaluation parameter for welfare’s development level. However, as for the most amazing complicated watches, complexity comes along with an intrinsic fragility. Grossly, from local outpatient activity to intensive care unit beds, health care system capacity is structured based on the average medical demands of a society during a discrete amount of time. A constant, linear evaluation of potential improvements and upcoming needs pairs with the health care activity; the analysis of these data, combined with infrastructural and research investments, will modify the system to become performative and in line with the times. Understanding the system’s necessities and providing adequate solutions is strictly dependent on the resources intended to cover the expenditures. The allocation of funds should cover the bare necessities of the system and consider the inevitable risks we are all exposed to. The emergency response is supposed to take care of the calculated number of accidental events over a while, plus a small potential number of events exceeding the standard. This differential varies enormously among different hospitals, countries, and world economic macro-areas, directly expressing public expenditure in health care, population density, risk scenarios, organization, facilities availability, and skills. But how can a system be at the same time complex and flexible? Is it possible to reach a 360-degree preparedness? Is elasticity compatible with affordable public spending?

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Mass casualty is, by its own meaning, something that can drastically stress the ability of a system to maintain proper responses and level of care for an undetermined period. According to the American College of Emergency Physicians (ACEP), a mass casualty incident or a disaster refers to when “the destructive effects of natural or man-made forces overwhelm the ability of a given area or community to meet the demand for health care” [1]. Any plan, at any moment, can be endangered by uncontrolled events. Usually, after a first response that forces to focus all the attention and great part of the resources to organize and produce an adequate reaction with all the consequent discomfort for routine, the activities gradually restart, depending on how long the burden created by the mass casualties is protracted. However, during the last months, the game rules changed dramatically: the pandemic is continuously at risk of suffocating the hospitals, leading to a progressive consumption of the stored resources and putting the involved personnel under pressure never seen before. Simultaneously, a first economic recessive phase seriously harmed the possibility of a prolonged and robust response that could manage the system’s required massive empowerment. Consequentially, any additional hit could be the proverbial straw that breaks the camelback. The reality we all live in is a multi-hazard risk condition [2], and, as already established for natural hazards, it needs specific risk assessment. This multi-hazard assessment aims “to determine the probability of occurrence of different hazards either occurring at the same time or shortly following each other,” because i, “they are dependent from one another;” ii, “the same triggering event or hazard” causes them; or because iii, “merely threatening the same elements at risk without chronological coincidence” [3]. Statistically speaking, it is clear that coping with the challenges presented by the actual pandemic will not spare us the necessity to deal in the same moment with all the incidental dramatic events that could occur at any time. So, how can we reinforce the resilience of a system already in trouble? How can we reach a higher level of preparedness? How can we optimize the first answer, maintenance, and recovery while we deal with a parallel crisis?

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Awareness and Preparedness

2.1 Awareness Humanity faced a vast number of hazards during the path of evolution, and history is, in great part, the result of the response we had against the unpredictable. The ability to predict the future has always been a major concern for the human being, from the ancient general reading the auspices the night before the battle to the modern broker trying to predict the finance stock exchange’s whims. With exponentially increasing computing power, computer science helped create more models and matrices to propose complex multifactorial scenarios. The experience and the

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development of specific science branches allowed to produce more refined know-how, meant to reduce the odds of unmanageable scenarios. The chance of incoming pandemics was theorized by several studies [4–7], but this advantage has not been appropriately used to prevent the uncontrolled risk of default of the health care system in several countries. Forced to learn the lesson in the worst way, the political attention is now focused on the intrinsic lability of the environment we live in, even considering a large variety of attitudes according to different political interpretations of the events [2]. Like the mythological priestess Cassandra cursed to prophesy the truth without being believed, the scientific community claimed, during the last years, for a change in the paradigms that rule economic choices and policies. It is maybe time for significant empowerment in the efficiency of communication and international cooperation [8], bringing more attention to the big picture through a coordinated effort. Interdisciplinary research [9] could be crucial to give the power of a comprehensive interpretation of the environment, compatible with cost-effective measures.

2.2 Economical Preparedness The pandemic’s nefarious effects are damaging the world economy, bringing the financial market and the production capacity to the edge of a new, dramatic crisis [10]. As the ant of the famous fable, we should get prepared for a long winter before the hard season starts. The pandemic requires extraordinary measures because, concerning every socio-political area of the world, it can destroy the labile equilibrium of a low-income country’s market and seriously harm the recoverability of stronger economies. Considering the necessity to focus on internal problems and the limitations of global travel and commerce, international economic cooperation reduces its balancing effects, enlarging the disparity among the first world and the remaining realities. The increasing needs of the health and welfare system bring more pressure on the economic sector, leading to a serious risk of default or to a strong inability to assist the necessities of the most fragile part of the population. In a 2018 paper [11], analyzing the experience of the 2014 Ebola spread, Berry et al. point the attention to the necessity to create an international fund, implementing the world health organization (WHO) Global Outbreak Alert and Response Network with important money-providing. The study highlighted the importance of investing to obtain a prompt network of responses in the early phases of an epidemic and maintain an active guard in high-risk zones to coordinate the first actions. Considering the consequences of an outbreak, implementing the resources in low- and medium-income countries could be an economic-effective help creating a barrier to the evolution of an epidemic to a pandemic. Thus, the creation of global insurance is the key to guaranteeing a source of supplies promptly available in case of need. It also offers an efficient way to prevent or at least to better control the infection’s spread. This, of course, is valid, especially in the case of concomitant, expected mass casualty incidents, allowing the preservation of the reserves stored for extra necessities and the correct development of multiple scenarios response. Any

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measure must be sustainable; therefore, it is necessary to base every decision on shared and thorough economic models [12–14]. More attention must also be given to the economic and scientific cooperation of the low- and medium-income countries. In a globalized world, any impairment that today puts my neighbors in danger is a problem that I could face tomorrow in my garden. In other words, any lack of resources in a country at risk for epidemic development is not only a local issue but the first ring of a chain that leads to dramatic consequences. In this perspective, the cooperation must be solid and, mostly, fair: as Rourke said in her work [15] (criticizing the role of WHO Pandemic Influenza Preparedness Framework), sharing of information, knowledge, and viral samples should be balanced by a proper distribution of the research results, specifically vaccines and antiviral- obtainable only with specific, expensive facilities, otherwise the role of low- and middle-income countries will be “the canary in the coal mine” [15], a mine that will always be dangerously close to the point of collapse.

2.3 Technical and Tactical Preparedness A multi-hazard reality needs health care with different levels of an organization, comparable to the bulkhead system of a cruise ship. The first thing to obtain this multilevel preparedness is to identify the crucial resources that must be preserved at all costs and the number of facilities and funds that these resources need to be maintained. An evolved hospital can guarantee various performances, from essential to complementary. Emergency medicine, intensive care, dialysis department, laboratory medicine, emergency surgery, trauma surgery, and radiology are part of the minimum guaranteed level that should always be protected [16], being part of the fast-evolving management of life-threatening conditions with high expense and specific expertise required. A second level is formed by all the actors involved in medical and surgical oncological care, oncological screening, and management of serious chronic conditions: in this case, even considering the possible severity of the condition, time is more favorable and allows proper and adaptable programming. The third level is represented by all the outpatient clinics: surgical or medical activity related to benign diseases with scarce or no chance to evolve in life-threatening conditions. The resources needed for the maintenance of all three levels, plus a variable amount of resources meant to cover additional, unexpected risks, result in the totality of the facilities, staff, and means necessary to keep a system active, thorough, and flexible. As already said, however, the structure reflects the average need of a given population and the available economic resources dedicated to health care. Nevertheless, events like a pandemic, with the drastic and all of the sudden increase of hospital accesses, can destroy every reserve, and they can worsen or even disrupt the ability to display an organized response to non-pandemic mass casualties and to ensure standard services [17–19]. For this reason, it is fundamental to recognize what is expendable, what can be postponed and how long, and what must be preserved and protected. Level 1 provisions must be guaranteed; level 2

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provisions should be organized; level 3 provisions can be suspended without serious implications. Moreover, we should avoid considering every hospital as a micro-universe close to the external world: an organization must be at least regional, so to identify the different levels of assistance that every single structure can assure, the resources consumed by each structure and the peculiar and strategical abilities that cannot be assigned to others professionals. The whole integrated system of a different hospital, in a defined macro-area, should concur into the creation of a harmonic response to the mass casualty, and it should cooperate to maintain, at least, the minimal level of assistance for life-threatening conditions while coping with the pandemic. So, recognizing and preserving ultra-specialistic branches of the health system is mandatory if we desire to avoid strategic resource depletion [20]. Simultaneously, a strong organizational collaboration with the private system should be reached, foreseeing the chance of a cooperative strategy in case of need. Now more than ever, we should also discuss the role of the health care worker during a crisis. The occurrence of several major natural disasters during the last years created a strong response in some health care workers’ categories [21], leading to the concept of constant education and attention towards disaster medicine. The Latin quote Si vis pacem para bellum (If you desire peace, get ready for war) could find an application in everyday physician life: alongside the protection of the strategic knowhows, there must be mobilization and increase in the preparedness of every figure involved in the health care assistance [22], especially if not directly involved in the supply chain of vital performances. After creating possible scenarios, drills are a fundamental part of the training, and they help to understand possible weaknesses of the first response. According to the professionality and the possibilities of every single health care worker, a standard level of expertise in disaster management can seriously help enlarge the available human resources in case of mass casualty incidents, reaching the ability to obtain a prompt enrolling of staff for critical demands (Figs. 1 and 2). Information availability, the performativity of communication, and informatic channels are also key points to understanding the system’s stress points. In terms of technical facilities or education in the proper use of these facilities, any lack of adequate means will increase the gap between first- and second-class health care and between adequate or incoordinate responses to mass casualty incidents. Adequate telehealth programs can reduce bed occupancy and hospital access, but the sine qua non condition to reach these possibilities is an adequate infrastructural implementation. The health care system, at its apical command, must also dialogue constantly with other disciplines: integration of sciences is necessary to respect the complexity and diverseness of complementary models for interpretation of reality [9] and also to give due consideration to different disciplines that could efficiently improve the power of a model to forecast the unpredictable. Mathematics, computer science, economics, and meteorological sciences are pillars of a correct and integrated approach towards scenarios that do not develop in a simple, bimodal way. There is no chance for preparedness without understanding complexity. The recent example

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Fig. 1 Univocal response: After starting the pandemic and the MCI, the level 3 resources are unemployed. The first response is obtained using the resources needed to maintain the basic level of assistance; these resources are already coping with the pandemic. The return to normality is long and comes after a period in the “crisis care” zone

Fig. 2 Modular response. After the first organization response, the level 3 resources are used to maintain proper assistance levels and avoid the exhaustion of level 1 and 2 resources. It avoids entering the “crisis care” zone, allowing a faster return to normality

of Cyclone Anpham in Bangladesh helps understand the necessity of a joint effort to prevent significant disasters [23]. Inserting the predicted seasonal hazard in the pandemic equation allowed to build shelters enough to host evacuated people, respecting physical distancing; the creation of safe routes to the shelters before the emergency made the evacuation plan feasible and efficient. During the first phase of the pandemics, these factors were considered, avoiding forgetting about the constant danger that could rout every mono-variable response plan. However, if a

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seasonal hazard is almost perfectly predictable, a stochastic hazard (i.e., fires, volcanic eruptions, and earthquakes) is more difficult to be analyzed; a certain risk of uncertainty should always be part of the decision-making process. In particular, proposed models have to consider the negative influence that a pandemic could have on supply chains, mobilization of international help, and availability of protective equipment [24] and, at the same time, consider all the possible developments the pandemic could have, also based on the analysis of previous experiences [25]. A natural disaster could worsen any pandemic scenario; these multi-hazard models require constant revision and a multidisciplinary methodology that cannot be reached without different competencies. Evacuation plans and decision-making should be guided by hybrid models, weather forecasting, engineering evaluation, and political creation of safe medical and humanitarian corridors; the pre-existent tactics should be tailored and criticized, also considering the necessity to create a response that follows careful risk assessment and mitigation strategies [24, 26]. It is mandatory to create reliable communication channels and crisis units at the regional and state level to avoid isolation, lack of coordination, and leadership void during the response.

3

First Response to a Mass Casualty During a Pandemic

3.1 Rapidity In the case of mass casualty care, when there is a massive influx of critical patients in a restricted time, the first challenge is to change the functionality of the hospital rapidly, trying to maintain a “conventional care model” or, at least, a “contingency care model” [27]. An organized response should avoid the sudden switch to “crisis care,” coming with high residual efficiency costs [28]. A central authority must coordinate every change. From the alert to the end of the crisis, the incident command system (ICS) [20] has the responsibility to manage mobilization of staff, supplies, and administration, synthesize the reports from the first lines, obtain extra resources, communicate with media, and ensure the functioning of information technology and mobile services. The effort must be harmonized and maximized according to the system’s regional capacity to distribute the impact’s pressure. Communication among different hospitals, with apical direction, plays a fundamental role, guaranteeing a proper quantification of resources, adequate distribution of these, and creating safe routes for patients’ movement and creating new beds. Part of the resources census should also consider the so-called alternative care sites (ACS), that is, any facility with at least minimal possibility to offer beds, industrial electrical power, access for ambulances, potable water, large doors, and, possibly, oxygen [29]. In extreme conditions, these facilities could help create a net of low to medium intensity of care centers, lightening the pressure on hospitals. As a matter of fact, in the case of a concomitant pandemic, we expect to have a system whose

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capacity and the unusual required effort could already impoverish efficiency. As said above, in mass casualty incidents (MCI), programming and prevention, combined with reactivity, are the only defense against default. Considering the peculiarity and unicity of every occurrence, pre-existent protocols could shorten the reaction time; then, an interim adaptation of the protocols will ensure a more precise action. Capital importance should also be given to triage to stratify and distribute victims according to the severity in the adequate hospital or facility, avoiding overstressing the capacity and causing a single center’s collapse. Participation in the first response must be proportioned and personalized to the services a hospital can guarantee [16], with specific attention to the ethical consequences of using emergency resource allocating protocols [30].

3.2 Modularity Inside a beehive, in case of mounting necessities, increasing the population, and appearance of a new queen, the swarm can decide to proceed to a functional division called swarming, giving birth to two smaller but complete and functional communities. Similarly, the effort spent in dealing with a pandemic should create, in the same facility, two different, simultaneous, and virtual structures. It would avoid the paralysis of the whole system, while a small part of the staff directly related to the pandemic is forced to carry all the task weight. The initial requirement is to find resources helpful to maintain an active a sufficient level of performance. Regular management of the pandemic should not involve staff, spaces, and supplies reserved to guarantee the first level’s operationality. Thus, immediate reserves can be obtained primarily by the suspension of the third level services [31]; according to the severity of the necessities, all the activities of the second level could be reorganized too, following a scale of urgency criteria [27] and temporally converting the spare resources [22]. In case of extreme need, protocols allowing staff displacement from a hospital to another should be considered. These strategies should create modular unities, guarantee a specialistic performance, and play a possible guidance role. These plans can be applied if there is strong cooperation among hospitals, with a common leadership and a fluid ability to optimize the existing means: this kind of skills can be reached only basing on a meticulous and shared knowledge of the actual forces, i.e., staff and supplies involved in every phase of services production. Any different reality needs different solutions: the process must consider the single peculiarities of the hospital and structures, adapting the decision making on specific protocols, because an universal approach could be more dangerous and unproductive than combined enhancement strategies [16]. It becomes possible to react quickly, starting from this awareness, to adequately and rationally expand the structure’s capacity [32].

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3.3 Protection of Victims and Health Care Workers Suppose a “normal” mass casualty scenario requires a complex organization. In that case, a mass casualty during a pandemic can seriously menace any measure established to restrain the contagion: big groups of people, forced to share a common evacuation point or transported to a hospital that could suddenly be submerged by victims, any of which could be a viral carrier. The key concept to reduce the risk is founded on the availability of protective equipment for victims and caregivers. Without a constant reintegration of the supplies, considering the standard daily need and a surplus based on unpredictable events, a modest contagion risk reduction is not feasible. Of capital importance, the accesses to the emergency departments (Eds) should be organized on different pathways, one for mass casualty victims, the other for the remaining conditions (including infective), and waiting areas should guarantee as much distancing as possible. While considering the severity of injuries assessment a priority, virtual screening of less serious victims for infective symptoms, via questionnaire and vital parameters check [33] and record, could have a first, gross division of patients. Evacuation of victims should follow appropriate, modified criteria: safe shelters should be characterized by technical adequacy and the possibility of limiting close contact and chance of contamination among people. An ideal building is supposed to be rapidly transformed in ACS and offer separated rooms with regulated access to toilets and common spaces [29]. Likewise, admitted patients must be treated according to the protection policy against patient-to-patient and health care worker-to-patient contamination. Hospitals involved in the first response and pandemic management should conserve different pathways, from the first contact to discharge, avoiding as much as possible the sharing of spaces, facilities, and personnel. Victims should be screened as soon as possible also for infection presence. In the ongoing pandemic, for example, the nasopharyngeal swab should be part of the admittance tests; in case of impossibility to perform the test, the patient should be treated as positive until proven otherwise. All the staff must be guaranteed adequate personal protection equipment. Contamination and infection among the personnel are not just a work-related risk, but a case that could bring severe consequences and to the inability of providing for the minimum assistance level: any professional hazard and contagion is a defeat for the system, subtracting precious resources, and endangering someone’s life in the fulfillment of her/his mission.

3.4 Non-Specialized Staff Education and Training The overstress of a regional health system during a pandemic, as said, needs particular decisions and deep reengineering of the general frame with the involvement of any professional available. Several models [16, 34, 35] consider the necessity of

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reallocation of workers and professionals to assist the specialists involved in the first response avoiding inactivity and waste of human resources. Alongside the protection of ultra-specialized workers, it should be considered to involve Level 3 health care workers in the response. This model of action reminds a military concept of organization again: as in the war period, training camps are supposed to give, in a short time, enough abilities and competencies to manage the field necessities. Similarly, captained by experts of emergency and critical care, doctors and nurses, according to the level of experience and personal limitations, should be given information and skills to manage the basic standard of care necessities for pandemic and mass casualty events [22]. Being the availability of experts limited, the only way to cover the needs is to multiplicate competencies, with the creations of micro operative units under the direct control of the appointee specialist. The personnel expansion process should also involve the residents [36]: to stay in the metaphor, if a specialist is a drill sergeant, a resident of the same area could be considered a corporal. Training, both in normal times and difficult ones, is necessary for an elastic system: the only way for the whole community to be ready is the preparedness of every element.

3.5 Data, Information, and Communication Channels Any ongoing process, especially if related to critical moments, must be revised and analyzed both during its evolution and, more thoroughly, in the end, in order to guarantee quality control and correction, if needed. At present, during the social media era, the easy availability of information contrasts with the wide and uncontrolled presence of inadequate depth of analysis or insufficient data control. Adding to this contest, the epistemic uncertainty [2] pairs with a potentially unknown new disease and the bulimic need for media of constant updates, and therefore, a severe risk of poor communication arises [37]. Communication with media and external environment must be managed only by selected members of the crisis coordination group, able to give focused, evidenced- and data-based communication, avoiding at all costs to create panic and sensationalism or, even worse, to feed conflictual or over-simplified interpretations of the situation [38, 39]. Of course, we should not consider Internet mobile technology only as a risk but rather as a priceless instrument for rapidly sharing vital information, contagion tracing, data collection, and regional, national or international coordination [40, 41]. The wide distribution of technology within the population should be used properly and exploited, giving life to enhanced chances of telemedicine, tracking, and training [42]. These results should be achieved without contrasting the inalienable right to privacy, especially if third parties manage data, but also considering the necessity to rely (idealistically speaking) on one single, world distributed, handy, and flexible mobile app or website, creating an all-encompassing database, shared and able to picture real-time situation. The effort of the scientific community, even in dramatic events, should be coherent and unidirectional, trying to avoid

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fragmentation and the concomitant start of multiple similar initiatives. Virtually, a single data-collecting instrument, approved in cooperation by governments and able to give trustworthy information, should be developed, considering implementations and adaptations for mass casualty incidents. In the meanwhile, empowerment of fact-checking on social media should be guaranteed. Even in the MCI contest, data analysts, communication and psychology experts, statisticians, and informaticians are full-fledged part of the emergency team from the very moment of the first response. They can reinforce the relevance and sharpness of the scientific activity that should guide and criticize any medical act.

4

Maintenance and Adaptation

4.1 Resources Implementation Being the first response strictly time-dependent, the immediate action’s main goal is to resist the external pressure as much as possible while guaranteeing the highest level of treatment that the system can afford [20]. At the end of every single wave, the ICS should take stock of the response results based on patients’ outcomes, effort spent, and resource consumption. A precise balance allows formulating precise requests, shortening the time for reintegration, and strengthening the structure. Considering the contemporary pandemic, it is necessary to evaluate which sector implementation will provide for the most valuable benefits, seeing the whole scenario as an interconnected unicum. Of course, the implementation does not relate only to means and material availability, but also to decisional processes and protocols utilized during the first response: continuous quality control must be applied, both by technical and ethical committee [28], considering all the implications of a rationalized use of facilities. Quantification and characterization of the effort, also influencing the Level 2 and Level 3 activities, will be fundamental to creating a plan for effective de-escalation of the extra personnel involved and a reprise of normal activity.

4.2 Support for Health Care Workers Even if preventing contagion is the main aim, health care worker safety does not end at the delivery of personal protection equipment. The effort required in case of emergency response to MCIs, pandemics, or, more generally, any event that raises the hospital compartment’s pressure is often the cause of severe exhaustion of staff, both physical and psychological. Burn-out is a severe risk to the unusual, heavier tasks required in a demanding and critical environment. Inability to maintain the normal standards, the necessity to limit interventions according to the availability of resources, the prolonged working hours, and the necessity to operate outside the usual working domain [36] are all factors that concur to wear out the resilience of

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the worker. Avoiding this dramatic scenario must be a crucial concern of the ICS: shift rotation, creation of reassurance plans, continuous assistance in decision making from specialists, psychological support, and periodical evaluations are only the same of the measures that should be considered. A 360-degree continuous help and protection for staff is not only a way to elude shortages of professionals, but mostly a capital duty, being any harm to the worker an unacceptable ethical failure.

4.3 Data Analysis and Scientific Production Records and analysis are the cornerstones of improvement. In such a peculiar situation as MCI during a pandemic (even rarer than the so-called “black swan” [43]), any data should be accurately registered and the results of analysis shared in order to give any possible clue for new models creation. Any investigation should follow multidisciplinary criteria, optimizing the depth of data interpretation from different perspectives. Pre-existing virtual registers could help the collection and the confrontation with different experiences. Parallelly, an inner interim analysis is mandatory to adjust the course of MCI management; this non-stop rethinking of operating and resources should bring the organization back to the start, creating a concentric loop of progressive amelioration, evaluate the results, correct preparation measures, implement protocols, apply protocols, and again evaluate the results.

5

Conclusion

David Foster Wallace, the late American author of novels, made a seminal commencement speech at Kenyon College in 2005. At the beginning of the speech, which was transcribed and published in essay form years later, was a famous joke: “There are these two young fish swimming along and they happen to meet an older fish swimming the other way, who nods at them and says, ‘Morning, boys. How’s the water?’. And the two young fish swim on for a bit, and then eventually, one of them looks over at the other and goes, ‘What the hell is water?’” [44]. In this historical moment, we risk behaving exactly like a couple of young fish, not understanding that the environment we are in has deeply changed. We are experiencing now that the pandemic could be the water we will be forced to swim in for the upcoming years, and considering it just as an unfortunate coincidence could lead to chronic unpreparedness and underestimation of concomitant risks. This traumatic experience we are all living, showing all our past inability to get ready for the unexpected, should be the right stimulus to reach a new global conscience. Strong international cooperation is needed, both economic and scientific; partisan politics, nowadays, could have a wild rebound effect with unpredictable consequences for any side. Avoiding duty and procrastinating, right now, are blind and harmful strategies that already cost a large number of victims and enormous amounts of public money. Yes, the water for sure is darker than it used to

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be but recognizing its presence worldwide allows us to discover the rich network of connections and chances of improvement that technology and cooperation could offer. It is time for consciousness and responsibility. Core Messages

• Interpersonal, national, and international cooperation is a key factor for pandemic management. • Training must be accurately planned and regularly performed to prevent chaos in the event of mass casualties during pandemics. • Before pandemics or large casualties, systems must be prepared, precisely constructed, and regularly updated. • Modular response to mass casualties during a pandemic may give the best result.

References 1. Furin M Disaster planning. https://emedicine.medscape.com/article/765495-overview 2. Hariri-Ardebili MA (2020) Living in a multi-risk chaotic condition: pandemic, natural hazards and complex emergencies. Int J Environ Res Public Health. https://doi.org/10.3390/ ijerph17165635 3. EU Commission Risk Assessment and Mapping Guidelines for Disaster Management. Brussels 4. Trock SC, Burke SA, Cox NJ (2015) Development of framework for assessing influenza virus pandemic risk. Emerg Infect Dis 21:1372–1378. https://doi.org/10.3201/eid2108.141086 5. Fox SJ, Miller JC, Meyers LA (2017) Seasonality in risk of pandemic influenza emergence. PLoS Comput Biol 13:e1005749. https://doi.org/10.1371/journal.pcbi.1005749 6. Kreuder Johnson C, Hitchens PL, Smiley Evans T, Goldstein T, Thomas K, Clements A, Joly DO, Wolfe ND, Daszak P, Karesh WB, Mazet JK (2015) Spillover and pandemic properties of zoonotic viruses with high host plasticity. Sci Rep 5:14830. https://doi.org/10. 1038/srep14830 7. Reperant LA, Grenfell BT, Osterhaus ADME (2015) Quantifying the risk of pandemic influenza virus evolution by mutation and re-assortment. Vaccine 33:6955–6966. https://doi. org/10.1016/j.vaccine.2015.10.056 8. Heymann, (2020) The need for a coordinated international pandemic response. Bull World Health Organ 98:378–379 9. Moradian N, Ochs HD, Sedikies C, Hamblin MR, Camargo CAJ, Martinez JA, Biamonte JD, Abdollahi M, Torres PJ, Nieto JJ, Ogino S, Seymour JF, Abraham A, Cauda V, Gupta S, Ramakrishna S, Sellke FW, Sorooshian A, Wallace Hayes A, Martinez-Urbistondo M, Gupta M, Azadbakht L, Esmaillzadeh A, Kelishadi R, Esteghamati A, Emam-Djomeh Z, Majdzadeh R, Palit P, Badali H, Rao I, Saboury AA, Jagan Mohan Rao L, Ahmadieh H, Montazeri A, Fadini GP, Pauly D, Thomas S, Moosavi-Movahed AA, Aghamohammadi A, Behmanesh M, Rahimi-Movaghar V, Ghavami S, Mehran R, Uddin LQ, Von Herrath M, Mobasher B, Rezaei N (2020) The urgent need for integrated science to fight COVID-19 pandemic and beyond. J Transl Med 18:205. https://doi.org/10.1186/s12967-020-02364-2

344

F. Coccolini et al.

10. World Economic Outlook Update, June 2020. https://www.imf.org/en/Publications/WEO/ Issues/2020/06/24/WEOUpdateJune2020 11. Berry K, Allen T, Horan RD, Shogren JF, Finnoff D, Daszak P (2018) The economic case for a pandemic fund. EcoHealth 15:244–258. https://doi.org/10.1007/s10393-018-1338-1 12. Pérez Velasco R, Praditsitthikorn N, Wichmann K, Mohara A, Kotirum S, Tantivess S, Vallenas C, Harmanci H, Teerawattananon Y (2012) Systematic review of economic evaluations of preparedness strategies and interventions against influenza pandemics. PLoS One 7:e30333. https://doi.org/10.1371/journal.pone.0030333 13. Clarke L (2020) An introduction to economic studies, health emergencies, and COVID-19. J Evid Based Med 13:161–167. https://doi.org/10.1111/jebm.12395 14. Pasquini-Descomps H, Brender N, Maradan D (2017) Value for money in H1N1 influenza: a systematic review of the cost-effectiveness of pandemic interventions. Value Heal J Int Soc Pharmacoecon Outcomes Res 20:819–827. https://doi.org/10.1016/j.jval.2016.05.005 15. Rourke MF (2019) Access by design, benefits if convenient: a closer look at the pandemic influenza preparedness framework’s standard material transfer agreements. Milbank Q 97:91– 112. https://doi.org/10.1111/1468-0009.12364 16. TariVerdi M, Miller-Hooks E, Kirsch T (2018) Strategies for improved hospital response to mass casualty incidents. Disaster Med Public Health Prep 12:778–790. https://doi.org/10. 1017/dmp.2018.4 17. Boriani G, Palmisano P, Guerra F, Bertini M, Zanotto G, Lavalle C, Notarstefano P, Accogli M, Bisignani G, Forleo GB, Landolina M, D’Onofrio A, Ricci R, De Ponti R (2020) Impact of COVID-19 pandemic on the clinical activities related to arrhythmias and electrophysiology in Italy: results of a survey promoted by AIAC (Italian Association of Arrhythmology and Cardiac Pacing). Intern Emerg Med. https://doi.org/10.1007/s11739-02002487-w 18. Tzeng C-WD, Tran Cao HS, Roland CL, Teshome M, Bednarski BK, Ikoma N, Graham PH, Keung EZ, Scally CP, Katz MHG, Gershenwald JE, Lee JE, Vauthey J-N (2020) Surgical decision-making and prioritization for cancer patients at the onset of the COVID-19 pandemic: a multidisciplinary approach. Surg Oncol 34:182–185. https://doi.org/10.1016/j. suronc.2020.04.029 19. CovidSurg Coll (2020) Elective surgery cancellations due to the COVID-19 pandemic: global predictive modelling to inform surgical recovery plans. Br J Surg. https://doi.org/10.1002/bjs. 11746 20. Coccolini F, Sartelli M, Kluger Y, Pikoulis E, Karamagioli E, Moore EE, Biffl WL, Peitzman A, Hecker A, Chirica M, Damaskos D, Ordonez C, Vega F, Fraga GP, Chiarugi M, Di Saverio S, Kirkpatrick AW, Abu-Zidan F, Mefire AC, Leppaniemi A, Khokha V, Sakakushev B, Catena R, Coimbra R, Ansaloni L, Corbella D, Catena F (2020) COVID-19 the showdown for mass casualty preparedness and management: the Cassandra Syndrome. World J Emerg Surg 15:26. https://doi.org/10.1186/s13017-020-00304-5 21. D’Alonzo GEJ (2010) Making disaster medicine every physician’s second specialty. Disaster Med Public Health Prep 4:108–109 22. Cobianchi L, Dal Mas F, Peloso A, Pugliese L, Massaro M, Bagnoli C, Angelos P (2020) Planning the Full Recovery Phase: An Antifragile Perspective on Surgery after COVID-19. Ann Surg. https://doi.org/10.1097/SLA.0000000000004489 23. https://www.redcross.org/about-us/news-and-events/news/2020/cyclone-amphan-inbangladesh-preparedness-paid-off.html 24. Quigley MC, Attanayake J, King A, Prideaux F (2020) A multi-hazards earth science perspective on the COVID-19 pandemic: the potential for concurrent and cascading crises. Environ Syst Decis. https://doi.org/10.1007/s10669-020-09772-1 25. Belser JA, Maines TR, Tumpey TM (2018) Importance of 1918 virus reconstruction to current assessments of pandemic risk. Virology 524:45–55. https://doi.org/10.1016/j.virol. 2018.08.009 26. Colwell RR, Machlis GE (2019) Science during crisis. Science 364:5

14

Pandemics and Mass Casualties: Cornerstones of Management

345

27. Wurmb T, Scholtes K, Kolibay F, Schorscher N, Ertl G, Ernestus R-I, Vogel U, Franke A, Kowalzik B (2020) Hospital preparedness for mass critical care during SARS-CoV-2 pandemic. Crit Care 24:386. https://doi.org/10.1186/s13054-020-03104-0 28. Maves RC, Downar J, Dichter JR, Hick JL, Devereaux A, Geiling JA, Kissoon N, Hupert N, Niven AS, King MA, Rubinson LL, Hanfling D, Hodge JGJ, Marshall MF, Fischkoff K, Evans LE, Tonelli MR, Wax RS, Seda G, Parrish JS, Truog RD, Sprung CL, Christian MD (2020) Triage of Scarce Critical Care Resources in COVID-19 An Implementation Guide for Regional Allocation: An Expert Panel Report of the Task Force for Mass Critical Care and the American College of Chest Physicians. Chest 158:212–225. https://doi.org/10.1016/j.chest. 2020.03.063 29. Iserson KV (2020) Alternative Care Sites: An Option in Disasters. West J Emerg Med 21:484–489. https://doi.org/10.5811/westjem.2020.4.47552 30. Weisleder P, Vidaurre J (2020) Allocating Medical Resources During a Mass Casualty Emergency: Sometimes It Is OK to Wait. Pediatr Neurol 110:3–4. https://doi.org/10.1016/j. pediatrneurol.2020.05.013 31. Ran NA, Samimi SS, Zhang J, Chaiyachati KH, Mallozzi CP, Hanson CW 3rd, Howell JT 3rd, Day SC, Mollanazar NK (2020) Redeployment of dermatologists during COVID-19: Implementation of a large-scale, centralized results management infrastructure. J Am Acad Dermatol 83:974–976. https://doi.org/10.1016/j.jaad.2020.06.034 32. Paganini M, Conti A, Weinstein E, Della Corte F, Ragazzoni L (2020) Translating COVID-19 Pandemic Surge Theory to Practice in the Emergency Department: How to Expand Structure. Disaster Med Public Health Prep. https://doi.org/10.1017/dmp.2020.57 33. Zens M, Brammertz A, Herpich J, Südkamp N, Hinterseer M (2020) App-Based Tracking of Self-Reported COVID-19 Symptoms: Analysis of Questionnaire Data. J Med Internet Res 22: e21956. https://doi.org/10.2196/21956 34. Duley MGK (2005) The next pandemic: anticipating an overwhelmed health care system. Yale J Biol Med 78:355–362 35. Cobianchi L, Pugliese L, Peloso A, Dal Mas F, Angelos P (2020) To a New Normal: Surgery and COVID-19 During the Transition Phase. Ann Surg 272:e49–e51. https://doi.org/10.1097/ SLA.0000000000004083 36. Taylor BL, Montgomery HE, Rhodes A, Sprung CL (2010) Chapter 6. Protection of patients and staff during a pandemic. Recommendations and standard operating procedures for intensive care unit and hospital preparations for an influenza epidemic or mass disaster. Intensive Care Med 36 Suppl 1:S45–54. https://doi.org/10.1007/s00134-010-1764-1 37. Legido-Quigley H, Asgari N, Teo YY, Leung GM, Oshitani H, Fukuda K, Cook AR, Hsu LY, Shibuya K, Heymann D (2020) Are high-performing health systems resilient against the COVID-19 epidemic? Lancet (London, England) 395:848–850. https://doi.org/10.1016/ S0140-6736(20)30551-1 38. O’Sullivan ED, Schofield SJ (2018) Cognitive bias in clinical medicine. J R Coll Physicians Edinb 48:225–232. https://doi.org/10.4997/JRCPE.2018.306 39. Dorasamy M, Raman M, Kaliannan M (2017) Integrated community emergency management and awareness system: A knowledge management system for disaster support. Technol Forecast Soc Change. https://doi.org/10.1016/j.techfore.2017.03.017 40. McCullough PA, Eidt J, Rangaswami J, Lerma E, Tumlin J, Wheelan K, Katz N, Lepor NE, Vijay K, Soman S, Singh B, McCullough SP, McCullough HB, Palazzuoli A, Ruocco GM, Ronco C (2020) Urgent need for individual mobile phone and institutional reporting of at home, hospitalized, and intensive care unit cases of SARS-CoV-2 (COVID-19) infection. Rev Cardiovasc Med 21:1–7 41. Drew DA, Nguyen LH, Steves CJ, Menni C, Freydin M, Varsavsky T, Sudre CH, Cardoso MJ, Ourselin S, Wolf J, Spector TD, Chan AT (2020) Rapid implementation of mobile technology for real-time epidemiology of COVID-19. Science 368:1362–1367. https://doi.org/10.1126/science.abc0473

346

F. Coccolini et al.

42. Smith AC, Thomas E, Snoswell CL, Haydon H, Mehrotra A, Clemensen J, Caffery LJ (2020) Telehealth for global emergencies: implications for coronavirus disease 2019 (COVID-19). J Telemed Telecare 26:309–313. https://doi.org/10.1177/1357633X20916567 43. Taleb N (2012) Antifragile: things that gain from disorder/Nassim Nicholas Taleb. Random House, New York 44. Foster Wallace D (2009) This is water: some thoughts, delivered on a significant occasion about living a compassionate life. Kenyon College. New York, Little, Brown

Federico Coccolini M.D., is a consultant general, emergency, and trauma surgeon working at the Pisa University Hospital (Pisa, Italy), one of the biggest Level I trauma centers in Italy. He teaches Emergency and Trauma Surgery at the University of Pisa. He has deep and documented experience in managing post-traumatic and non-post-traumatic patients. His principal interests are emergency surgery, trauma surgery, advanced oncology, oncologic gastrointestinal surgery, laparoscopic and minimally invasive surgery, tissue engineering and experimental surgery, evidence-based medicine, and surgery. He is the principal investigator of multicentre studies and registers. He authored hundreds of papers and tens of book chapters. He edited several books and a book series. He served as a reviewer for several journals. He is the research editor of the World Society of Emergency Surgery (WSES) and the World Journal of Emergency Surgery (WJES), a member of the Executive Committee of the Italian Society of Geriatric Surgery and the Italian Society of Surgical Physiopathology. Dr. Coccolini is a world opinion leader in general emergency surgery. Massimo Chiarugi M.D., FACS, is a Full Professor of General Surgery and Director of the Postgraduate School of General Surgery at Pisa University (Italy). He is the Chief of the Emergency Surgery Unit & Trauma Center at Cisanello Hospital, Pisa, a busy and most renewed referring center for acute care surgery in Italy. For years, he has gained broad experience in elective, emergency, and trauma surgery. Main research fields include inflammatory response to acute conditions, general emergency surgery, laparoscopy and minimally invasive surgery in elective and acute care, gastrointestinal oncology, colorectal conditions, and trauma surgery. The same were subjects of manuscripts and book chapters. Member of the editorial board of Updates in Surgery, he serves as a reviewer for many surgical journals. Fellow of several surgical societies, he is currently the Incoming President of the Società Italiana di Chirurgia d’Urgenza e del Trauma (SICUT) and Vice-President of the American College of Surgeons, Italy Chapter.

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Social Marketing Contributions to Mitigate Global Epidemics Beatriz Casais and João F. Proença

For governments across the world, understanding human behaviour sits at the heart of developing interventions to tackle big social and health challenges. Jeff French [1]

Summary

Social marketing focuses on citizens’ change for social welfare. By adopting marketing tools and principles from multidisciplinary fields, social marketing has developed theories to attract citizens’ involvement with the purpose of social attitude and behavior change. Health is a topic to which social marketing applies, and so health outcomes provide insights for social marketing theory and managers worldwide. This chapter presents the principles of social marketing, particularly in their application to global epidemics response. It intends to be a helpful guide for public policymakers, public health strategists, and multidisciplinary professionals in society with the responsibility of implementing public health programs for epidemic response. The chapter evaluates how different multidisciplinary theories influencing behaviors may be used in a social marketing strategy targeting heterogeneous segments. The application of social marketing to public health is discussed, particularly the adaptation of social marketing to epidemic dynamics in proactive and reactive ways. In the B. Casais (&) School of Economics and Management, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal e-mail: [email protected] CICS.NOVA.UMinho, Braga, Portugal Integrated Science Association (ISA), Universal Scientific Education and Research Network (USERN), Braga, Portugal J. F. Proença Faculty of Economics, University of Porto, Porto, Portugal ADVANCE, ISEG, University of Lisbon, Lisbon, Portugal © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_15

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meantime, challenges of a consistent strategy to be applied at the global, national, or local level with various stakeholders are presented. The authors nudge the control management approach and, in the end, highlight the evaluation of the goals’ achievements. Graphical Abstract/Art Performance

Social marketing balance in public health

The code of this chapter is 01000101 01,110,000 01,101,001 01,100,100 01,100,101 01,101,101 01,101,001 01,100,011 01,110,011. Keywords










Behavioural change Epidemic prevention Health promotion Public health Social marketing

1




Introduction

Social marketing adapts marketing techniques to behavioral change [2, 3] to improve the good of society. It is an essential topic for public policy, considering the exchange paradigm and the need to engage citizens as social development actors, both by voluntary actions, regulated behavior policies, or both [4–6]. Social Marketing has been particularly applied in public health [7–9], with a relevant purpose for disease control, considering prevention benefits compared to treatments [7–13].

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Social marketing strategies should be correctly applied to achieve behavior change effectiveness [14–16]. Social marketing requires audience analysis and the correct orientation for different population segments. According to the sociologic or psychological theories, people with different habits should be treated with particular social marketing approaches that implicate the barriers or motivations for behavior change. Social marketing interventions should promote exchanges that overcome the competitive calls for undesirable behaviors, which are the competitive social marketing subjects [17, 18]. Besides the audience research and competitive approach, social marketing has to be contextually focused [19]. First, social marketers need to understand the epidemic situation and address priorities with an adapted social marketing intervention [20, 21]. One particular problem is that sometimes social marketing is developed reactively and not from a proactive perspective [19]. However, this latter approach faces constraints in the communication process because citizens fail to understand the risk that justifies behavioral changes. Further, it is crucial to harmonize upstream and downstream interventions to guarantee a general social marketing approach under the assumptions of a strategic public policy [22, 23] and community-based contextual interventions [24]. In this sense, an integrated response at all levels helps potentiate effects in social norms [25]. As both a management and marketing approach, social marketing requires strategic planning and evaluation [20] and effectiveness analysis according to the intervention’s resources and cost [7, 26]. Social marketing offers a managerial perspective to face social problems by changing citizens’ behaviors [27]. When an epidemic hits, to assure necessary citizens’ behavioral changes, social marketing requires a policy strategy, segmentation and exchange perspective, a practical communication approach, and distribution of methods for epidemic prevention. In this sense, this chapter explores the theoretical principles of social marketing to mitigate global epidemics.

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Social Marketing in Public Health

In general, social marketing encourages behavioral changes, which are of particular interest in promoting public health [7, 9, 28]. Kotler and Zaltman [29] introduced the first definition of social marketing as a tool to solve social problems [30, 31]. Some of them had been ironically caused by the influence of commercial marketing on behaviors [32]. Influencing lifestyles with social marketing tools can also lower the costs linked to illness treatment [33]. Social marketing has the advantage of being cost-efficient in reducing the incidence of illness by promoting earlier disease screenings and healthier lifestyles [11]. The social marketing definition illustrates the focus on the voluntary change of behavior by way of the marketing principle of exchange and marketing techniques to improve society [34–36]. However, the exchange principle has come to include policy regulation as an active tool for social marketing, with rewards or penalties as

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incentives or disincentives for behavior change [37]. To be considered a social marketing intervention, it needs to: • • • •

be about behavioral influence; select audience segments strategically and carefully; explain why and how behavior is to change; and present the marketing strategies to become the desired behavior a social norm [18].

Social marketing started to be developed in the 1960s with family planning interventions in India [38] to change lifestyles and behaviors [7, 11, 33, 39–42]. Social marketing has been an important tool to reduce smoking habits, infection transmission [43], and drug and alcohol consumption [44]. It has promoted sports practice [39, 45], tuberculosis treatment adherence [40], compliance with clinical guidelines [41], organ donation [46], and controlling the transmission of infections like leprosies [47] and HIV [34, 48]. Social marketing could be an important tool to fight COVID-19, considering the benefits of citizens’ behavioral change to mitigate epidemics and the barriers and efforts required to follow social distance, to wear a mask, and wash hands, particularly where there are no proper conditions for that [49].

3

Multidisciplinary Theories and Approaches Used in Social Marketing

The exchange theory is the central theoretical assumption of the marketing field, explaining that the individual is influenced by the exchange of the benefit (tangible/intangible) that he/she receives and the cost paid for that benefit [50]. Besides the price of the social products required for a specific desired behavior, such as the use of a condom, a sterile needle, or a facial mask, for example, another high cost that deserves to be recognized is the cost of change of habits and the constraints and discomfort connected to that desired behavior. Examples are the discomfort of wearing a mask when making a speech to an audience, the difficulty of keeping a distance from friends and family during the COVID pandemic, or the discomfort of wearing a condom in sexual intercourse to prevent sexually transmitted infections such as HIV/AIDS. The theory of planned behavior and the connected theory of reasoned action represent the scheme of how behavioral intention is organized [50]. The behavioral choice depends on the target’s own positive attitude towards the desired behavior and how others view such action according to social norms. Further, another item is the perceived behavioral control, such as the skills necessary to conduct the intended behavior, the opportunities, and the perceived importance of achieving the goals, which considers the assumptions of goal-setting theory. Another relevant and

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connected notion is the social norms theory, regarding what society considers to be the expected behavior [51]. This influences behavioral intention. Social marketing effectiveness may also change social norms since the acceptance of a specific action in society due to social marketing interventions may also change the desired attitudes and behaviors in culture [53]. An exciting example is face masks in Western countries after COVID-19 and the physical distance under the new normality. The social marketing interventions have had rapid success in influencing what the general public now views as normal behavior, such as repudiating the absence of masks or failure to maintain physical distance, now taken unconsciously as standard, all within a few months. The health belief model regards the topics creating incentives for behavioral change in the field of public health [52]. Those factors are the perceived susceptibility, severity, benefits, and barriers for behavioral change connected to the exchange theory, the perceived efficacy, and cues to action. That is why it is crucial to work on the perception of a particular epidemic in an at-risk population, for example, the vulnerable people in concentrated HIV epidemics or the places with the higher transmission of SARS-CoV-2. Some countries applied a color map to show the most at-risk districts to incentivize preventive behaviors among people with higher perceived susceptibility to infection. In the case of COVID, this model is critical considering the different severity outcomes the disease provokes among various age levels, making younger people less preventive regarding epidemics, with severe transmission to society. The loss of perceived severity also occurred with ART’s appearance in HIV therapy, with AIDS becoming a chronic disease instead of a death sentence. The transtheoretical model of stages of change considers that change does not occur immediately. The target audience will have to evolve over a set of five sequential steps: pre-contemplation, contemplation, preparation, action, and confirmation [53]. In this sense, public health policies should consider these different communication messages to positively affect behavioral change adoption. Citizens start to be aware of a particular problem and to think that it will require change, processing the idea. Abrupt changes, for example, using regulation, may result in citizens’ incomprehension and lack of behavioral change, considering the barriers for that change. The social cognitive theory explains that behavioral change should consider some influences, such as closed environment (family, closest friends, local community), social context (culture, socioeconomic conditions, norms, structural aspects), and personal characteristics (goals, aspirations, self-efficacy, education, and skills). Other theories, like social network theory, the theory of group relations, and the prototype-willingness model, consider the importance of group leaders in the community to influence behavioral change. It justifies celebrities and digital influencers’ impact on social marketing to promote public health [50, 54]. Finally, cognitive dissonance theory explains how people face maintaining undesired behaviors, looking for a cognitive explanation for their choice. This cognitive dissonance also occurs when the desired behavior’s perceived efficacy is not absolute and the perceived self-efficacy to change is low. In this sense, it is crucial

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to communicate the effectiveness of behavioral change in disease prevention. Here, an example is the use of facial masks to prevent coronavirus transmission and the self-efficacy to change, even after having tried and failed, as it is with obesity, where people diet to lose weight and gain the weight back after a few months. In this last example, the long-term lifestyle change is the best argument to communicate and achieve change and get positive results. These theories are linked together and help understand a specific target group’s dynamics and behavioral attitudes. Before a social marketing intervention is applied, it is necessary to make a precise analysis and understand which theories may help achieve the desired behavioral change [50]. There are some essential basic questions to ask: • Where is the target group located in relation to the theory of stages of change? • What factors influence the current behavior, according to the social cognitive theory? • How can the target group adopt the desired behavior, according to the exchange theory?

4

Social Marketing Mix and Forms of Exchange

The success of a marketing strategy lies in the identification of how to segment the market correctly. Segmentation is a tool that allows us to divide a population into homogeneous groups that have one or more characteristics in common, making them prone to respond to similar strategies. After evaluating the segments, it is essential to look at the following criteria: viability (see if the segment has the potential to impact the behavior to be changed); accessibility (if communication and distribution channels are available to conduct the desired action); and the responsiveness (if there is a capacity to serve the target audience). Social marketers should look at exchange forms and design an intervention based on active or passive exchange forms, positively or negatively. This includes the hug intervention, based on rewards; the smack intervention, based on penalties; the shove, considering unconscious barriers to the undesired behavior; or the nudge based on unconscious conditions facilitating the desired actions, for example, by the opting-out strategy of choosing behaviors [37]. The marketing mix or 4Ps is the model used by marketers to plan their decisions to satisfy customers’ needs better than their competitors. Kotler and Zaltman’s [55] definition of social marketing references the Marketing Mix’s critical points. Three levels must be considered when making a product: i. the main product is the benefit that consumers will obtain by adopting a specific behavior change; ii. the real product is that people use or purchase and is related to the behavior that we want to change; and

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iii. the augmented product refers to any additional elements associated with the main product that would motivate behavioral change. The price refers to the cost that the target audience associates with the adoption of the desired behavior. This cost can be of two types: monetary cost (purchase of goods and services) and non-monetary cost (time, effort, energy, psychological risk, physical discomfort). The distribution tool shows the place where the audience performs the desired behavior. In this sense, decisions related to distribution determine the success of a social marketing strategy. For example, to mitigate obesity, a particular government develops a communication advertisement to promote a healthy diet. However, if vending machines and most restaurants do not serve healthy meals with vegetables or fresh fruit, behavioral change may not occur due to the lack of opportunity. Many countries have achieved important results in citizens’ physical activity not by advertising and communication calls but by constructing outdoor facilities for physical activity, such as cycling paths, walking paths, or public gardens with exercise equipment. When we talk about promotion, we refer to marketing communication: advertising on mass and digital media, public relations, such as the organization of events, product placement of the desired behavior in movies, series or videoclips, or sales promotion used commonly for social marketing rewards. Social marketing includes several tools to promote attitude and behavioral change [56] to achieve social good [57]. Although it goes beyond the simple framework of the marketing mix applied to social issues, including capability, opportunity, and motivational conditions [58], communication is a critical tool of social marketing to promote information sharing and emotional appeals for social change [58]. One important perspective of social marketing is the need to act in a proactive way to prevent social problems instead of conducting a reactive response [19]. However, a proactive response may result in low perception of risk and misconceptions regarding the social problem’s severity. An example regarding the COVID-19 epidemics is that some countries entered into lockdown as a reactive response to health services’ incapacity. However, other countries entered into lockdown before that situation presented itself. In the latter cases, the population wondered whether it had been a good decision, showing a lesser threat perception. In sum, the proactive response in social marketing has to be accompanied by a suitable communication policy, where the risk and severity are well communicated to justify the proactive response.

5

Social Marketing Appeals

Several messages encourage unhealthy behaviors [45]. Alternative and innovative strategies, such as branding social marketing campaigns, are fundamental to persuade individuals to adopt recommended behaviors [59, 60] in a long-term perspective [59].

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Social marketing messages may be presented through positive or negative appeals, or either through informational or emotional appeals [61, 62]. Emotional appeals are more popular, in commercial [62, 63], political [64], and social marketing [28, 59, 61, 65–67]. Their effectiveness is higher in comparison with informational-type appeals [60, 68, 69]. However, there is not unanimous opinion about the most effective social marketing strategy [60, 61, 66, 70–74]. Effectiveness in social marketing is a research challenge [9]. Prospect theory explains rensponses to social messages vary depending on the way they are perceived [75, 76]. In commercial marketing [63, 77], messages tend to have an emotional tonality [28, 59, 65, 78–80] because of its effectiveness [60, 68, 69, 72, 81]. In social marketing there are different visions about the effectiveness of positive or negative appeals [60, 70–72, 74, 78, 79, 82]. Protection motivation theory (PMT) defends negative appeals [83] through fear and threat messages [68, 71, 72, 78, 79, 84–90]. However, positive appeals may be considered an efficient alternative for long-term purposes [60, 79, 91], including in public health [60, 92].

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Social Marketing Influencers and Digital Media

Digital media should receive higher use for social marketing purposes in a segmented approach to better communicate with citizens. Given the more extensive use of digital media, such as social networks, blogs, and mobile devices, social marketing must adopt a digital marketing approach [93]. While celebrity endorsers used to be important stakeholders to promote social change [94], other role models have also to be engaged [54]. Digital influencers from blogs and Instagram should be increasingly recruited for this important task of promoting public health since they maintain trust and fit in well with the endorsed cause, creating engagement in line with what is happening in digital endorsements to commercial products [95].

7

Upstream, Mid-Stream, and Downstream Social Marketing

Most social problems cannot be solved when the action is limited to the individual level. In other words, the problem has to enter into the government’s agenda. In this way, social marketers need to relate directly to other audiences (organized groups) that can put ostensive pressure on the government to change some decisions. Social marketing has to be focused on acting downstream, but also on upstream levels. In this way, the social marketing concept includes tools to influence social well-being. Social marketing may be organized in downstream, midstream, and upstream levels, requiring stakeholders to collaborate to provide those interventions [25]. A downstream approach is a direct approach, targeting individuals involved in

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behaviors to improve. The upstream approach is aimed at decision-makers. The individual’s role remains but has been complemented by the more enveloping conditions of society. The midstream approach involves those who can influence, including family members, neighbors, co-workers, and friends.

8

Social Marketing Control

As in any management discipline, the monitoring and evaluation activity in social marketing must be conducted to certify that the objectives are achieved [7]. Sometimes, public interventions lack measurable goals, considering that health should not be counted in a measured way. On the contrary, the social marketing approach considers that the health gains have to be measured, and the objectives have to point to health gain goals to justify the investments [96]. The return on investment in health is sometimes difficult to calculate. According to health economics principles, it has to be followed as an important step of health policy decision-making. In this sense, for each health goal, a key performance indicator has to be identified. With surveys, focus groups, observation or interviews, and health statistics outputs, the social marketer may measure whether their goals have been achieved with the social marketing actions conducted in the planned intervention.

9

Conclusion

The chapter explains the main principles and theories used in the social marketing approach. It highlights the importance of this field of study to public health, particularly to mitigate global epidemics. First of all, the chapter shows how multidisciplinary theories from sociology, psychology, and marketing may predict the perceived self-efficacy to change and the self-efficacy of a particular attitude or behavior to prevent diseases. By understanding the antecedent variables and moderators of behavioral change, an audience segmentation approach would enable social marketers to design intervention by enhancing social gains and benefits and facilitating to overcome changing barriers, such as by the action of nudge strategies, rewards strategies, or the application of penalties or restrictions of access to undesired behaviors. In this sense, it is crucial to assess the health benefit proposed compared with the cost of conducting the desired behavior, including the change cost. Also, specific barriers to achieving the desired behavior need to be recognized to examine actions required and expand the opportunities to adopt the desired behavior, particularly in the distribution dimension and in the nudge. Social marketing interventions should integrate policies at global, national, and local levels. However, social marketing implicates more than communication actions, social advertising appeals have to be strategically designed, and digital influencers have to be involved for social endorsement cues.

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Core Messages

• Social marketing considers the exchange of a benefit and a change cost. • The perceived risk, disease severity, efficacy, and self-efficacy are drivers of behavioral change. • The behavior change may be triggered by barriers to the undesired action and regulations with penalties or rewards. • Promoting social behaviors that are social norms allows unconscious social change through nudge techniques. • Positive and negative appeals should be used according to the segment, the severity of the disease, and risk perception.

Acknowledgements João F. Proença gratefully acknowledges financial support from FCT – Fundação para a Ciência e Tecnologia (Portugal), national funding through research grant UIDB/04521/2020. His work is financed by national funds through FCT – Foundation for Science and Technology, I.P., within the scope of the project «UIDB/04647/2020» of CICS.NOVA – Interdisciplinary Centre of Social Sciences of Universidade Nova de Lisboa.

References 1. French J, Blair-Stevens C, McVey D, Merritt R (2010) Social marketing and public health theory and practice. Oxford University Press, New York 2. Dann S (2010) Redefining social marketing with contemporary commercial marketing definitions. J Bus Res 63(2):147–153 3. Truong VD (2014) Social marketing: a systematic review of research 1998–2012. Soc Mark Q 20(1):15–34 4. Hastings G (2003) Relational paradigms in social marketing. J Macromark 23(1):6–15 5. Shultz CJ, Deshpandé R, Cornwell TB, Ekici A, Kothandaraman P, Peterson M, Shapiro S, Talukdar D, Veeck A (2012) Marketing and public policy: transformative research in developing markets. J Public Policy Mark 31(2):178–184 6. Hill RP, Martin KD (2014) Broadening the paradigm of marketing as exchange: a public policy and marketing perspective. J Public Policy Mark 33(1):17–33 7. Stead M, Gordon R, Angus K, McDermott L (2007) A systematic review of social marketing effectiveness. Health Educ 107(2):126–191 8. Grier S, Bryant CA (2005) Social marketing in public health. Annu Rev Public Health 26 (1):319–339 9. Evans WD, McCormack L (2008) Applying social marketing in health care: communicating evidence to change consumer behavior. Med Decis Making 28(5):781–792 10. Gordon R, McDermmot L, Stead M, Angus K (2006) The effectiveness of social marketing interventions for health improvement: what’s the evidence? Public Health 120(12):1113–1139 11. Walsh DC, Rud RE, Moeykens BA, Moloney TW (1993) Social marketing for public health. Health Aff 12(2):104–119 12. Evans WD (2006) How social marketing works in health care. BMJ 322(7551):1207–1210 13. Crawshaw P (2013) Public health policy and the behavioural turn: the case of social marketing. Crit Soc Policy 33(4):616–637 14. Albrecht TL (1996) Advances in segmentation modeling for health communication and social marketing campaigns. J Health Commun Int Perspect 1(1):65–80

15

Social Marketing Contributions to Mitigate Global Epidemics

357

15. Bloom PN, Novelli WD (1981) Problems and challenge in social marketing. J Mark 45(2): 79–88 16. Fine SH (1980) Toward a theory of segmentation by objectives in social marketing. J Consum Res 7(1):1–13 17. French J, Russell-Bennett R (2015) A hierarchical model of social marketing. J Soc Mark 5 (2):139–159 18. Wettstein D, Suggs LS (2016) Is it social marketing? The benchmarks meet the social marketing indicator. J Soc Mark 6(1):2–17 19. Casais B, Proença JF (2018) Social advertisements for public health and epidemic dynamics: a study based on hiv/aids prevention television advertisements in four european countries. J Soc Mark 8(4):397–420 20. Wymer W (2011) Developing more effective social marketing strategies. J Soc Mark 1(1): 17–31 21. Silva SC, Silva MF (2012) Failure is a stepping stone for success. Int Rev Public Nonprofit Market 9(2):153–179 22. Key TM, Czaplewski AJ (2017) Upstream social marketing strategy: An integrated marketing communications approach. Bus Horiz 60(3):325–333 23. Gordon R (2013) Unlocking the potential of upstream social marketing. Eur J Mark 47 (9):1525–1547 24. Lynes J, Whitney S, Murray D (2014) Developing benchmark criteria for assessing community-based social marketing programs. A look into Jack Johnson’s “All at Once” campaign. J Soc Market 4 (2):111–132 25. Almestahiri R, Rundle-Thiele S, Parkinson J, Arli D (2017) The use of the major components of social marketing: a systematic review of tobacco cessation programs. Soc Mark Q 23 (3):232–248 26. Whitelaw S, Smart E, Kopela J, Gibson T, King V (2011) Developing social marketing capacity to address health issues. Health Educ 111(4):319–331 27. Levit T, Cismaru M (2020) Marketing social marketing theory to practitioners. Int Rev Public Nonprofit Market 17:237–252 28. Keller PA, Lehmann DR (2008) Designing effective health communications: a meta-analysis. J Public Policy Mark 27(2):117–130 29. Kotler P, Zaltman G (1971) Social marketing: an approach to planned social change. J Market 35:3–12 30. Kotler P, Levy SJ (1969) Broadening the concept of marketing. J Mark 33(1):10–15 31. Wiebe GD (1951) Merchandising commodities and citizenship on television. Public Opin Q 15:679–691 32. Hastings G, Saren M (2003) The critical contribution of social marketing theory and application. Mark Theory 3(3):305–322 33. Rothschild ML (1999) Carrots, sticks, and promises: a conceptual framework for the management of public health and social issue behaviors. J Market 63:24–37 34. Andreasen AR (2003) The life trajectory of social marketing some implications. Mark Theory 3(3):293–303 35. Andreasen AR (2002) Marketing social marketing in social change marketplace. J Public Policy Mark 21(1):3–12 36. Quinn GP, Ellery J, Thomas KB, Marshall R (2010) Developing a common language for using social marketing: an analysis of public health literature. Health Mark Q 27(4):334–353 37. French J (2011) Why nudging is not enough. J Soc Mark 1(2):154–162 38. Dholakia RR (1984) A macromarketing perspective on social marketing: the case of family planning in India. J Macromark 4(1):53–61 39. Gordon R, McDermmot L, Stead M, Angus K (2006) The effectiveness of social marketing internventions for health improvement: what’s the evidence? Public Health 120(12): 1113–1139 40. Grier S, Bryant CA (2005) Social marketing in public health. Annu Rev Public Health 26:319–339

358

B. Casais and J. F. Proença

41. Morris ZS, Clarkson PJ (2009) Does social marketing provide a framework for changing healthcare practice? Health Policy 91(2):135–141 42. Evans WD (2006) How social marketing works in health care. BMJ 322:1207–1210 43. Mah MW, Deshpande S, Rothschild ML (2006) Social marketing: a behavior change technology for infection control. Am J Infect Control 34(7):452–457 44. Jones SC, Rossiter JR (2002) The applicability of commercial advertising theory to social marketing: two case studies of current Australian social marketing campaigns. Soc Mark Q 3(1):6–18 45. Wymer W (2010) Rethinking the boundaries of social marketing. activism or advertising? J Bus Res 63:99–103 46. Harrison TR, Morgan SE, Chewning LV (2008) The challenges of social marketing of organ donation: news and entertainment coverage of donation and transplantation. Health Mark Q 25(1/2):33–65 47. Wong ML (2002) Can social marketing be applied to leprosy programmes? Lepr Rev 73: 308–318 48. Chance Z, Deshpandré R (2009) Putting patients first. J Macromark 29(3):220–232 49. Lee NR (2020) Reducing the spread of COVID-19: a social marketing perspective. Soc Market Quart 50. Luca NR, Suggs LS (2013) Theory and model use in social marketing health interventions. J Health Commun 18(1):20–40 51. Burchell K, Rettie R, Patel K (2013) Marketing social norms: Social marketing and the ‘social norm approach.’ J Consum Behav 12(1):1–9 52. Rosenstock IM (1974) The health belief model and preventive health behavior. Health Educ Behav 2(4):354–386 53. Prochaska JO, DiClemente CC, Norcross JC (1992) In search of how people change. Am Psychol 47:1102–1114 54. Brace-Govan J (2013) More diversity than celebrity: a typology of rolemodel interaction. J Soc Mark 3(2):111–126 55. Kotler P, Zaltman G (1971) Social marketing: an approach to planned social change. J Mark 35(3):3–12 56. Spotswood F, French J, Tapp A, Stead M (2012) Some reasonable but uncomfortable questions about social marketing. J Soc Mark 2(3):163–175 57. Carvalho HC, Mazzon JA (2015) A better life is possible: the ultimate purpose of social marketing. J Soc Mark 5(2):169–186 58. Wood M (2008) Applying commercial marketing theory to social marketing: a tale of 4Ps (and a B). Soc Mark Q 14(1):76–85 59. Basu A, Wang J (2009) The role of branding in public health campaigns. J Commun Manag 13(1):77–91 60. Lewis IM, Watson B, White KM, Tay R (2007) Promoting public health messages: should we move beyond fear-evoking appeals in road safety? Qual Health Res 17(1):61–74 61. Brennan L, Binney W (2010) Fear, guilt, and shame appeals in social marketing. J Bus Res 63:140–146 62. McKay-Nesbitt J, Manchanda RV, Smith MC, Huhmann BA (2011) Effects of age, need for cognition, and affective intensity on advertising effectiveness. J Bus Res 64:12–17 63. Bagozzi RP, Gopinath M, Nyer PU (1999) The role of emotions in marketing. J Acad Mark Sci 27(2):184–206 64. Brader T (2005) Striking a responsive chord: how political ads motivate and persuade voters by appealing to emotions. Am J Polit Sci 49(2):388–405 65. Lang A, Yegiyan NS (2008) Understanding the interactive effects of emotional appeal and claim strength in health messages. J Bradcast Electron Media 52(3):432–447 66. Block LG, Keller PA (1995) When to accentuate the negative: the effects of perceived efficacy and message framing on intentions to perform a health-related behavior. J Mark Res 32: 192–203

15

Social Marketing Contributions to Mitigate Global Epidemics

359

67. Keller PA (2002) Depressive realism and health risk accuracy: the negative consequences of positive mood. J Consum Res 29:57–69 68. Vincent A-M, Dubinsky AJ (2005) Impact of fear appeals in a cross-cultural context. J Euromarket 14(1):145–167 69. Flora JA, Maibach E (1990) Cognitive responses to AIDS information: the effects of issue involvement and message appeal. Commun Res 17(6):759–774 70. Reeves BR, Newhagen J, Maibach E, Basil M, Kurz K (1991) Negative and positive television messages: effects of message type and context on attention and memory. Am Behav Sci 34(6):679–694 71. Dillard JP, Anderson JW (2004) The role of fear in persuasion. Psychol Mark 21(11):909–926 72. Hastings G, Stead M, Webb J (2004) Fear appeals in social marketing: Strategic and ethical reasons for concern. Psychol Mark 21(11):961–986 73. Gardner MP, Wilheim JFO (1987) Consumer responses to ads with positive vs. negative appeals: some mediating effects of context induced mood and congruency between context and ad. Curr Issues Res Advert 10(1):81–98 74. Lewis IM, Watson BC, White KM (2009) Response efficacy: the key to minimizing rejection and maximizing acceptance of emotion-based anti-speeding messages. Accid Anal Prev 42 (2):459–467 75. Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47(2):263–292 76. Tversky A, Kahneman D (1981) The framing of decisions and the psychology of choice. Science 211(4481):453–458 77. McKay-Nesbitt J, Manchanda RV, Smith MC, Huhmann BA (2011) Effects of age, need for cognition, and affective intensity on advertising effectiveness. J Bus Res 64(1):12–17 78. Block LG, Keller PA (1995) When to accentuate the negative: the effects of perceived efficacy and message framing on intentions to perform a health-related behavior. J Mark Res 32 (2):192–203 79. Brennan L, Binney W (2010) Fear, guilt, and shame appeals in social marketing. J Bus Res 63 (2):140–146 80. Keller PA (2002) Depressive realism and health risk accuracy: the negative consequences of positive mood. J Consum Res 29(1):57–69 81. Helmig B, Thaler J (2010) On the effectiveness of social marketing—what do we really know? J Nonprofit Public Sect Mark 22(4):264–287 82. Gardner MP, Wilheim JFO (1987) Consumer responses to ads with positive vs. negative appeals: some mediating effects of context induced mood and congruency between context and ad. J Curr Issues Res Advert 10(1):81–98 83. Rogers RW (1983) Cognitive and psychological processes in fear appeals and attitude change: a revised theory of protection motivation. In: Cacioppo J, Petty R (eds) Social psychophysiology. Guilford Press, New York 84. Gallopel-Morvan K, Gabriel P, Gall-Ely ML, Rieunier S, Urien B (2009) The use of visual warnings in social marketing: the case of tobacco. J Bus Res 64(1):7–11 85. Cauberghe V, Pelsmacker PD, Janssens W, Dens N (2009) Fear, threat and efficacy in threat appeals: message involvement as a key mediator to mesasge acceptance. Accid Anal Prev 41 (2):276–285 86. Cismaru M, Lavack AM, Markewich E (2009) Social marketing campaigns aimed at preventing drunk driving. Int Mark Rev 26(3):292–311 87. Cismaru M, Lavack AM, Hadjistavropoulos H, Dorsch KD (2008) Understanding health behavior: an integrated model for social marketers. Soc Mark Q 14(2):2–32 88. Cismaru M, Lavack AM (2007) Interaction effects and combinatorial rules governing protection motivation theory variables: a new model. Mark Theory 7(3):249–270 89. McKinley CJ (2009) Investigating the influence of threat appraisals and social support on healthy eating behavior and drive for thiness. Health Commun 24(8):735–745 90. Charry KM, Demoulin NTM (2012) Behavioural evidence for the effectiveness of threat appeals in the promotion of healthy food to children. Int J Advert 31(4):773–794

360

B. Casais and J. F. Proença

91. O’Keefe DJ, Jensen JD (2008) Do loss-framed persuasive messages engender greater message processing than do gain-framed messages? A meta-analytic review. Commun Stud 59(1): 51–67 92. Slavin S, Batrouney C, Murphy D (2007) Fear appeals and treatment side-effects: an effective combination for hiv prevention? AIDS Care 19(1):130–137 93. Shawky S, Kubacki K, Dietrich T, Weaven S (2019) Using social media to create engagement: a social marketing review. J Soc Mark 9(2):204–224 94. Casais B, Proença JF (2012) Inhibitions and implications associated with celebrity participation in health-related social marketing: an exploratory research focused on hiv prevention in portugal. Health Mark Q 29(3):206–222 95. Marques IR, Casais B, Camilleri MA (2020) The effect of macro celebrity and micro influencer endorsements on consumer-brand engagement on Instagram. In: Camileri MA (ed) Strategic corporate communication in the digital age. Emerald, Bingley, UK 96. Shaw A (2018) Using the social return on investment framework to evaluate behavior changes of individuals living with learning difficulties. Soc Mark Q 24(4):281–298

Beatriz Casais is a Ph.D. in Business and Management Studies– specialization in Marketing and Strategy, by the University of Porto, and assistant professor of marketing and strategy at the University of Minho, School of Economics and Management, Portugal. She was the marketing and communication manager at the National Coordination for HIV/AIDS Infection, Portuguese Ministry of Health. She was responsible for developing the social marketing strategy of HIV/AIDS in Portugal between 2006 and 2011. As an academic and researcher, she has been publishing in international scientific journals in the field of social marketing, as the Journal of Social Marketing, Journal of Macromarketing, Social Sciences, International Review on Public and Nonprofit Marketing, and Health Marketing Quarterly, as well as in the field of corporate social responsibility, place branding and digital marketing applied to tourism. Beatriz has also published book chapters and pedagogical case studies. João F. Proença is a full professor at the University of Porto and researcher at the Advance-CSG, ISEG, University of Lisbon, Portugal. He has been the Rector of the Universidade Europeia, Lisbon, Portugal, and the Dean of the Faculty of Economics, University of Porto, where he was also in charge of relevant positions as Director of the Ph.D., M.Sc, or B.Sc. He also held relevant professional positions in companies as administrator, CEO, managing director, or sales director. Furthermore, he has more than 150 papers published in several academic journals, such as Industrial Marketing Management, Journal of Service Management, Services Industry Journal, and the Journal of Business & Industrial Marketing, among many others. He also has published books, book chapters, conference papers, and opinion articles in newspapers and magazines. His research interests cover sustainability, services, B2B marketing, relationships and business networks, and the links between industry and services.

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The Significance of Super Intelligence of Artificial Intelligence Agencies in the Social Savageries of COVID-19: An Appraisal Kabita Das, Manaswini Pattanaik, and Biswaranjan Paital

Much has been written about AI’s potential to reflect both the best and the worst of humanity. For example, we have seen AI providing conversation and comfort to the lonely; we have also seen AI engaging in racial discrimination. Yet the biggest harm that AI is likely to do to individuals in the short term is job displacement, as the amount of work we can automate with AI is vastly bigger than before. As leaders, it is incumbent on all of us to make sure we are building a world in which every individual has an opportunity to thrive. Andrew Ng

Summary

The world is now distressed with the coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although vaccination is not done in full fledge and a particular drug is not available to treat COVID-19, modern technologies help humanity fight against it. Data science is a part of modern technological approaches which involve many technologies like artificial intelligence (AI) and machine learning under a single roof. This chapter focuses on using AI in the management of COVID-19 pandemic for various purposes and making the dichotomy between intelligent man and intelligent machine. AI can function as a radar, robot, computer, machine for medical uses, engine driving, teaching in educational institutions, and other

K. Das
M. Pattanaik Post Graduate Department of Philosophy, Utkal University, VaniVihar,, Bhubaneswar, India B. Paital (&) Redox Regulation Laboratory, Department of Zoology, College of Basic Science and Humanities, Odisha University of Agriculture and Technology, Bhubaneswar 751003, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_16

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purposes at micro-business and macro-business levels during the current pandemic. AI also brings hope for the prevention of this deadly infection using drones and robots for the disinfection of public places and the cities under lockdown, for quicky transfer of samples for testing, to deliver medical supplies, food, and small medical devices to a health care provider, and for cleaning and testing in quarantine centers. AI has already arrived in every sector, but few doubts exist asking us: can AI act like a sagacious person? Until now, ‘do not,’ but it is believable that it can be possible soon. Graphical Abstract/Art Performance

Artificial intelligence applications during pandemics

The code of this chapter is 01101110 01110100 01001001 01100101 01100101 01101100 01101001 01101110 01100101 01101100 01100111 01100011. Keywords













Artificial intelligence COVID-19 Intelligent machine Pandemic Sagacious machine Simulation




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The Significance of Super Intelligence of Artificial Intelligence …

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Introduction

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The present pandemic coronavirus disease (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is a highly infectious and dangerous disease that has devastated the world. This deadly disease-causing virus [1, 2] was first reported in Wuhan, China, placed under high alert to reduce the fatal disease spread: the public places and different institutions (like educational, spiritual, political, sports institutions) were shut down [1–3]. Despite this, it spread worldwide, so the world health organization (WHO) declared it a global pandemic on 11 March 2020. Due to the COVID-19 pandemic, people in over 224 countries, areas, or territories have been severely affected. A total of 636,440,663 confirmed cases and 6,606,624 deaths were reported as on 28 November 2022. The symptoms like body ache, breathing issues, cough, diarrhea, fever, muscle pain, vomiting, and loss in taste and smell, are most common to the patients affected by COVID-19 [4]. SARS-CoV-2 is transmitted directly through close contacts like touch or handshakes with an infected person and respiratory droplets. It can also spread indirectly by touching the contaminated surface [5]. Social distancing, isolation, and staying at home are the best way to break the chain of COVID-19 [5]. In the health care system, the use of AI is prominent. Of particular interest is its application in COVID-19 diagnosis under the crisis period. Medical images such as computed tomography (CT), histological figures, X-ray pictures, and ultrasound data or figures are better analyzed using AI to manage the disease better. The faster analysis and diagnosis is critical to saving the life of COVID-19 and non-COVID-19 patients in emergency [6–8]. Designing and deploying AI-based medical image analysis tools in a short period with limited data has become an urgent need. In this present health crisis, AI is the most effective technology to observe and control the disease spread [9]. In the current scenario, most people worldwide are accustomed to AI as a particular means to understand the virus and develop preventive and control measures [10]. This chapter focuses on how AI’s superintelligence could be a potent weapon to handle the COVID-19 pandemic and its subsequent issues. Moreover, we also show the differences between human responses to stimuli and those given by AI machines. Intelligence has two capacities: i. achieving mixed goals in mixed environments; and ii. acquiring better knowledge and thinking and reasoning skills. AI is one of the most prominent branches whose motto is to imitate human intelligence to computer software out of the various computer science branches. The term “Artificial Intelligence” was first introduced by John McCarthy in 1956 as “the science and engineering of making intelligent machines” [11]. Reasoning, knowing, analyzing, planning, predicting, learning, communication, interpretation, perception, and the ability to move, curate, and manipulate objects and happenings are the aspects of human intelligence that have been imitated by AI software.

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To imitate human intelligence, AI uses various tools. As biological nervous systems are created in the human body, similarly artificial neural networks have been designed, developed, and tested using generalizations of mathematical formulae and models. A neural network is needed to create different network architecture, such as the connection mode, strength, duration between pairs of neurons (weights), their node properties, and update rules as per the current need, such as COVID-19 crisis. The weights and the processing elements (neurons) are controlled by learning rules whose function determines the network’s complete status [12]. The network is used to generalize from known to unknown tasks and handle imprecise information. AI can be more useful by following its in-depth weights to make changes that are required or already done in the surroundings. In a very short period, AI tools such as robots and drones have been very useful and could help humans, especially in current pandemics. Robots aid in supplying medical equipment, including food, medicines, clothes, utensils, and many more things, to coronavirus-affected patients in hospitals, while drones are used to disinfect streets and other public places [13]. It can also be used to detect, predict, and explain COVID-19 infections [10]. As AI is believed to be a new and modern invention, there are long histories about it where AI was prevalent. In Ancient Greece mythology, in Europe and the Arab World, they thought about light and some programmable robots during the Dark ages. In the past-renaissance time, they thought about mechanical calculators. After the seventeenth century, they developed humanized machines during the Industrial Revolution, and after world war II, they thought about economic and scientific development using AI [14].

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Use of AI During COVID-19 Pandemic

Humankind in current times is very much physically and mentally dependent on AI for various day-to-day applications. It is broadly used in every sphere, especially in the medical domain, such as AI concepts, clinical techniques, diagnostics, prediction of appropriate medicines from previous treatment schedules, and results based on patient’s history and tools employed in medical cases. AI aims to help health care professionals by supporting and improving their effectiveness, productivity, and consistency, and tackling problems in this field. AI techniques have steadily increased their accuracy and efficiency and the availability of AI software [15]. The traditional method of AI named as diagnosis, therapy, automatic classification, rehabilitation, and the current improvement of AI application in this sphere is identifying or predicting disease-causing genes, wearable computations, hospital clinic management, visualization of patient’s history and response, medical applications of robotics, surgery simulation for micro-operations, artificial consciousness for patient’s mental health management, etc.

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AI has already dominated the medical field, and literature surveys have been carried out regarding its use. Networks used in this field are of two types. The first one was the neural networks, and the second one was designated fuzzy logic–neural networks. The former was the most frequently used analytical tool. The latter was the most often used AI technique with intensive interests in genetics, cardiology, radiology, and so forth [16, 17]. Machine learning-based technologies are very time-consuming and speedy, playing a vital role in current pandemic situations. In medical fields, clinical professionals use AI in the form of machine learning to study viruses and bacteria or other pathogens, to perform diagnostic tests, predict potential treatment schedules, diagnose individual drug dose responses, and overall to implement the obtained knowledge in the public health management control system [18]. AI needs its data-consuming method very speedily in large numbers and replicates human intelligence to identify patterns and insights. So, for preparedness in crucial time the machine learning has been applied by many organizations without any delay, such as scaling customer or patient’s communications and responses, response of the society and family members to the COVID-19 infected persons, understanding the contagious nature of COVID-19 in general and contextual cases, for example, under high air pollution conditions, and the present research and treatment about drug development and vaccination against COVID-19 [19]. Many organizations, such as health care and government, work depending on the machine learning process. These organizations permitted chatbots for all possible contactless screening of COVID-19 symptoms, identifying asymptomatic cases, and resolving the issues of both public with or without infection. For example, Clevy is a French start-up, and a customer service program has launched a chatbot to make easy information exchange to the public from government official communications about COVID-19. It would help restrict the spreading of false news about COVID-19 [19]. AI is also used effectively in handling this life-threatening coronavirus pandemic in many ways, like alarming the early detection of the outbreak, visualization and monitoring the disease’s spread, and predicting infection risk for early and useful treatment. AI can help make rapid decisions and discover medicine and vaccines [20]. AI can be used by following ways for handling this life-threatening situation, by [6, 7, 21, 22]: • analyzing chest X-ray, CT scans, and other clinical symptoms to detect COVID-19; • making drug or vaccine discovery for coronavirus; • analyzing the effect of existing medicines on coronavirus; and • analyzing remedies for the current and future attacks of the same type. A workshop held by the National Institute of Health working in partnership with other services and agencies in July 2019 reported that “Machine Intelligence (MI) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to

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make more informed decisions and achieve better outcomes. When deployed in health care settings, these approaches have the potential to enhance efficiency and effectiveness of the health research and care ecosystem, and ultimately improve quality of patient care” [23–26]. AI is being merged for the first line of defense in the pandemic. Renowned epidemiologists suggest that AI can help in the current situations in predicting the spreading rate of SARS-CoV-2. Thus, advanced steps may be taken to combat the present and upcoming issues such as the present condition of the disease in society; data analysis to find the most susceptible person that may contract the disease; and the use of machine learning mathematical algorithms for scanning to measure temperatures, locate and identify individual datum of the infected persons, and to verify the existing current information for facilitating the public essential supply chain [23–25]. Overall, AI is made by human intelligence and imitates everything as a human mind does, but it replaces human intelligence in a faster sensor and an organized manner [28]. Under the quick spreading of COVID-19, researchers and clinicians tried to access, identify, and analyze the available data on time to make a quick conclusion about the disease until proper vaccination or drugs are not developed [25, 29, 30]. Therefore, repurposing of drugs was employed in many health care centers to save many infected COVID-19 patients. According to a very recent study, AI can help distinguish COVID-19 from other lung diseases. Few Chinese physicians analyzed the available data during the initial months of the COVID-19 outbreak only from the patient’s chest CT scanning results. They planned to use this preliminary CT scan data as a marker to identify the stages of infection and distinguish other respiratory diseases from COVID-19 cases. They concluded that deep learning and data from CT scan reports may be helpful but must not be considered the only source of identification [6, 7, 25, 26, 31, 32].

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AI is Used to Recognize, Predict, and Forecast the Path of Outbreaks

Human beings could recognize the path of the spread of this virus. AI plays a pivotal role in efficiently forecasting and tracking these outbreaks. AI technology analyses the data available at the latest news reports, various social media platforms, updates, health care reports, and government official statements. It would be more convenient to predict many factors and aspects that can help fight against such a global crisis [33, 34]. For example, a Canadian company named, BlueDot, developed a Toronto-based start-up that aims to detect the virus. It uses AI algorithms to build prediction models to detect the virus [25–27, 31, 32, 35]. BlueDot uses Natural Language Processing (NLP) and machine learning to gather data from social media, government papers, and health care data to construct intelligence. It can track the paths of the epidemics of over 100 different diseases every 15 min around the clock [36–38]. BlueDot not only successfully detected the outbreak of the Zika virus in Florida but also spotted coronavirus nine days before the

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declaration released by World Health Organisation (WHO) for alerting people [20, 39]. This program has been proposed to predict infectious diseases and to locate and track their spreading. The Indian government has released a mobile application called “Aarogya Setu” to track COVID-19. This application developed by the National Informatics Centre is a tracking system that uses GPS and Bluetooth features to track infection [25–27, 31, 32, 40]. Governmental organizations take AI applications’ help and update all details regarding the coronavirus, like the probability of being infected. AI technology would forecast the expected areas of contamination, the spread of the infection, and the beds and health care workers’ requirements during this pandemic [35, 41]. As the current pandemic affected the whole world, policymakers, the medical community, and other parts of society have used AI tools for the early detection, prevention, response, and recovery from the disease [42]. Many scientists dedicatedly contributed to this COVID-19 time, how COVID-19 can be detected. For example, the early version of the COVID Voice detector, introduced by scientists from Carnegie Mellon, is an application that would analyze a patient’s voice pattern (as throat infection is common in COVID-19 patients) to detect the infection. This app depends on people by collecting voice samples from healthy and infected persons. It can analyze the voice which the human ear is unable to hear. It also helps the health care community understand the symptoms and the origin of SARS-CoV-2. This app uses AI technology to relate coronavirus symptoms with the individuals’ voice, whereby using only a smartphone, one can monitor the ways and the early symptoms through an alert. However, this type of application is not entered yet into many countries. It is totally “out of the box” [25–27, 31, 32, 39]. These innovative and advanced applications are possible due to collecting and leaping up such obtained information and machine learning practice that apply to coronavirus. Implications of these datasets require the involvement of scientists, including biologists, chemists, physicians, and other associated professionals, to ensure that the outcome or information people get from this app would not be falsifiable, and these conversations regarding the pandemic would be truly defined [25–27, 29, 30, 43]. Ethical implications are needed to form a peaceful life in this current time of the pandemic [44]. The real-life examples are creating challenges to the AI system. To fight against coronavirus, can AI help to make life or death decisions? Some researchers of China developed a machine learning tool that can support doctors to give advice and treatment to the seriously affected COVID-19 patients by analyzing the blood samples to compare survival rates. Can it be possible for an AI tool to discuss survivability/treatability in a triage prioritization? A human mind is equipped with ethics, but AI does not. For example, a doctor’s intuition checks the patient’s age and accordingly designs the treatment. Here, an AI can assist the doctors in several ways, such as identifying the patients from blood samples and correlating the survival rates with those of serious conditions of the patients [40, 45]. That is why in real cases, the health care professionals ranging from the paramedical staff to clinicians and diagnostics professionals such as pathologists and radiologists, are facing high stress, the real difficulties in hospitals due to

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constraints with resources including basic information and medicines under various environments that came in COVID-19 pandemic [25–30, 46]. Government organizations’ office-bearers gradually emphasize both low-technological solutions (e.g., using face masks, personal protective equipment (PPE) kits, sanitizers, etc.) and high-technological solutions such as ventilators, testing kits, testing machines that could create major help to health care practitioners. According to these high-tech solutions, many tools help the practitioners in this crucial period. For example, ‘suki’ is an AI combined with the power of a voice assistant used by doctors to record conversations and specify complete clinical suggestions further for patients with a suspected diagnosis of COVID-19 and any other (such) diseases. This Suki data is highly delicate, and it results from clinical interaction and health records. Another form of voice detection by AI tool is named ‘KARA.’ It is a product of the iPhone produced by ‘saykara’ and has been considered “test-piloting the solution.” It was designed for communication to chart the chatting (e.g., telehealth) of remote patients who encounter a problem with physical access to physicians [25–32, 40]. The two potent AI tools are ‘EPIC’ and ‘EKO.’ EPIC, an AI voice assistant who assists through voice to maintain records and give new information from monitoring the COVID-19 patients. Another AI tool ‘EKO’ is a stethoscope. Its neural network detects and differentiates between normal or expected and abnormal or unwanted cardiac sounds produced by the heart during circulation. As EKO is a “wireless auscultation” product for the heart and lungs, the practitioner can use this tool to carefully listen to their patient after wearing lots of PPE kits. As the COVID-19 outbreak started in China, this country uses the powerful AI tools of deep learning techniques such as X-rays’ film patterns, results of biochemical tests, pathogenic identifications, CT patterns, and patterns obtained from the magnetic resonance imaging (MRI), and results of positron emission technology (PET) scan for addressing COVID-19 diagnosis and analyzing the speed of time for image interpretation [25–30]. Many companies, through innovations, use this deep learning techniques because the growth of such profound learning expands the rapidity and correctness in the clarification of diagnostic imaging results. For example, patterns obtained from the chest X-ray are powerful AI that can be used for pneumonia patients. To elevate and include the population of COVID-19 patients in analysis for getting robust results, a developer named Surgisphere from Quartz Clinical health care data analytics has created a tool and named it as “decision support tool” that incorporates “three common laboratory tests to identify patients likely to have coronavirus infection.”

3.1 AI for Detection of the Virus and Tracing COVID-19 Real and Unreal Patient AI is a potent tool as it diagnoses both symptomatic and asymptomatic COVID-19 positive patients in a short time, so it is very much essential and needful in the current outbreak. The time is saving, and better treatment helps health workers and

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governments diminish the pandemic’s stress. Chest X-ray and CT images are used as significant methodical AI input [47]. However, a Beijing-based AI Company named “infervision” has released a coronavirus detection mechanism to detect and prevent the virus outbreak by analyzing the patient’s condition [32, 40]. Evidence shows that infervision was employed at the pandemic’s epicenter at Wuhan’s Tongji Hospital. The speed of CT diagnosis has been enhanced thanks to infervision, which is critical for detecting pneumonia in suspected patients as early as possible [32, 38]. This AI-based solution company has proved very useful in this life-threatening coronavirus outbreak. Fast and accurate diagnosis of coronavirus can save lives and limit the spread of the disease. Mobile robots and automated camera systems are used in public areas for temperature measurement, screening, and diagnosis on a large scale [25–27]. AI-based CT scan is an advanced technology applied to detect, monitor, and differentiate between COVID-19 infected persons and healthy persons [32, 48].

3.2 AI for Rapid Detection of Patient Number by Contact Tracing Contact tracing is an effective method to defeat the COVID-19 pandemic. As per WHO guidelines, there are three steps for contact tracing such as: i. tracking the infected person with their travel history; ii. registering the evidence of those individuals; and iii. confirming cases that require intervention in health care testing. Today, AI is worthy and helpful to communicate with others and save people in large numbers in this outbreak [49–52]. What is the work of AI developers in this outbreak? This is not only AI developers’ work to help the health care practitioner, but it is also a responsibility of the community. However, it is more concern for AI developers to develop the models and tools to help the practitioners like guiding for drug development, diagnostic app, contact tracing app, tracking app, and many more [49, 50, 53].

3.3 AI for Predicting Patient Number to Take Precaution AI technology helps detect the patients infected by the coronavirus through medical imaging machines with MRI and CT examinations of people’s various body components [41, 49, 50]. AI provides modernized data useful in preventing the illness by using real-time information inspection [41]. There are two most effective methods to break the chain of this deadly virus, i.e., social distancing and home quarantine airborne precautions. Other protective measures have been proposed to prevent the disease [54]. Individuals need various kinds of PPE like face masks, face shields, and hand gloves to reduce infection risk while traveling through public transport [55]. Medical staff come under sensitive individuals as they directly

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contact COVID-19-affected patients. So proper training about prevention and protection methods must be given to them. As a result, they can give self-protection and protection to the patients in general and COVID-19 patients in particular. So, using PPE kits such as face masks (N95 or FFP3), face shields, goggles to protect eyes, medical gowns, and gloves is a must to minimize infections [55].

3.4 AI in Robotic Cleaning to Prevent Spreading of COVID-19 To stop spreading and minimize the risk factor of COVID-19 from closed environments in public spheres such as hospitals, shopping malls, apartments, community centers, etc., cleaning and disinfecting are essential methods [46, 56]. So, AI technology-based robot cleaners are used in health care sectors to disinfect the surfaces for limiting the transfer of the disease via contaminated surfaces. One famous AI technology named Asimov Robotics from India has made a three-wheeled robot to treat patients residing in isolation wards. It is also used to serve patients’ foods and medications [51].

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AI for Diagnostic Purposes

4.1 Use of AI in the Development of Drugs, Vaccines, and Technical Check-Ups of Patients and Doctors The health care professionals such as doctors, nurses, sanitary workers, lab technicians, and other health care workers have needed this AI technology to fight against this outbreak with better facilities. They must be protected from the virus using PPE kits, including surgical and respiratory masks, goggles, gloves, medical gowns, face shields, shoes, head cover, etc. Re-use of any PPE kits must be properly washed, sanitized, or decontaminated [57]. Moreover, they can also use AI technology to reduce person-to-person contact [40]. Health care professionals are the real heroes who save others’ lives by risking their own lives. Some health care workers have already tested positive. So, AI may help reduce the spread of the disease [40, 57, 58]. There are currently no licensed drugs, but few vaccines are available to treat coronavirus disease. Therefore, supportive care is the only major treatment option available for severe illness patients [58–60]. Supportive care involves isolating the patient to an isolation room under negative pressure and providing adequate rest, hydration, nutritional support, and electrolyte balance [59]. Admission to the intensive care unit (ICU) is required for close monitoring or organ support to the patients who require oxygen. For drug and vaccine development AI can be beneficial. Plasma therapy and drugs like arbidol, atazanavir, remdesivir, and favipiravir are under-testing or used under the drug repurposing category [22]. AI is useful for quick drug trials rather than normal trials, which take much time. Several trials for

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developing vaccines, tests, and treatments are currently undergoing around the globe [5, 60]. Epidemic forecasting and monitoring the immunization power are the two most important tools in AI for clinical management. AI supports a different health care system, such as implementing telemedicine for COVID-19 patients using the CLEW-ICU, a Tele-ICU solution. This platform uses the AI predictive analytics method to enlarge or expand the ICU’s capacity and resources. The algorithm allows identifying respiratory levels in advance. It enables early predictions and interventions that bring a clinical change to COVID-19 patients, which also helps to notice the disease’s severity and monitoring the patients, and give better facilities even from an isolated place. It is like a war for the telemedicine and telehealth workers on the job, addressing multiple queries of COVID-19 patients. AI tools and applications are helping the telemedicine workers by assisting and tracking the patients from different allocated places and making their job a little tireless [59, 60].

4.2 Use of Drones to Deliver Medical Suppliers Drones are widely used in this coronavirus pandemic. During the lockdown, AIpowered drone delivery is one of the fastest ways to supply medicines and other medical equipment without person-to-person contact [61]. To disinfect public places, high-power AI tools are used, such as drones for aerial spray, transfer of medical samples, and quarantine materials in the cities under lockdown and consumer goods delivery [58]. Surveillance drones are used to monitor individuals throughout lockdowns, dispersing public gatherings, traffic observation, waste disposal observation, identifying persons not following social distancing and not using the mask, and determining the persons who violate the laws made by the government [62]. Drones are not only used to minimize human interaction but also used to reach inaccessible areas [51]. Drones are also used to make public announcements and screen the masses.

4.3 Use of Robots for Sterilizing and Touchless Sanitation Robots are not affected by the virus, and they cannot transmit the virus to humans, so they can be used to perform the tasks like cleaning, sterilizing, and delivering food and medicines during the outbreak. Robots are used to sterilize everything that comes in contact with humans. The sterilizing process involves food items, clothes, utensils, medical equipment, and many more. AI allows health care facilities to deliver advanced treatment and supports them with highly equipped technology to provide better patient care during the pandemic.

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4.4 Use of AI for Money Transaction and Marketing Systems in the Pandemic Currency notes also carry viruses on them. So, the World Bank is advised to minimize cash transactions and promote digital transactions through e-cards or other payment apps. Using net banking or online payment, we can maintain social distance and live inside our house [63]. During this pandemic situation, the marketing system also plays a major role. People depend on the market for buying their necessary things, which are used daily like food items, grocery items, clothes, utensils, and many more things. Social distancing and sanitization are the major problems observed in the marketplace. So, to avoid this gathering problem, a home delivery service was suggested by the government. However, the delivery boy may also get infected during the service, and therefore AI-based robots are developed for home delivery to prevent the spread of the coronavirus [64]. Due to the nationwide lockdown, there is a significant shift in the share market and the world economic market. Many countries have faced financial problems like shortages of goods and services [61, 65, 66]. Due to this reason, there is a rise occurring in various products, and consumers are suffering. We can take the example of petrol. Because of lockdown, there is less demand for petrol, and therefore its price is very high.

4.5 Use of AI in Coronavirus Prevention It is not new to understand that deep learning and machine learning can count for human behavior and individual thinking dynamics. AI can analyze and predict a disaster or a disease outbreak in advance [29]. Deep learning and machine learning are also used to analyze people’s data by tracking mobile locations. WHO has suggested some preventive measures to avoid the infection of the virus. These measures, for example, are washing hands for 20 s by using sanitizer, which contains alcohol, or using soap with water and maintaining a social and physical distance. Also, using a face mask for covering the mouth and nose [64] is necessary when someone sneezes and coughs; one must cover the nose and mouth so that the virus would not be transmitted to others. Also, to avoid touching the eyes, nose, and mouth that act as the virus’s entry point, with bare and unwashed hands, and avoiding personal contacts such as kissing, hugging, and sharing cups or eating utensils with an infected person is suggested [67]. AI technology plays an important role in detecting and preventing coronavirus spread. Thermal testing is the first step towards detecting the virus by observing body temperature. A mobile application developed in India named Aarogya Setu is used for self-testing if anyone fills an illness [64]. This app helps the government find or track those who previously contacted the infected person. Travelers from COVID-19 affected countries must be quarantined properly to diminish the spread of the disease. Some other typical recommendations to avoid the spread of the disease are [5]:

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maintaining at-least 1-m distance with anyone; covering face with an elbow while coughing or sneezing; avoiding face, eye, and nose touch; avoiding less-cooked or raw meat; staying quarantine if someone is feeling unwell; abstain from smoking; practice physical or social distancing; avoid travel or large-gatherings; and stay home if it is not much necessary to go out.

AI for Social Relation and Emotional Bond During the COVID-19 Pandemic

Human beings are social animals, and therefore social relation is essential for their existence in society. However, this social relation is now hampered due to the coronavirus pandemic as the government implements social distancing, lockdown, and self-isolation to reduce the spread of the disease [46, 61, 65]. Because of the absence of these social relations, human beings lead stressful life like loneliness, anxiety, depression, mental disorders, health hazards, and many other issues. As a result, it affects the individual’s life and society as a whole [68]. The impact of COVID-19 is reflected in all the spheres of human life, such as economic, political, social, cultural, and ecological. As a result, a human being is affected both physically and mentally. His emotional state of mind is also affected [66]. The coronavirus’s most emotional effect is that the dead body of COVID-affected people cannot be given to the family members and relatives due to lockdowns and restricting possible infection of healthy subjects. Nationwide lockdown closes all the educational institutions, religious places, entertainment places like parks, cinema halls, and restaurants for an unlimited period. It has a major impact on children and adults [61, 65]. The lockdown period was a golden opportunity for parents to spend quality time with their children and family members. During this period, we can productively use time by performing many activities. We can use the unusable lands for farming organic vegetables. Using clean cotton, we can sew masks in the home as it is a compulsory item in the present situation. People can utilize time by learning new things from electronic media or E-media. Exercise and yoga help us avoid stress and depression, keeping us fit and healthy. People can give some time to hobbies like painting, craft making using waste material, singing, dancing, cooking, gardening, reading, writing, and many more [28, 65, 66]. Many indoor games are safe and helpful for children to develop their skills. These games are chess, ludo, carrom, puzzle-solving, word game, building blocks, hide and seek, etc. The above activities can help reduce children’s stress and anxiety through parents’ involvement with their children.

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Older people (above age 60) are more prone to COVID due to weaker immune systems and age-associated other health issues. Social distancing may harm their mind, as social isolation and loneliness can elevate their risk of catching anxiety and associated symptoms, depression, an acute rise in blood pressure, cognitive dysfunction, heart disease, etc. [61, 65]. Older people are dependent on younger people for their daily needs, and self-isolation or social distancing may damage their mental condition. So, we can practice physical distancing of at least six feet instead of social and mental distancing from them. Physical distancing does not mean abstaining from meeting older people. Younger people should take care of older people and spend some time with them daily, reducing their stress levels [61, 65, 69].

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AI for Education, Entertainment, and the Mental Growth During Lockdowns

Throughout these difficult periods, human beings have various defies of life: freedom, social-religious integrity, fraternity, moral regulation, business, trade, food, and education. It is, thus, not surprising to fall into mood swings, melancholy, fretfulness, fright, and deep emotional crisis, in some cases even desperate. Lockdown is the most valuable way to break the chain of spreading coronavirus [46, 61, 65]. Educational institutions were first to the shutdown, which seriously changed millions of students’ and faculties’ lives worldwide during this pandemic period. As a result, it disrupted the learning and education of numerous students [65]. Without access to schools, all the prime responsibilities like hygiene, proper handwashing techniques, and adjusting to situations fall on parents and guardians. Moreover, government agencies have provided rich and accurate public health information when schools are closed through appropriate media. They have started distance learning and online classes to recover the study syllabus through video conferencing apps, cloud meetings, work collaboration tools, team viewer apps, virtual private networks, etc. Many institutes plan for online exams. As a result, students can qualify for the annual examination [63, 65]. Confinement in homes is a serious matter for kids, and it has a challenging impact on people’s mindsets. During the lockdown period, because of children’s home confinement, there are also side effects like increased stress, prolonged fear, frustration, boredom, etc. [65, 70]. UNESCO has taken several steps to ensure that education systems should sustain adequately. Some countries already provide online facilities such as Google meet, zoom, WebEx, etc., to students and teachers to ensure learning and teaching continuity. However, there are various obstacles in many countries, communities, families, or social groups to access the internet facility. Many children live in rural areas with either a poor network or no network at all [65]. Children with disabilities, those affected by their location, family situation, and other inequalities face challenges in their education from the start, and they face new challenges during the pandemic. Families with low incomes face additional challenges due to school closures, such

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as the midday meal program, which effectively improves a child’s physical and psychosocial health in India. The government should take preventive measures like ensuring the continuity of mid-day meals during school closure and reducing children’s disproportionate challenges [65]. The major impacts of COVID-19 pandemics on society can be economic, social, and political. The other side effects of such pandemic are individual behavioral change, recession, and credulity [5, 61, 65]. Mental health and stress level patterns during COVID-19 pandemics can be detected and analyzed using AI and deep learning technology [5]. Because of the COVID-19 pandemic, people are being locked down and quarantined, so social media platforms are being used as a source of information and entertainment. AI-based technology also plays a pivotal role in communication, helping people avoid stress. AI technology-based digital gaming software is also a source of entertainment for youngsters [63]. Moral obligation is another important factor in caring for COVID-19 patients living in isolation in ICU. Nurses, especially those engaged at ICU to take care of ill patients, feel morally distressed because patients cannot meet their family and friends [71], and many of the patients even die without meeting their families. Although considered dehumanization and inhumane care, it is the only option available for nurses, doctors, patients, and families. On the other hand, the paramedical and medical staff are also kept isolated to prevent a possible infection from their family members. Under such conditions, telephonic conversations, video calls, and other morale boosters are helpful for all above persons, including patients [65, 71]. So maintaining the moral and mental health of COVID-19 patients, medical and paramedical staff in hospitals, and emergency supply chains workers out of the hospital are very important under such pandemics. It will diminish their moral distress to maintain the social balance. Transparent, true, mindful, and supporting communication with minimum red-tapism at the government level is suggested [72–75]. AI’s roles in measuring the impacts of COVID-19 on environments [76–80], society, education system, health care units, economics, etc., seem to be very important [72–75]. It has been said that AI could be used to solve a very bad problem in medical care during the COVID-19 outbreak. Discrimination of patients could be solved with AI. AI also brings hope to prevent this deadly virus using drones and robots. Drones can be used for the disinfection of public places. The cities under lockdown drones can quickly transfer samples to the testing centers and deliver medical supplies to the health care provider. Robots can be used to deliver food and medical equipment supplies and clean and test in quarantine centers to reduce the transmission of the disease from patients to other frontline health care workers. An intelligent person is a sagacious person. However, can it be considered an intelligent machine, as is a sagacious machine? The earth’s people have solemnly resolved to make the earth into a digitalized, computerized, artificialized, and robotized to secure all human beings. All it is possible due to making intellect machinery. Machines are made by human intelligence, and it also works as a human mind, tone, and action; in other words, one can say that AI is the replica of the most intelligent being on this planet, i.e., human, and it renovates and updates the human

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being in current times. AI shows its qualities associated with the human mind, such as learning and problem-solving. Despite this problem-solving nature, it also helped detect the problem and lead to good actions. AI is so useful about the COVID-19 pandemic that it can be a potent weapon in the fight against the coronavirus. Because AI can track the virus, it will be useful to humanity to fight it. By analyzing the data available on news reports, social media platforms, government documents, published literature, audio–video files, and medical and clinical approaches with COVID-19 patients, AI can assemble all data to detect an outbreak as COVID-19. After detecting the disease, people will adopt preventive measures to reduce coronavirus spread. It is also about to think that if humankind could develop AI at such a level that AI could work like doctors and nurses, then probably the doctors and nurses would not die by the effect of coronavirus.

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Conclusion

Human beings are the most intelligent being on this earth with highly equipped technology and medical services, but the present pandemic has made them much vulnerable one in this planet and also weakened the human mind and body. Human beings have to shift to the advanced stage of health care, where AI comes to the rescue. AI is the most important invention by a human being. It outperforms humans when it comes to its main functions, making the medical industry better and people healthier quickly. AI can help dig through the reports worldwide by alerting the experts regarding the abnormalities or the oddity before reaching the ground. The most useful and upcoming tool is AI to identify the prior infections caused by a coronavirus and monitor the condition of affected patients. AI can drastically improve treatment consistency and decision-making ability by developing useful algorithms. In the past pandemics, people did not get the right medical treatment or take the right steps to avoid getting sick; consequently, many deaths before the disease was found. AI is the technological revolution upon the path towards human beings and the best and helpful way to treat COVID-19. Due to the increase in AI accuracy, it is used to make management decisions in pandemics and thus leads a pivotal role in urban health policy. To avoid the spread of this deadly virus, health care organizations require certain kinds of decision-making technologies through which people can get suitable suggestions within the time. Digital technologies are also essential for vaccine development. It also helps health care organizations to properly screen, analyze, and track current patients to future patients. In this pandemic’s current research systems, the AI tools and databases are robust and easily accessible for urgent diagnosis and therapeutic processes. The world needs the new AI-powered tools to fight against this COVID as well as to take care of the post-vital consequences of the pandemic.

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Core Messages

• Artificial Intelligence (AI) systematically uses data via modern technologies to efficiently handle the COVID-19 pandemic. • AI is useful for creating awareness and taking smart actions using telemedicine or robotic therapies. • Robotics mode of disease diagnosis, treatment algorithms, education delivery, etc., under COVID-19, is important. • For example, automated real-time sanitization, treatment of COVID-19 patients by drones or robots are encouraged. • Creating a global network among all countries using AI to fight against COVID-19 must be the call of the day.

Acknowledgements BP acknowledges schemes (number No. ECR/2016/001984 by SERB, DST, Govt. of India and 1188/ST, Bhubaneswar, dated 01.03.17, ST- (Bio)-02/2017 and DST, Govt. of Odisha, India). KD has received funding (36 Seed/2019/Philosophy-1, letter number 941/69/OSHEC/2019 dt 22.11.19) from the Department of Higher Education, Govt. of Odisha under the OURIIP scheme. The authors acknowledge Prof. Pravat Kumar Roul, honorable Vice-Chancellor of Odisha University of Agriculture and Technology, and Prof. Sabita Acharya, Honourable Vice-Chancellor of Utkal University Bhubaneswar, for their encouragement for which this article could be written. Also, the authors express their gratitude to Dr. Shravani Bhanja for language correction.

References 1. Zhou P, Yang XL, Wang XG (2020) A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579:270–273 2. Li Q, Guan X, Wu P (2020) Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 382:1199–1207 3. Niiler E (2020) An AI epidemiologist sent the first warnings of the Wuhan virus. https://www. wired.com/story/ai-epidemiologist-wuhan-public-health-warnings/. Accessed 23 July 2020 4. Salman M, Fatima AN, Samy S, Alajrami E, Naser A, Bassem S, Ashqar BAM (2020) COVID-19 detection using artificial intelligence. Int J AcaEng Res 4(3):18–25 5. Chawala S, Mittal M, Chawala M, Goyal LM (2020) Corona virus—SARS-CoV-2: an insight to another way of natural disaster. EAI End Trans Perv Heal & Tech 6:22 6. Wan KH, Huang SS, Young A, Lam DS (2020) Precautionary measures needed for ophthalmologists during pandemic of the coronavirus disease 2019 (COVID-19). Acta Ophthalmol 98(3):221–222 7. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, Cao K (2020) Artificial intelligence distinguishes COVID-19 from community-acquired pneumonia on chest CT. Radiology 19:200905 8. Smeulders AW, Van GAM (1989) An analysis of pathology knowledge and decision making for the development of artificial intelligence-based consulting systems. Anal Quant Cytol Histo 11(3):154e65 9. Vaishya R, Javaid M, Khan IH, Haleem A et al (2020) Artificial intelligence (AI) applications for COVID-19 pandemic. Dia & Meta Syn: Clin-Res& Rev 14:337e339

378

K. Das et al.

10. Naude W (2020) Artificial intelligence against COVID-19: an early review. http://hdl.Handle. net/10419/216422 11. Andresen SL (2002) John McCarthy: father of AI. Intelligent Systems 17(5):84–85 12. Zurada MJ (1992) Introduction to artificial neural system. West Publishing Co., New York 13. Nguyen TT (2020). Artificial intelligence in the battle against coronavirus (COVID-19): a survey and future research directions. Preprint. https://doi.org/10.13140/RG.2.2.36491.23846 14. Gabriel J (2016) Artificial intelligence: artificial intelligence for humans. Create Space Independent Publishing Platform, USA 15. Heilweil R (2020) How AI is battling the coronavirus outbreak. https://www.vox.com/recode/ 2020/1/28/21110902/artificial-intelligence-aicoronavirus-wuhan. Accessed 23 July 2020 16. Ramesh AN, KambhampatiC MJRT, Drew PJ (2004) Artificial intelligence in medicine. Ann R Coll Surg Engl 86:334–338 17. Yardimci A (2007) A survey on use of soft computing methods in medicine. International conference on artificial neural networks. Artificial Neural Networks, pp 69–79 18. Hu Z, Ge Q, Jin L, Xiong M (2020) Artificial intelligence forecasting of COVID-19 in China. arXiv preprint arXiv:2002.07112 19. Ting DSW, Carin L, Dzau V, Wong TY (2020) Digital technology and COVID-19. Nat Med 27:1e3 20. Dananjayan S (2020) Artificial intelligence during a pandemic: the COVID-19 example. Int J Health Planning Manage. https://doi.org/10.1002/hpm.2987. Accessed 23 July 2020 21. Arora K, Bist AS, Chaurasia S, Prakash R (2020) Analysis of deep learning techniques for covid-19 detection. Int J Sci Res Eng man 4(4):1–5 22. Arora K, Bist AS (2020) Artificial intelligence based drug discovery techniques for COVID-19 detection. Apt Trans Techno 2(2):120–126 23. Tarnok A (2020) Machine learning, COVID-19 (2019-nCoV), and multi-OMICS, cytometry. J Qua Cell Sci Part A 97A: 215–216 24. Shin HS, Kim Y, Kim G, Lee JY, Jeong I, Joh JS, Kim H, Chang E, Sim SY, Park JS (2019) Immune responses to middle east respiratory syndrome coronavirus during the acute and convalescent phases of human infection. Clin Infect Dis 68(6):984–992 25. Yang Z, Zeng Z, Wang K, Wong SS, Liang W, Zanin M, Liu P, Cao X, Gao Z, Mai Z, Liang J, Liu X, Li S, Li Y, Ye F, Guan W, Yang Y, Li F, Luo S, Xie Y, Liu B, Wang Z, Zhang S, Wang Y, Zhong N, He J (2020) Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. J Thorac Dis 12(3):165–174 26. Yang Y, Qingbin L, Mingjin L, Yixing W, Anran Z, Neda J, Natalie D, Ira L, Halloran M E, Bo X, Xiaoai Z, Liping W, Wei L, Liqun F (2020) Epidemiological and clinical features of the 2019 novel coronavirus outbreak in China 27. Yang GZ, Nelson BJ, Murphy RR, Choset H, Christensen H, Collins SH, Dario P, Goldberg K, Ikuta K, Jacobstein N, Kragic D, Taylor RH, Mcnutt M (2020) Combating COVID-19: the role of robotics in managing public health and infectious diseases. Sci Robot 5: eabb5589 28. Das K, Paital B (2020) The synergy between philosophy and science, need of the contemporary society. Int J Hum Soc Sci Res 6(1):45–51 29. Alimadadi A, Aryal S, Manandhar I, Monroe PB, Joe B, Cheng X (2020) Artificial intelligence and machine learning to fight COVID-19. Physiol Genom 52:200–202 30. Schmitt M. (2020) How to fight COVID-19 with machine learning. Towards Data Science. https://towardsdatascience.com/fight-COVID-19-with-machine-learning-1d1106192d84. Accessed 23 July 2020 31. Luo H, Tang QL, Shang YX, Liang SB, Yang M, Robinson N, Liu JP (2020) Can Chinese medicine be used for prevention of coronavirus disease 2019 (COVID-19)? A review of historical classics, research evidence and current prevention programs. Chin J Int Med. https:// doi.org/10.1007/s11655-020-3192-6 32. Kricka LJ, Polevikov S, Park JY, Fortina P, Bernardini S, Satchkov D, Kolesov V, Grishkov M (2020) Artificial intelligence-powered search tools and resources in the fight against COVID-19. J Int Fed CliChemi & Lab Med 31(2):106–116

16

The Significance of Super Intelligence of Artificial Intelligence …

379

33. Marr B (2020) Robots and drones are now used to fight COVID-19. Forbes Enterprise Tech 34. Allam Z, Dey G, Jones DS (2020) Artificial Intelligence (AI) provided early detection of the coronavirus (COVID-19) in China and will influence future urban health policy internationally. AI 1:156–165 35. Bowles J (2020) How Canadian AI start-up blue dot spotted coronavirus before anyone else had a clue. https://diginomica.com/how-canadian-ai-start-bluedot-spotted-coronavirusanyone-else-had-clue. Accessed 23 July 2020 36. Blue Dot (2020a) Blue dot protects people around the world from infectious diseases with human and artificial intelligence. https://bluedot.global/about/. Accessed 23 July 2020 37. Blue Dot (2020b) Better public health surveillance for infectious diseases. https://bluedot. global/products/explorer/. Accessed 23 July 2020 38. Dinh CN, Ding M, Pathirana NP, Seneviratne A et al (2020) Blockchain and AI-based solutions to combat coronavirus (COVID-19)-like epidemics: a survey. https://doi.org/10. 20944/preprints202004.0325.v1 39. Allen K (2016) How a Toronto company used big data to predict the spread of Zika. https:// www.thestar.com/news/insight/2016/02/22/how-a-toronto-company-used-big-data-topredict. Accessed 23 July 2020 40. Vaishnavi P, Agniswar J, Padmanathan K, Umasankar S, Preethika T, Annapoorani S, Subash M, Aruloli K (2020) Artificial intelligence and drones to combat COVID-19. https:// doi.org/10.20944/preprints202006.0027.v1 41. Voleti R (2020) AI-A shield Against COVID-19. Int J Res App Sci & Eng Tech 8: VII. www. ijraset.com. https://doi.org/10.22214/ijraset.2020.7004 42. OECD (2020) Using artificial intelligence to help combat COVID-19, https://www.oecd.org/ coronavirus/policy-responses/using-artificial-intelligence-to-help-combat-COVID-19ae4c5c21/. Accessed 23 July 2020 43. Lau AYS, Staccini P (2019) Section editors for the IMIA yearbook section on artificial intelligence in health. Artificial intelligence in health: new opportunities, challenges, and practical implications. Yearb Med Inform 28(1):174–178 44. Das K (2020) The mechanism of golden mean and middle path: the pursuit of happiness. Int J Multidisc Educ Res 9, 3(4):138–148 45. Buckley C, May T (2020) Ejects of coronavirus begin echoing far from Wuhan epicenter. https://www.nytimes.com/2020/01/25/world/asia/china-wuhan-coronavirus.html. Accessed 23 July 2020 46. Paital B (2020) Nurture to nature via COVID-19, a self-regenerating environmental strategy of environment in global context. Sci Tot Environ 729:139088. https://doi.org/10.1016/j. scitotenv.2020.139088 47. Alabool H, Alarabiat D, Abualigah L, Habib M, Khasawneh A, Alashinwan M, Shehab M (2020) Artificial intelligence technique for containment COVID-19 pandemic: a systematic review. Res Squ. https://doi.org/10.21203/rs-30432/v1 48. Imran M, (2020) Adoption of artificial intelligence in a combat with COVID-19. Alo Cha J IX (VI):3614–3620 49. Jiang F, Jiang Y, Zhi H (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neuro l2:230–243 50. Hamet P, Tremblay J (2017) Artificial intelligence in medicine. Metabolism 69:S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011 51. Chamola V, Hassija V, Gupta V, Guizani M et al (2020) A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2992341.Accessed23July2020 52. Llyas M, Rehman H, Nait A (2020) Detection of COVID-19 from chest X-ray images using artificial intelligence: an early review. arXiv:2004.05436v1. Accessed 23 July 2020 53. Heaven WD (2020) AI could help with the next pandemic- but not with this one. https://www. technologyreview.com/615351/ai-could-help-with-the-next-pandemicbut-not-withthis. Accessed 23 July 2020

380

K. Das et al.

54. Daga M, Kumar N, Arthi J, Mawari G, Garg S, Rohatgi I (2020) From SARS-CoV to coronavirus disease 2019 (COVID-19)—a brief review. J Adv Res Med 6(4):1–9 55. Dhama K, Sharun K, Tiwari R, Sircar S, Bhat S, Malik YS, Singh KP, Chaicumpa W, Aldana D, Morales A (2020) Coronavirus disease 2019—COVID-19. https://doi.org/10. 20944/preprints202003.0001.v2 56. Ramalingam B, Yin J, Elara MR, Tamilselvam YK, Rayguru MM, Muthugala M, Gomez B (2020) A human support robot for the cleaning and maintenance of door handles using a deep-learning framework. Sensors 20:2–18 57. Assadi M, Gholamrezanezhad A, Jokar N, Keshavarz M, Picchio M, Seregni E, Bombardieri E, Chiti A (2020) Key elements of preparedness for pandemic coronavirus disease 2019 (COVID-19) in nuclear medicine units. Euro J Nuc Med & MolIma 47:1779–1786 58. Mahomed S (2020) COVID-19: The role of artificial intelligence in empowering the healthcare sector and enhancing social distancing measures during a pandemic. Issues Med. https://doi.org/10.7196/SAMJ.2020.v110i7.14841 59. Sahu KK, Mishra AK, Lal A (2020) COVID-19: update on epidemiology, disease spread and management. Mon Arch Chest Disease 90:1292 60. Wallis C (2019) How artificial intelligence will change medicine. Nature 576:S48–S48 61. Paital B, Das K, Parida KS et al (2020) International social lockdown versus medical care against COVID 19, a mild environmental insight with special reference to India. Sci Tot Environ 728:138914. https://doi.org/10.1016/j.scitotenv.2020.138914 62. Dule C, Rajasekharaiah KM, Manjaiah DH et al (2020) Challenges of artificial intelligence to combat COVID-19. Int Conference Inn Com Com. https://ssrn.com/abstract=3608764 63. Singhal N, Prakash S (2020) A fight against COVID-19: major it trends. https://ssrn.com/ abstract=3601504 64. Kurde S, Deshpande P (2020) COVID-19 pandemic: survey on technology for detection of suspect and prevention from CORONA virus. Int J Adv Resin Sci Tech 2(2):13–18 65. Das K, Paital B (2020) First week of social lockdown versus medical care against COVID-19 —with special reference to India. Cur Trend Bio Phar 14(2):196–216. https://doi.org/10.5530/ ctbp.2020.2.20 66. Devi K (2020) Corona pandemic: a lesson for preparedness in future. Tathapi 19(34) 67. Rewar S (2015) How treatment and prevention of middle east respiratory syndrome coronavirus (MERS-CoV). Wor J Pharm Res 4(5):2691–2702 68. Singh J, Singh J (2020) COVID-19 and its impact on society. Elect Res J Soc Sci & Hum 2 (1):168–172 69. Javed B, Sarwer A, Soto EB, Mashwani Z (2020) Impact of SARS-CoV-2 (coronavirus) pandemic on public mental health. Front Pub Health. https://doi.org/10.3389/fpubh.2020. 00292 70. Wang G, Zhang Y, Zhao J, Zhang J, Jiang F (2020) Mitigate the effects of home confinement on children during the COVID-19 outbreak. www.thelancet.com. vol: 395 71. Erwin JO, Kompanje (2020) Ethics in the time of Corona. https://www.ersnet.org/COVID-19blog/ethics-in-the-time-of-corona. Accessed 23 July 2020 72. Angela SP (2020) The moral obligation of nurse leaders: COVID-19. Elsevier Inc. https:// www.nurseleader.com/article/S1541-4612(20)30098-7/pdf 73. Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, Bernheim A, Siegel E (2020) Rapid AI development cycle for the Coronavirus (COVID-19) pandemic: initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv:2003.05037 74. Pirouz B, Shaffiee HS, Piro P (2020) Investigating a serious challenge in the sustainable development process: analysis of confirmed cases of COVID-19 (new type of coronavirus) through a binary classification using artificial intelligence and regression analysis. Sustainability 12(6):2427 75. Ting DS, Carin L, Dzau V, Wong TY (2020) Digital technology and COVID-19. Nat Med 26 (4):459–461. https://doi.org/10.1038/s41591-020-0824-5

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The Significance of Super Intelligence of Artificial Intelligence …

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76. Paital B, Agrawal, PK (2020) Air pollution by NO2 and PM2.5 explains COVID-19 infection severity by overexpression of angiotensin-converting enzyme 2 in respiratory cells: a review. Environ Chem Lett 1–18. https://doi.org/10.1007/s10311-020-01091-w 77. Paital B, Das K, Behera TR (2020) Social lockdown and ecological intervention for the prevention of the community spread of COVID 19. Cancer Res Stat Treat. https://doi.org/10. 4103/CRST.CRST_222_20 78. Mousazadeh M, Paital B, Naghdali Z, Mortezania Z, Hashemi M, Niaragh EK, Aghababaei M, Ghorbankhani M, Lichtfouse E, Sillanpää M, Hashim KS, Emamjomeh MM (2021) Positive environmental efects of the coronavirus 2020 episode: a review. Environ Develop Sustain. https://doi.org/10.1007/s10668-021-01240-3 79. Das K, Pingali MS, Paital B, Panda F, Pati SG, Singh A, Varadwaj PK, Samanta SK (2021) A detailed review of the outbreak of COVID-19. Front Biosci (Landmark Ed). 6:149–170. https://doi.org/10.52586/4931 80. Paital B, Das K (2021) Spikes in pollution to ignite the bursting of COVID-19 second wave is more dangerous than spike of SAR-CoV-2 under environmental ignorance in long term: a review. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-12601-w

Kabita Das currently working as Assistant Professor at the Department of Philosophy, Utkal University, India, after completing an M.Phil and Ph.D. in Philosophy from the Center for Philosophy, Jawaharlal Nehru University, New Delhi. She has published more than 20 research and review articles on virtue ethics, interdisciplinary sciences, etc., in many national and international Journals. Applying ethics and philosophy in a practical sense is her primary focus area. So Dr. Das is now trying to develop a strong interdisciplinary background to adopt Scientific methods and science to develop the quality of experimental philosophy. She focuses on using artificial intelligence in the current state of the COVID-19 outbreak. Using AI approaches in communities and hospitals under lockdowns is the main focus of this article. Her main logic in the present article is that continuing treatment for COVID and non-COVID patients is possible via AI with maintaining social distancing. Biswaranjan Paital currently working as Assistant Professor in Zoology, Odisha University of Agriculture and Technology, India, after finishing M.Sc. M.Phil. and Ph.D. in Zoology from Utkal University, India, Post-doctoral works from Banaras Hindu University, University of Technology MARA, and Indian Institutes of Sciences. His works focus on translating environment upheaval-induced stress to the field level to exploit organisms of economic importance. He has published more than 80 research and review articles in many international journals and acts as a reviewer and editorial board member of >60 Journals. Along with medicos and social scientists, he used artificial intelligence in community outreach programs during the COVID-19 outbreak. He has published many useful articles on the socio-environmental aspects of COVID-19. The current article focused on artificial intelligence in medical care, education, and social sectors during the COVID-19 outbreak.

Integration of Sex and Gender Approaches in National Ethics Committees’ Mandate to Appraise COVID-19 Research Protocols: Lessons from West Africa

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Guillermo Z. Martínez-Pérez

There’s no gender-neutral pandemic, and this one is no different. Phumzile Mlambo-Ngcuka

Summary

COVID-19 has made a substantial social, economic, and public health impact in Africa. Its impact may be higher on populations targeted by discrimination based on their sex, gender, class, ethnic group, skin color, sexual orientation, or political and religious practices. However, this is not the first epidemic that the African continent has suffered this century. Lessons from the 2014–2016 Ebola epidemic in West Africa could shed some light on integrating sex and gender-based approaches to design study protocols. This chapter proposes a ‘Framework for the ethical evaluation of COVID-19 research protocols under a sex and gender lens.’ The Framework has been developed in the frame provided by a project by the University of Zaragoza and the Senegalese Ministry of Health and Social Action to improve West African National Research Committees’ capacities and skills to mainstream a gender perspective in their research evaluation mandate. This Framework consists of a 30-item tool to help National Research Ethics Committees and Institutional Review Board members guide their ethics appraisal of COVID-19 research protocols. We expect that its use

G. Z. Martínez-Pérez (&) Department of Physiatrics and Nursing, University of Zaragoza, Facultad de Ciencias de La Salud. Calle Domingo Miral S/N, Despacho 18, 50009 Zaragoza, Spain e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_17

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leads to COVID-19 research that advances towards achieving gender equality and, ultimately, ending sex and gender-based discrimination in all research conducted in the West African region. Graphical Abstract/Art Performance

Discrimination in medical research. (Adapted with permission from the Association of Science and Art (ASA), Universal Scientific Education and Research Network (USERN); Made by Nastaran-Sadat Hosseini).

The code of this chapter is 01101111 01100101 01000011 01100101 01110011 01101001 01101101 01110100 01110100 01101101. Keywords

COVID-19 Africa

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Ethics
Gender
Research protocol
Sex and gender
West

Introduction

The COVID-19 is, after SARS, MERS, and Ebola, the last health emergency that the world faces in this twenty-first century. The first COVID-19 cases were reported in Wuhan, China, in December 2019 [1]. Only two months later, most West African countries had notified cases of the disease in their territories [2, 3]. Contrary to

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previous health emergencies, the COVID-19–albeit not as lethal as Ebola–will be remembered as the disease that drove the lockdown of entire societies and which led to a social, economic, and public health crisis never experienced in history. A wide range of biomedical, clinical, and socio-epidemiological COVID-19 research has been conducted. There has been significant progress in knowledge on the pathogenesis, transmissibility, and therapeutics for infection with SARS-CoV-2, the coronavirus causing the COVID-19 [4, 5]. In mid-December 2020, the United Kingdom became the first country authorizing a COVID-19 vaccine [6]. Major cultural and societal changes are emerging, though, at a different pace. Future social science research is necessary to clarify the socioeconomic and psychological impact of the pandemic in populations that are a target of discrimination based on their sex, gender, class, ethnic group, skin color, or religious practices. The year 2020 may also be remembered, in scholarly environments, due to the number of grants, consultancies, scholarships, and all sorts of financial efforts to accelerate research on a pandemic scenario. During the 2014–16 Ebola epidemics that especially hit Guinea, Liberia, and Sierra Leone, a few Ebola cases were reported outside West Africa. Nevertheless, despite its pandemic potential, Ebola did not attract as much interest in vaccine development as the COVID-19. For instance, in September 2020, over 120 COVID-19 vaccine clinical trials are being conducted, with at least nine vaccine clinical trials already in phase III and with countries engaged worldwide [7]. Countries from West Africa are not behind this research agenda. In this scenario, members of the National Research Ethics Committees (NRECs) invest a significant amount of time and effort to keep up with global demands to discover innovative approaches to tackle the COVID-19 pandemic. NRECs have the mandate to oversee that research with human participants complies with international and local ethical guidelines [8]. NRECs are responsible for vaccine, drugs, and health products trial protocol review. NRECs revise, follow up and monitor clinical trials in cooperation with National Regulatory Authorities (NRAs). NRECs and NRAs, in their infancy in some West African countries at the start of the millennia, substantially developed their capacities during the 2014–16 Ebola epidemics. During the Ebola crisis, a paradigm emerged in ethics and regulatory bodies governance to support that NRECs and NRAs in the region must work together to sum efforts to halt epidemics’ spread and impact. This paradigm engaged multiple projects, training programs, and networks to build the capacities of NRECs and NRAs in clinical trial review and the harmonization of review procedures across all of West Africa and beyond [9]. In compliance with health research ethics principles highlighted in cornerstone documents such as the Helsinki Declaration and the International Conference for Harmonization’s Good Clinical Practice, the NRECs and other institutional review boards (IRBs) guide their trial protocol review mandate under four major expectations:

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• trial participants would be enrolled autonomously, voluntarily, and fully informed; • their rights to privacy, safety, and confidentiality during their engagement in the trial are respected; • no population group bears disproportionately the burden of participation in a trial; and • they share the benefits of the trial results [10, 11]. In many resource-constrained societies, women and non-binary persons as trial participants are at increased risk of seeing these principles not fully safeguarded. In some contexts, as members of society, they may be less empowered, socially, economically, and health-wise than cisgender men. They may also be affected by regulations passed by their Governments to legally consolidate their unprivileged position compared to the cisgender male part of their populations. As women’s and non-binary’s contribution to and benefit from clinical trials may be jeopardized, NRECs/IRBs must ensure equality in research access and participation. Gender mainstreaming is among the strategies that can help NRECs/IRBs achieve this aim. Gender mainstreaming would involve incorporating a gender perspective into the design, execution, and evaluation of research policies, regulations, projects, and outputs, to promote equality between all persons, irrespective of their sex and gender, and eliminate discrimination [12]. Sex- and gender-based analyses, gender equality plans, gender audits, and gender statistics are among the various frameworks and tools that can help NRECs/IRBs to mainstream gender into their governance and core clinical trial review, follow up and monitoring activities. West African countries are signatories of the Protocol to the African Charter on Human and Peoples’ Rights on the Rights of Women in Africa (i.e., Maputo protocol) and of the 1981 African [Banjul] Charter on Human and Peoples’ Rights [13, 14]. Hence, West African NREC/IRBs are socially and politically well placed to explore the best ways to learn on gender mainstreaming and integrate a gender lens into their regulations and standard operating procedures. To assist in the process of gender mainstreaming at the NRECs/IRBs level, the University of Zaragoza (UNIZAR), in cooperation with the Senegalese Ministry of Health and Social Action (MoHSA), started a European & Developing Countries Clinical Trials Partnership (EDCTP)-funded project in mid-2019 [15]. This project’s main goal, of acronym BCA-WA-ETHICS, is to improve the capacities and skills of West African NRECs to mainstream a gender perspective in their research evaluation and inspection mandate. This chapter describes the results of a transnational participatory process that led to developing a Framework for the ethical evaluation of COVID-19 research protocols under a sex and gender lens (hereafter, the Framework).

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Theoretical and Methodological Approaches to the ‘Framework’ Development

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Sex and gender-based approaches (SGA) in health research mainly take place in Northern countries. Different theoretical frameworks underpin different SGA. The handbook ‘Better Science with Sex and Gender’ by the Canadian Women’s Health Research Network (WHRN) was among the pioneer documents emphasizing the added value of SGA [16, 17]. Soon after its publication, in 2009, the Government of Canada issued the ‘Health Portfolio Sex and Gender-based Analysis Policy,’ a policy to use SGA to design, execute and evaluate the Canadian health portfolio’s research, legislation, and healthcare provision programs [18]. Other institutions and countries followed Canada and developed their policies and procedures. In the 10s, the United States Food and Drug Administration (FDA) issued several documents, of which the most relevant might be the ‘Evaluation of Sex-Specific Data in Medical Device Clinical Studies,’ to guide sex-specific patient recruitment, data analysis, and communication of study information for medical technologies applications [19]. In Europe, the European Institute for Gender Equality (EIGE), established by the European Union in 2006, published the ‘Gender Equality in Academia and Research (GEAR)’ tool in 2016 [12]. Inspired by gender mainstreaming, the GEAR tool, combined with other tools, such as gender budgeting, gender audit, gender statistics, and gender equality plans, may provide meaningful guidance for NRECs/IRBs to incorporate SGA. In the context of BCA-WA-ETHICS, in addition to the methodological packages provided by the WHRN and the EIGE, the promotion of the incorporation of SGA by the West African NRECs was also imbued with a feminist perspective. Nevertheless, as there are multiple feminisms—some of them in strong opposition among themselves—the project did not advocate for any single feminist theory in particular. Besides, some feminist scholars engaged in developing SGA in health research design and conduct are Northern hemisphere-based. Their work may lack considerations of race, oppression, extreme poverty, and colonial history that are of interest for many Southern hemisphere-based feminists. Simply put, while not defending any specific feminism, BCA-WA-ETHICS reminded the NRECs members that participated in its activities of the big tenets that are inevitably located across most feminist movements. Among them, the recognition that women and gender minorities have been historically underprivileged; that, when working in male-dominated worlds, they must defend double what they do (and, oftentimes, to obtain half of what men obtain for the same work); that equality is the goal of all feminisms; and that feminism benefits all, men included, as it helps to advance towards socioeconomic, cultural, and emotional development for all. All feminisms identify patriarchate as a set of norms created and maintained by men and by some women, which primarily benefit men (at large) and the women who endorse them [20]. Within BCA-WA-ETHICS, these basic tenets permeated the process of the design of the Framework.

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A third influence in promoting SGA across West African NRECs was the contributions to socio-epidemiological research by West African researchers during the 2014–16 Ebola epidemics. Drawing from lessons from the field and findings from the body of Ebola literature, BCA-WA-ETHICS emphasized on; • counting on the scientists and ethicists communities in a transnational, multidisciplinary, and participatory manner; • understanding local contexts and their past and present history, to better assess the effects of health research in vulnerable populations; • stopping to undermine the role that rumors on epidemic-prone communities may play in clinical trial conduct; and • pushing for research agendas that are respectful to expressed needs by local communities and honest in their benefit-sharing plans, especially regarding issues such as third uses of trial participants’ samples, specimens, and data beyond the end of their engagement in the trial. In summary, these theoretical frameworks and methodological packages informed the design of a process to promote SGA integration in West African NRECs mandate to appraise COVID-19 research protocols during 2020. The process was led by UNIZAR and by the MoHSA. The MoHSA is the government body that hosts the Senegalese NREC (i.e., the CNERS). Before the first COVID-19 cases, the process started with a ‘landscape’ of gender issues in health research in West Africa [21]. The findings of this ‘landscape’ informed the materials of a ‘Workshop’ in Gender Mainstreaming in Dakar in January 2020. Over 20 scientists collaborating with the CNERS attended. Four of the ‘Workshop’ trainees participated in an ‘internship’ at the Aragon Research Ethics Committee (Zaragoza, Spain), where they learned about the actual application of SGA in clinical trial protocol review. All ‘Workshop’ trainees were invited to partake as speakers, together with guest ethicists from West African countries other than Senegal, in the first International ‘Congress’ on Gender Mainstreaming: Health Research in West Africa was held online in March 2020. The majority of ‘Workshop’ trainees and ‘Congress’ speakers joined a project-specific ‘virtual community of practice’ in which they received news, resources, and literature on SGA. In parallel, a UNIZAR-based ‘Virtual Gender Mainstreaming Secretariat’ (VGMS) was dedicated to specifically providing support to the CNERS and other West African NRECs in integrating SGA in their dossiers of regulations and procedures. Guided by the VGMS, in March 2020, a steering ‘committee’ was created. This ‘committee,’ composed of a dozen gender and ethics in research experts from the sub-region, was entirely dedicated to the development of a policy brief titled ‘A Framework for the Ethical Evaluation of Research Protocols from a Sex and Gender Perspective during the COVID-19 Pandemic and Other Epidemics’ (i.e., the framework).

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The RESULT of the PROCESS: The Framework

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Many NRECs/IRBs, as a routine practice, share investigators’ research proposals with their members at least one week before the review meeting. All members should have sufficient time to read all proposals included in the next meeting agenda independently and reflect on their adequacy to ethical principles and compliance with local regulations and clinical trials with good clinical practice [11]. At the review meeting, all members share their appraisals’ results, discuss ethics and legal issues, and decide on each reviewed proposal. The Framework, a 30-item tool to help NREC/IRB members guide their ethics appraisal of COVID-19 trial proposals, may be used either by all members independently before the review meeting, only by an NREC/IRB-designated gender expert, or by all members during the review meeting. The Framework, which can be used for any infectious diseases research protocol—be them clinical trial proposals or otherwise—needs to be applied in three steps. Reflection and group discussion are encouraged after each step concludes.

3.1 1st Step The background, rationale or justification, and aims and objectives sections of a trial protocol must be evaluated to ensure that the investigators have considered sex and gender when introducing the study topic and justifying the need to carry out their research (Table 1).

Table 1 Step 1: evaluation of background and rationale of the research 1 2 3 4

5 6 7 8

Step 1

Compliance

Does the protocol explain if sex and gender are relevant to the study? Are the terms “sex” and “gender” properly used, and without any conflation or confusion between them? Are the knowledge gaps of sex- and gender-specific information on the study's subject included in the protocol? Is the knowledge of the roles, responsibilities, and vulnerabilities of women, men, and non-binary concerning the study's topic and the sociocultural, political, and legal context of the study site included in the protocol? Was the participatory engagement of women, men, and non-binary people in the trial design and planning phases mentioned? Are sex and gender included in the trial hypothesis? Are sex and gender taken into account in the trial objectives? Is the inclusion of a single-sex or gender justified?

Yes No Yes No Yes No Yes No

Yes No Yes No Yes No Yes No

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This step includes consideration of the following aspects: (1) An explanation of sex and gender as relevant to the study is given. However, sex and gender are not always relevant. For instance, it is difficult to consider gender as a variable of interest during pre-clinical vaccine trials performed in cells or animals; (2) The terms sex and gender are not being used interchangeably. When investigators inappropriately use them, that may reflect their lack of expertise in SGA or their lack of attention to sex and gender as distinct biological and social determinants of health. For instance, investigators should refer to the sex - not to the gender—of any animal used in pre-clinical COVID-19 vaccine trial stages; (3) Current knowledge of existing sex- and gender-specific information on the study subject is described in the Introduction section of the protocol. For instance, NRECs/IRBs members could assess if investigators consider and refer to sex- and gender-specific scientific evidence from studies on previous epidemics caused by coronaviruses (e.g., SARS-CoV-1). In the absence of evidence, investigators should explain knowledge gaps in the scientific literature (i.e., phase IV or post-marketing trials would aim to understand the effectiveness of COVID-19 vaccines that regulatory authorities have approved; thus, their protocols should acknowledge potential sex-differential effects such as different levels of antibody response, and gender-differential effects, such as varying levels of vaccine hesitancy as a consequence of gender values that may make some families prioritize boys over girls—or vice versa—for vaccination); (4) The roles, responsibilities, and vulnerabilities of women, non-binary, and men concerning the study topic and the sociocultural, political, and legal context of the trial site are mapped. For instance, gender norms dictating how women should behave may prevent them from being attended by a male investigator in charge of the vaccination process and thus, hinder their participation in the trial; (5) Women, non-binary, and men are engaged in a participatory way in the trial design and planning phases. For instance, investigators proposing Phase IV COVID-19 vaccine trials in West Africa may create data and safety monitoring boards inclusive of all genders and before the trial protocol approval by an NREC/NRA to involve local community representatives and community leaders in assessing how the trial may differently affect women, non-binary and men; (6) Sex and gender are considered in the trial hypothesis. For instance, investigators are not expected to assume that any COVID-19 vaccine will be safe and effective in the same dosage for females or males, or their side effects acceptable for women, non-binary, and men in equal degree; (7) Sex and gender are considered in the trial objectives. For instance, investigators aiming at finding the safest and most effective COVID-19 vaccine for West African populations may specifically state in their study objectives that

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they will assess the feasibility, cultural adequacy, and acceptability of a vaccine prototype among women, non-binary, and men from the community; and (8) In studies that include only one sex as study participants, the researchers justify why other sexes are excluded. For instance, a trial to assess the safety and effectiveness of different dosages of an authorized COVID-19 vaccine in pregnancy may be justified, provided there is data from pre-marketing trials on the effectiveness of this vaccine in non-pregnant people.

3.2 2nd Step The methods section of trial protocols must be evaluated to ensure that trials are designed to allow SGA to happen. The sex and gender aspects identified in the background are considered, along with the aim and objectives sections of the protocols (Table 2). Table 2 Step 2: evaluation of methodology of the research 9 10 11

12 13 14

15

16 17 18 19

20

Step 2

Compliance

Are women, non-binary, and men represented in the research team? Is the research team gender-sensitive, where at least one member had received training in sex- and gender-based analysis? Do the inclusion criteria impede women, men, or gender minorities’ participation due to gender barriers even when they fulfill the requirements? Does the inclusion criteria exclude people because of their sex, sexual orientation, gender expression, or identity? Is the sample representative of females and males of all ages, all gender identities, and all socioeconomic classes? Are sampling procedures sensitive to existing gender-related vulnerabilities and characteristics, for example, recruitment materials, community mobilization, training of recruiters, etc.? Are local gender norms considered in the informed consent documents, for example, power relations, decision-making actors, social distribution of tasks, etc.? Do data collection tools capture variables that allow sex- and gender-based analysis? Is detailed sex- and gender-based analysis that considers intersectionality proposed? Is a detailed plan to present results in a sex- and gender-stratified manner in all research outputs proposed? Is gender-sensitive budgeting (e.g., there are no gender-related wage inequalities between field agents who have the same responsibilities) mentioned and explained? Is the scientific dissemination and communication of the trial results at the community level aimed at social transformation and the achievement of health equity by reducing gender inequalities planned?

Yes No Yes No Yes No

Yes No Yes No Yes No

Yes No

Yes No Yes No Yes No Yes No

Yes No

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This evaluation includes consideration of the following aspects: (9) Women, non-binary, and men are represented in the research team. For instance, trial sponsors detail the composition of their trial teams and demonstrate that there is a balance in representation and allocation of tasks and responsibilities among women, non-binary and men; (10) The research teams are gender-sensitive. For instance, when describing their teams, sponsors may state if trial investigators or any other team member has received training on SGA, if any is a gender expert, or if any has been appointed as the focal for gender issues alongside the trial conduct; (11) The inclusion criteria do not impede women’s, non-binary’s, or men’s participation due to gender barriers when they fulfill participation requirements. For instance, community-based large-scale post-marketing COVID-19 vaccine trials must be careful not to schedule recruitment or clinical follow-up visits to rural communities in tight schedules when, for instance, men might be attending the cattle, or when women might be farming or attending their daily household chores; (12) The inclusion criteria do not exclude people due to their sex, sexual orientation, gender expression, or identity. For instance, sponsors will specifically state that the investigators will train and sensitize trial recruiters to avoid any selection bias derived from sex- and gender-based type of discrimination; (13) The sample represents females and males of all ages, all gender identities, and all socioeconomic classes. For instance, proposals include a strategy to ensure the principle of maximum variation sampling is achieved via setting quotas on the minimum of women, non-binary and men by age and education that should be invited to participate in a COVID-19 vaccine trial; (14) Sampling procedures are sensitive to existing gender-related vulnerabilities and characteristics. For instance, the investigators may explain in their community mobilization strategy how they will handle those gender norms that may impede women from demanding enrolment in a vaccine trial as participants in the absence of their partner’s or parents’ permission; (15) Local gender norms are considered in the informed consent process. For instance, the investigators express how they will consider power relations between females and males and how they will ensure that trial recruitment materials, information sheets, and consent form documents reflect this knowledge on local gender norms; (16) Data collection tools include variables that will allow sex and gender-based analysis. For instance, investigators may decide to assess intentions to receive a COVID-19 vaccine as a secondary objective through household-based surveys and to include a masculinity/femininity index as a sub-scale integrated into the case reporting forms [21]; (17) Detailed sex and gender-based analysis that considers intersectionalities is proposed. For instance, investigators may explain how, in a household-based survey of intentions to administer an authorized

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COVID-19 vaccine to children, they plan to run bivariate and multivariate analyses to assess how sex and gender interact with other social determinants of health in influencing individuals’ and household heads’ attitudes towards a COVID-19 vaccine; (18) A detailed plan to present results in a sex and gender-stratified manner in all research outputs is proposed. For instance, in a phase IV trial of an authorized COVID-19 vaccine, the results will be presented in a sex and gender-disaggregated manner for health professionals administering the vaccine in clinical settings to understand if there are sex and gender differences in the effectiveness of the vaccine; (19) The research budget is sensitive to existing gender inequalities to prevent reinforcing them. For instance, there are no gender-related wage inequalities between field agents who have the same responsibilities (e.g., male village healthcare workers do not receive higher stipends than traditional female midwives as mobilizers of communities engaged in a COVID-19 vaccine trial); and (20) The scientific dissemination and communication of trial results at the community level must aim at social transformation and health equity by reducing gender inequalities. For instance, the dissemination of the results acknowledges any potential difference in the uptake rates of the COVID-19 vaccine among genders, and it encourages the vaccination of key populations that, due to marginalization issues (e.g., transgender sex workers in some settings), may be deprived of their right to access healthcare services.

3.3 3rd Step The trial proposal’s overall ethical approach and expected impact are evaluated to ensure that the trial will not cause social harm or any health damage to its participants or communities and will not perpetuate existing social and gender inequalities (Table 3). This evaluation includes consideration of the following aspects: (21) Equal benefit-sharing strategies are developed or are proposed by the researchers. For instance, investigators may guarantee that the most vulnerable and exposed populations (i.e., pregnant women, healthcare workers) have prioritized access to the vaccine once it is commercialized; (22) Risk mitigation measures are proposed to tackle existing gender inequalities and intersections and their impact on research participants. For instance, in contexts where male heads of households control the women’s whereabouts, community sensitization in the form of house-to-house visits may be done to explain that the decision to participate in a Phase IV vaccine trial needs to be done solely by women in an autonomous, independent and fully informed manner;

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Table 3 Step 3: evaluation of the ethical impact of the research 21 22

23

24 25

26 27

28 29 30

Step 3

Compliance

Have equal benefit-sharing strategies been developed or proposed by the researchers? Have risk mitigation strategies been proposed considering existing gender inequalities and intersections and their impact on research participants? Have procedures been established to ensure that the reporting of adverse events to the regulatory authorities during clinical trials occurs under equal conditions regardless of participants’ sex or gender? How could privacy and data security breaches impact women and men differently? Has this been taken into account? In clinical trials, are there mechanisms to ensure that existing gender inequalities do not affect equal access to healthcare services for female and male study subjects in confinement and quarantine? Does the clinical trial health insurance coverage benefit any gender group over the other without adequate justification? In trials with a socio-behavioral component, have gender considerations influenced the proposal of mechanisms to guarantee access and use of psychosocial counseling and mental health services, if necessary? Are there measures to ensure that protocol violations do not affect one gender group more than the other? Are women, men, and non-binary research team members engaged in the trial's quality control under equal conditions? Are women, men, and non-binary research team members engaged in research dissemination and intellectual property under equal conditions?

Yes No Yes No

Yes No

Yes No Yes No

Yes No Yes No

Yes No Yes No Yes No

(23) Reporting adverse events to the regulatory authorities during clinical trials occurs under equal conditions regardless of the sex or gender of affected participants. For instance, investigators should consider that females and males may not present the same adverse effects to the same vaccine dosage, and at the same time, they should guarantee a safe environment taking into account local gender norms that may impede women to speak freely; (24) The proposal considers how privacy and data security breaches may impact women, non-binary, and men differently. For instance, unintentional release of sensitive data may have a more significant impact and worse consequences on female participants in vaccine trials than on male participants, especially when data on co-infections (e.g., HIV) and other sensitive aspects is part of the case report forms; (25) The researchers propose mechanisms to ensure that existing gender inequalities do not affect equal access to healthcare services for female and male trial subjects in confinement and quarantine. For instance, access to healthcare services may be harder for women with small children and/or dependants. Including home visits for this population on the protocol may help to avoid this obstacle;

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(26) The clinical trial health insurance coverage, including damage compensation mechanisms, does not disproportionately benefit any gender group over the other without adequate justification. For instance, health insurance policies may acknowledge that risks derived from participation in the study can be different for females and males (e.g., erectile dysfunction, dysmenorrhea, etc.); (27) In trials with a socio-behavioral component, gender considerations influence the proposal of mechanisms to guarantee access and use of psychosocial counseling and mental health services, if necessary. For instance, these trials could explore how gender norms and roles affect the way people grieve their loved ones when they are allowed to attend their funerals or burials amid a pandemic, and how their needs may differ to put into place measures for specific populations; (28) There are measures to ensure that protocol violations do not affect one group more than the other. For instance, quality control in a vaccine trial site includes separate verification of protocol compliance registers for women, non-binary, and men participants; (29) Women, non-binary, and men research team members are engaged in the trial’s quality control under equal conditions. For instance, data collection in case report forms and data analysis will be performed by all genders to avoid potential biases; and (30) Women, men, and non-binary trial team members are engaged in research dissemination and intellectual property under equal conditions. For instance, results should be shared with all the team members. Reports, including peer-reviewed publications, will give all team members appropriate credit, paying special attention to those members belonging to collectives with less visibility.

4

Future Prospects

The use of this Framework by NREC/IRB members may lead to COVID-19 research that makes advances to achieve gender equality and to the end of gender-based discrimination. These are gender mainstreaming goals aligned with ethics bodies’ aim to promote the ethical principles of justice, beneficence, and autonomy in health research. The Framework’s application could improve how the most vulnerable individuals could access, participate in, and benefit from health research and its outputs in an autonomous, voluntary, fully informed, and engaging manner. When ensuring participation in equal conditions, researchers need to be careful not to perpetuate the norms and values that make women and gender minorities lack the same decision-making power and benefit-sharing opportunities that men have. NREC/IRB can assist trial teams in moving from gender insensitive to gender transformative research.

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During the 2014–2016 Ebola crisis, the emphasis was on the following points; • ensuring community participation; • promoting horizontal—rather than vertical and highly hierarchical - approaches to research conduct; • improving an understanding of contexts’ history and societal realities (i.e., gender norms and values) to assess health issues better; • stop undermining the stigma towards epidemic-infected population; and • pushing for research agendas that are respectful to local populations’ needs and plan honest and transparent benefit-sharing, especially regarding issues such as sampling and specimen exportation. The above lessons learned are ingrained in the modus operandi of many West African researchers today. They were inspiring to pursue quality and social justice in health research and were capitalized on in the process of proposing this Framework. The BCA-WA-ETHICS Gender Mainstreaming Secretariat is promoting the integration and utilization of the Framework by West African NRECs. Time will tell if the enterprise will succeed. Commitment from NREC members in West Africa towards gender equality is an opportunity to impede the gender gap in COVID-19 research. The Framework design process has demonstrated that any institutional reforms need to be led by the actors of change. Northern experts can bring their gender expertise and SGA tools. However, they must be mere technical instruments in the hands of national experts. These hold the knowledge on how to capitalize on such expertise and adapt tools to their local environments in a culturally-congruent and sustainable manner. Currently, due to the heavy workload that the COVID-19 pandemic has created to the West African NRECs that collaborate in BCA-WA-ETHICS, intensive advocacy for the use of the Framework has not been feasible. While this is a tool aimed at improving COVID-19 trial review, it has to be acknowledged that it has been created when training, mentoring, and monitoring its use are challenging tasks. Besides, irrespectively of the current working difficulties that many NRECs are facing amid the pandemic, some barriers to its effective implementation were anticipated while developing the Framework. Among them, the high turnover of NREC members in many countries, the sustainability of gender mainstreaming in the NRECs, and local scientists’ acceptability of the theoretical sources underpinning the SGA promoted by Northern institutions, such as the WHRN and the EIGE. Gender and SGA experts emphasize analyzing the intersectionality between sex and gender and class, education, ethnicity, religion, or sexual orientation in non-male research participants. Based on our programmatic experience, no West African scientist in health seems to oppose the added value of assessing intersectionalities’ effect across different gender groups. However, there are three additional social determinants of well-being that should not be disregarded in West African-led COVID-19 research. Namely, exposure to and experiences of colonialism, poverty, and fragile health systems. These three determinants, which rarely

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come to some European researchers’ minds when planning research in Europe, might be crucial to trial participants in the Global South. Firstly, the experience of colonialism is a multifaceted concept. The oldest persons in the West African region may have grown up when—except Liberia— their current States were ruled by the British, French, or the Portuguese. The youngest ones have not lived in such times. Still, their social, cultural, religious, and economic development has largely depended on the ‘heritage’—which includes penal codes, borders, non-vernacular languages, and education systems—of the colonial era. Furthermore, all West Africans of any age are seeing and experiencing more recent economic and religious colonialism. On the one hand, many West African States suffered the effects of the structural readjustment programs imposed on them by the International Monetary Fund and the World Bank [22]. They have seen their local products replaced in their markets by Chinese products against which their local industries cannot compete [23]. Indeed, imported capitalism and free trade agreements affect the daily lives of millions of West Africans. On the other hand, religious colonialism is gaining terrain. Many West Africans today do not practice Black Islam or Christianism as they used to in the early postcolonial period; animism or traditional spiritual and religious practices are being abandoned. North American evangelical congregations are increasing their influence in some of the region’s communities [24]. As a result, many West Africans are reshaping their perception and acceptance of gender-nonconforming individuals and families. In sum, newer forms of colonialism are changing people’s values, including gender norms, and these changes can affect women and gender minorities’ conditions in research. Colonialism is a crucial factor to consider in trial proposal appraisal. Mainly when that proposal is sponsored by a Northerner institution that aims to execute it in a West African setting. Secondly, regarding extreme poverty, its effect as a deterrent to gender equality is a dimension that West African feminists identify as an aspect that Northern feminists, who are perceived to be writing about women in industrialized societies as if their problems were global and globalizable, might not necessarily comprehend [25]. Poverty, for women and gender minorities, is not merely living on less than 1 US$ per day. They could live with twenty times that quantity a day and still suffer the effects of poverty in their experiences as trial participants. Poverty is multidimensional. Its levels are not finite and are dependent on people’s communities and social geographies. West African NRECs mainly consider poverty as a factor that, intersecting with gender, can compromise vulnerable populations’ abilities to decide if and how they participate in research voluntarily. Cheikh Anta Diop described in ‘The cultural unity of Black Africa’ how matriarchy was commonplace in Africa [26]. However, poverty, colonialism, and male-dominated fragile healthcare systems have contributed to the social dominance of patriarchy, as well as the disappearances of niches of power for women. Feminism emphasizes the need to challenge patriarchal values as the root cause of all women’s and gender minorities’ compromised decision-making capacities. For

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gender equality to be achieved in the future, NRECs need to reflect on what, according to Anta Diop, was local and common and accepted in Africa and to discuss how current SGA in research conduct, though gender-sensitive, may be insensitive to issues of poverty and colonialism that are of interest to African participants in research. Finally, poverty also exposes people to fragile healthcare systems that may be under-equipped, under-staffed, and under-budgeted to diagnose efficiently, treat and prevent tropical, poverty-related, and neglected tropical diseases. This exposure, which is commonplace for many Western Africans, is a daily experience that many Northerners are ignorant of. In addition to the internal idiosyncrasy of health systems, issues of stigma and discrimination, violation of patients’ rights to confidentiality, corruption, informal payments, lack of knowledge and skills at the healthcare workers side, among many other characteristics, make exposure to fragile health systems a factor as important as a gender when appraising proposals of COVID-19 trials that are to be executed in the usual healthcare environments where vulnerable populations seek for healthcare services. Despite advocacy for gender equality, there are many other issues that West African scientists know that could affect women and gender minorities as trial participants. As various Israeli researchers reported in a study conducted by this chapter’s author, reflecting on Israeli researchers obviating gender approaches in research with African asylum-seekers in Israel, ‘when there is war, and conflict, and human and civil rights violations, it is difficult to think about gender equality’ [27]. Israel and West African countries differ in many aspects. However, there is one aspect that must be recognized. No matter how much advocacy for gender equality is done for NRECs to consider it among their social justice goals: insofar as societies hosting clinical trials have other pending life-threatening issues to solve, it will be challenging to get ethicists and scientists’ attention to SGA.

5

Conclusion

In conclusion, NRECs in West Africa are encouraged to use an easy-to-follow Framework to appraise COVID-19 proposals from a sex and gender perspective. This Framework, developed in collaboration with West African NRECs who collaborate with the project BCA-WA-ETHICS, draws from lessons learned during the 2014–16 Ebola crisis and follows some of the EIGE GEAR tools fundamentals. Despite meriting further adaptation to local West African realities, it can be a powerful tool in the hands of ethics and regulatory bodies to push for gender transformative research to achieve gender equality for all in a future COVID-19-free world. More rounds of consultations at the NREC/IRB level are necessary, ensuring that the Framework reflects African research review practice nuances. On the one hand, the Framework will need to introduce lessons from regions other than West Africa (i.e., the Democratic Republic of Congo ethics and regulatory bodies also have broad experience in appraising Ebola vaccine trials).

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Finally, its potential for harmonization across countries and integration with other research review frameworks must be assessed. In short, the Framework will need to incorporate further lessons learned from its actual application during the COVID-19 era once the pandemic is controlled and it becomes part of the History of Humanity. Core Messages

• As women’s and non-binary’s contribution to and benefit from clinical trials may be jeopardized, NRECs must ensure equality in research access and participation. • Gender mainstreaming is among the strategies that can help NRECs achieve equality in research access and participation. • Gender mainstreaming integrates a gender perspective into the design, execution, and evaluation of research policies, regulations, projects, and outputs, to promote equality for all, irrespective of people’s sex and gender, and eliminate discrimination. • To improve the capacities and skills of West African NRECs to mainstream a gender perspective in their research evaluation mandate, a ‘Framework for the Ethical Evaluation of Research Protocols from a Sex and Gender Perspective during the COVID-19 Pandemic and Other Epidemics’ is proposed. • A Framework is a 30-item tool developed in the frame of a project led by the University of Zaragoza and the Senegalese Ministry of Health and Social Action to help NREC members guide their ethics appraisal of COVID-19 trial proposals. • NREC members’ Framework may lead to COVID-19 research that advances towards gender equality and the end of gender-based discrimination.

Acknowledgements This publication was produced by BCA-WA-ETHICS, which is part of the EDCTP2 program supported by the European Union (grant number CSA2018ERC-2314). The views and opinions of authors expressed herein do not necessarily state or reflect those of EDCTP.

References 1. Nasiri MJ, Haddadi S, Tahvildari A, Farsi Y, Arbabi M, Hasanzadeh S, Jamshidi P, Murth M, Mirsaeidi M (2020) COVID-19 clinical characteristics, and sex-specific mortality: systematic review and meta-analysis. Front Med 21(7):459. https://doi.org/10.3389/fmed.2020.00459 2. Achoki T, Alam U, Were L, Gebremedhin T, Senkubuge F, Lesego A, Liu S, Wamai R, Kinfu Y (2020) COVID-19 pandemic in the African continent: forecasts of cumulative cases, new infections, and mortality. MedRxiv. https://doi.org/10.1101/2020.04.09.20059154 3. Taboe HB, Salako KV, Tison JM, Ngonghala CN, Glèlè Kakaï R (2020) Predicting COVID-19 spread in the face of control measures in West Africa. Math Biosci 328:108431. https://doi.org/10.1016/j.mbs.2020.108431

400

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4. Chen J (2020) Pathogenicity and transmissibility of 2019-nCoV: A quick overview and comparison with other emerging viruses. Microbes Infect 22(2):69–71. https://doi.org/10. 1016/j.micinf.2020.01.004 5. Dhama K, Sharun K, Tiwari R, Dadar M, Singh Malik Y, Pal Singh K, Chaicumpa W (2020) COVID-19, an emerging coronavirus infection: advances and prospects in designing and developing vaccines, immunotherapeutics, and therapeutics. Hum Vaccin Immunother 16 (6):1232–1238. https://doi.org/10.1080/21645515.2020.173522 6. Mahase E (2020) Covid-19: UK approves Pfizer and BioNTech vaccine with rollout due to start next week. BMJ 2020(371):m4714. https://doi.org/10.1136/bmj.m4714 7. World Health Organization (WHO) (2020) Draft landscape of COVID-19 candidate vaccines. Available via WHO. https://www.who.int/publications/m/item/draft-landscape-of-covid-19candidate-vaccines. Accessed 11 Sep 2020 8. World Health Organization (WHO) (2009) Research ethics committees: basic concepts for capacity-building. WHO Document Production Services, Geneva 9. European & Developing Countries Clinical Trials Partnership (EDCTP) (2019) Towards consolidation of clinical trial regulation, oversight and registration in Africa. PACTA Report. Available via EDCTP. https://edctpprint.maglr.com/pacta-report/cover?getpdf=all. Accessed 11 Sep 2020 10. World Medical Association (2013) Declaration of Helsinki: ethical principles for medical research involving human subjects. World Medical Association, Fortaleza 11. International Conference of Harmonization (ICH) (1996) Tripartite guideline for good clinical practices E6 (R1). ICH, Geneva 12. European Institute for Gender Equality (EIGE) (2016) Gender equality in academia and research. Publications Office of the European Union, Luxembourg, GEAR tool 13. African Union (2003) Protocol to the African Charter on Human and People’s Rights on the Rights of Women in Africa (Maputo Protocol). Available via the United Nations. In: https:// www.un.org/en/africa/osaa/pdf/au/protocol_rights_women_africa_2003.pdf. Accessed 11 Sep 2020 14. African Union (1981) African charter on human and peoples’ rights. Available via the United Nations. https://treaties.un.org/doc/Publication/UNTS/Volume%201520/volume-1520-I26363-English.pdf. Accessed 11 Sep 2020 15. University of Zaragoza (2020) BCA-WA-ETHICS. Available via BCA-WA-ETHICS. http:// www.bcawaethics.com. Accessed 11 Sep 2020 16. Johnson JL, Greaves L, Repta R (2007) Better science with sex and gender: a primer for health research. Women’s Health Research Network, Vancouver 17. Johnson JL, Greaves L, Repta R (2009) Better science with sex and gender: facilitating the use of a sex and gender-based analysis in health research. Int J Equity Health 8:14. https://doi. org/10.1186/1475-9276-8-14 18. Government of Canada (2009) Health portfolio sex and gender-based analysis policy. Available via the Government of Canada. https://www.canada.ca/en/health-canada/corporate/ transparency/corporate-management-reporting/heath-portfolio-sex-gender-based-analysispolicy.html. Accessed 11 Sep 2020 19. Food and Drug Administration (FDA) (2014) Evaluation of sex-specific data in medical device clinical studies. Guidance for industry and Food and Drug Administration staff. Available via FDA. https://www.fda.gov/media/82005/download. Accessed 11 Sep 2020 20. Delap L (2020) Feminisms: a global history. Penguin UK, London 21. Lenney E (1991) Sex roles: the measurement of masculinity, femininity, and androgyny. In: Robinson JP, Shaver PR, Wrightsman LS (eds) Measures of social psychological attitudes, vol 1. Academic Press, London, pp 573–660 22. Van der Geest W (ed) (1994) Negotiating structural adjustment in Africa. United Nations Development Programme, New York 23. Harneit-Sievers A, Marks S, Naidu S (2010) Chinese and African perspectives on China in Africa. Pambazuka Press, Nairobi

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24. Cooper BM (2006) Evangelic Christians in the Muslim Sahel. Indiana University Press, Bloomington 25. Reddy V, Moletsane R (2009) Gender and poverty reduction in its African Feminist practice: an introduction. Agenda: Empowering Women for Gender Equity 3–13. https://doi.org/10. 2307/27868975 26. Anta Diop C (1989) The cultural unity of Black Africa. The domains of matriarchy and of patriarchy in classical antiquity. Karnak House, London 27. Martínez-Pérez GZ, Figueroa-Romero A (2020) Israeli researchers’ gender approaches in health research with Eritrean and Sudanese asylum-seekers (In press)

Guillermo Z. Martínez-Pérez is a social scientist in health currently working as an Assistant Professor at the Department of Physiatrics and Nursing of the University of Zaragoza. He completed a BSc in Nursing and Podiatry, MA History, and a Ph. D. in Health Sciences. He has over 12-year research and international cooperation experience in various sub-Saharan African settings. His main research interests are traditional gendered harmful practices, innovative tools for improving HIV and other stigmatizing diseases care uptake and capacity building for health research in low-resource settings. Since 2017, Dr. Martínez-Pérez has been the President of the African Women’s Research Observatory, a Catalonian association fully dedicated to supporting research led by young African women scientists in health.

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Psychology, Law, Ethics, Telehealth, and the Global Pandemic Gerald Young

I have no idea what’s awaiting me, or what will happen when this all ends. For the moment I know this: there are sick people and they need curing. Albert Camus [1]

Summary

The chapter reviews ethical issues pertinent to tele-assessment in psychological injury and discusses the constraints and limitations involved. It follows an overview of divergent recommendations on the feasibility of conducting such assessments remotely. Psychological injury evaluations are conducted in cases of negligent injury leading to tort lawsuits, worker compensation cases, disability determinations, military veterans’ examinations, malpractice lawsuits, harassment and discrimination cases, sexual assaults, and so on, making them high stakes, forensic, and scrupulously examined in court and related venues. Remote testing in these assessments is fraught with challenges related to conducting quality synchronous, live interviews and supervising online testing by respondents using test company platforms. The challenges in conducting these assessments are reviewed, from the most basic, such as using secure platforms, to the most complex, such as the ethical and legal context for telehealth assessments of this type. The latter include obtaining fully-informed, voluntary consent to participation in the assessment remotely, despite the increased limitations and risks inherent in the remote modality. The chapter makes recommendations on how to proceed when the assessments are mandated or otherwise in the case’s best interests and offers a sample telepsychology informed consent form discussed in detail. The threat to the reliability, validity, and probative value of these assessments for court purposes calls for a cautious attitude in the assessments. The chapter reviews the American Psychological

G. Young (&) Glendon College, York University, Toronto, Canada e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_18

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Association Ethics Code (https://www.apa.org/ethics/code/#:%7E:text=Copy right%20%C2%A9%202,017%20American%20Psychological%20Association. %20All%20rights,five%20General%20Principles%20%28A-E%29%20and% 20specific%20Ethical%20Standards) concerning these assessments and considers the legal ramifications. Conclusions relate to admissibility to the court of psychological tele-assessments. Graphical Abstract/Art Performance

Psychology, law, ethics, telehealth, and the global pandemic (Adapted with permission Association of Science and Art (ASA), Universal Scientific Education and Research (USERN); Made by Reihaneh Khalilianfard). The Nastaliq-written panel includes a Arabic and Persian and also a verse from Hafez, implying that the mind perfection is perfection, and the peace of the world is in ethics perfection

from the Network quote in in ethics

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The code of this chapter is 01110011 01110100 01100011 01101000 01000101 01101001. Keywords





















Admissibility Coronavirus Court COVID-19 Daubert Ethics Ethics code Federal rules of evidence Global pandemic Informed consent Law Psychological injury Psychology Tele-assessment Telehealth Telepsychology




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Introduction

Telehealth is a burgeoning modality of contact in providing health care remotely to underserved areas and is a health provision mode of choice for its convenience. However, it has limitations and risks that need to be understood and accommodated. The global pandemic has led to an exponential increase in its use, and the challenges inherent in telehealth have become magnified by this health crisis. The risks and limitations are even greater for forensic and related telehealth assessments, which is also the case for forensics-related evaluations of psychological injuries. For present purposes, telehealth refers to telepsychology and tele-assessment. Telepsychology offers “telecommunications” toward providing remote health information and psychological care [3]. Tele-assessment can be defined as conducting evaluations securely and confidentially at a distance, or remotely, through the computer, electronic, or telephone technology (e.g., including and not restricted to) virtual platforms and videoconferencing; mobile device/cellular phone/smartphone technology, the regular landline telephone), and in a way that should follow existing professional, governmental, and legal rules and guidelines. The chapter concerns mental health (psychological/psychiatric) tele-assessments but uses the generic term of tele-assessments. Psychological injuries are injuries produced by an event, such as by a motor vehicle accident (MVA). They include chronic pain, mild traumatic brain injury (MTBI), posttraumatic stress disorder (PTSD), and persistent post-concussion syndrome (PPCS) [4–6]. Other examples refer to an adjustment disorder after a sexual assault [7] and the onset of depression comorbidly in all these cases. Psychological injuries are contentious in court because they are subject to exaggeration, feigning, and malingering for external gain, including financial compensation for the alleged damages [8–10]. In general, the COVID-19 pandemic has magnified the importance of telehealth, and more specifically, telepsychology. COVID-19 was declared to be a health crisis in mid-March 2020 [11] (by researching the WHO timeline on March 11, 2020, they declared on their website that COVID-19 could be a pandemic and NOT just a public health crisis), and the impact on society has been enormous and broad, from

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health morbidity and mortality, to economic and social upheavals and downturns, and to negative effects on mental health. The need for telepsychology and telepsychiatry will further increase as the COVID-19 cases keep increasing, the first waves do not abate in countries and jurisdictions, and Omicron manifests in others that had apparently “flattened the curve.” Nearly 38% of Canada’s population has experienced mental health deterioration during the pandemic [12]. Primers have addressed how to conduct telepsychology practice [13]. The research has shown that telepsychology allows for equivalence in efficacy compared to standard face-to-face delivery modalities of psychological services in creating rapport and dealing with common mental health diagnoses, such as anxiety and PTSD [14]. However, there is little research on the comparability of the assessment procedures in the two modes. Indeed, some types of evaluations are not feasible in the remote context, especially forensically. Psychological practitioners are being placed under external pressures to lower the minimum criteria for undertaking valid assessments in mandated arenas, in high stake cases requiring immediate evaluations, and by attorneys and other court-related professionals who need the assessments for their determinations. The same pressures apply to the assessment procedures themselves and the tests used in the procedures. It places an increasing burden on acquiring fully-informed, voluntary consent for participating in remote telepsychological assessments. Of course, practitioners consult evolving and newly appearing professional guidelines and regulatory rules of practice, ethics documents, ethical codes, government regulations, and their own attorneys on their decisions in these regards and the procedures to follow should they decide to undertake the assessments, including for informed consent. The present chapter reviews the recent peer-reviewed publications, guidelines, and case law on telepsychology and tele-assessment, including psychological injury areas in the legal context. It finds some firmly supported conclusions about telepsychology and fundamentals toward its effective practice and found inconsistencies, cautions, limitations, and even recommendations not to use it for certain assessments and certain test instruments. Practitioners themselves have divergent opinions about whether telepsychology can be used in their practices for certain referral questions. Therefore, after the literature review, for psychological injuries, the chapter presents best practices at multiple levels for telepsychology and especially concentrates on informed consent. Can it be fully informed? Can it be voluntary in all cases? Can the assessments be fully secure and confidential? Are the risks in undertaking tele-assessments unsurmountable? Can proprietary tests truly be given equivalence to the standard face-to-face situation, and are their norms usable in this context? In short, are the tests given remotely in a tele-assessment reliable in both the psychological sense (that is, consistent) and the legal sense (that is valid)? Will testimony based on tele-assessments likely be deemed inadmissible to court because of its lack of scientific credibility and its unhelpful contribution to the court process, triers of fact (judges and juries), and their deliberations? Or, will testimony such as this be allowed into court and then face withering cross-examination to reduce its weight in determining the outcome in the case at

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hand? The profession of psychology, and that of all mental health work, including psychiatry, face daunting challenges in these regards, and the present chapter was written to prepare them for the task of undertaking tele-assessments and fare well in court to the degree possible permitted in the circumstance. The following literature review underscores how psychology deals with telepsychology and tele-assessments and some of the difficulties being faced. The areas covered include ethics, practice, flattening the distress, and assessment, with the lack of consistency in recommendations and guidelines for forensics-related practice highlighted.

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Recent Literature Review

2.1 Ethics 2.1.1 According to the APA Telepsychology and tele-assessment in COVID-19 present logistic and ethical challenges to mental health workers, generally. Chenneville and Schwartz-Mette [15] have offered guidance to psychologists concerning remote psychological services according to the American Psychological Association’s ethics code [2]. The authors refer to: i. beneficence and nonmaleficence; ii. fidelity and responsibility; iii. integrity; iv. iv, justice; and v. respect for people’s rights and dignity as guiding principles in this code. For the first principle of beneficence/ nonmaleficence, the transition to telepsychology should be undertaken without violating caring well for patients. For the second, trust should be at the forefront in relating to patients. Next, about integrity, Chenneville and Schwartz-Mette [15, p. 2] raised the central point that psychologists need to acknowledge the knowns versus the unknowns about the pandemic’s effect on mental health. For this integrity principle, the chapter author adds that psychologists have to report what is known and what is not known about telepsychology and tele-assessment both to themselves and their patients in these COVID-19 times. For the principle of justice, Chenneville and Schwartz-Mette indicated that the principle of justice “implores” psychologist practitioners to provide “equitable access” in their service provision. COVID-19 presents unique challenges in this regard because of the inequity in access to the computer and internet technology that disadvantaged groups will have. Finally, for respect/rights/dignity of people’s, Chenneville and Schwartz-Mette indicated that the principle calls for psychologists to specify the “potential limits, risks, and/or implications” of virtual telepsychology and tele-assessment, including by taking extra precautions for personal data protection and privacy in the

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psychological services that they provide. The chapter author maintains that this concern is critical to adjusting to telepsychology and tele-assessment and greatly elaborates this concern in this chapter. As for ethical standards, Chenneville and Schwartz-Mette [15] considered telepsychology from the perspective of the first standard set of APA’s ethics code [2] that is important to the study of ethical issues. An example is when one’s organization insists on using telepsychology when one does not yet have the required competence. However, COVID-19 leads to some slack in these regards because of its emergency status. The next standard of competence relates to the same concern: is one sufficiently prepared to use telepsychology? For human relations, psychologists need to be aware of equal access and not discriminate in telepsychology use. Moreover, for the present work’s focus, Standard 3.10 in the APA ethics code dictates that informed consent forms should be modified to ensure that patients are fully informed of telepsychology’s risks and benefits. Continuing with this theme, the next standard on privacy and confidentiality indicates that the revised informed consent should be “frank” about the limits of confidentiality in remote work, given other members in the locale of the patient could be privy to sessions or the computer could be hacked. The systems set up for videoconferencing must comply with relevant laws (e.g., the U.S. federal Health Insurance Portability and Accountability Act, HIPAA, and state/provincial regulations; [the equivalent federal law in Canada is PIPEDA or the Personal Information Protection and Electronic Documents Act]). Patients can refuse to be recorded; indeed, they could record psychologists without them knowing! Moving to standard 9 on assessment, Chenneville and Schwartz-Mette [15] emphasized the APA stance that assessment conclusions and recommendations should be based on procedures and tests that are deemed reliable and valid (Standards 9.01, 9.02). Therefore, in tele-assessments, do the procedures and tools used meet these psychometric standards? Furthermore, some instruments might not be available online or conducive to maintaining test security. Finally, instrument procedures are standardized on non-virtual populations, and if any adjustments are required for virtual testing, does the test still have acceptable psychometric properties? Chenneville and Schwartz-Mette advised that “creative thinking” might facilitate “good enough” solutions in these regards. However, the present author strongly suggests that test protocols should not deviate in any way that compromises sufficient reliability and validity of the test for court purposes. Later in their paper, Chenneville and Schwartz-Mette [15] maintained that conclusions deriving from tele-assessments that “deviate” from “accepted” standards of practice in the field should be “articulated” in the conclusions [15, p. 9]. As for the last standard on therapy, Chenneville and Schwartz-Mette stated that psychologists should know how to deal with unexpected virtual session interruptions, technology failures, crises, and the like. Psychologists should know how to obtain technological supports for hardware and software issues. Presumably, this standard refers to the psychologist’s own devices and not those of the patient.

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Comment Chenneville and Schwartz-Mette [15] have described the principles and standards of the APA ethics code that stand behind the development of telepsychology and tele-assessment. However, they do not consider the increased risks of this service format in high stakes and forensic cases. Legal bars on admissibility, application of relevant science, and instruments’ reliability and validity cannot be jeopardized or even altered for practical reasons. Practice

2.1.2 Consolidated Model McCord et al. [3] organized five available telepsychology guidelines into a common model covering core practice domains. The authors found differences among them that included “significant” and confusing ones. Therefore, they organized the practice domains according to service modality and setting. For present purposes, the critical telepsychology practice domains relate to assessment, ethics, and law. The psychologist is advised to know evolving remote assessment methods, their limitations, and to maintain standardization protocols. Tests appropriate for online administration should be used, with their psychometric reliability and validity “preserved.” Data protection and the integrity of used instruments are of primary importance. For ethics/law, psychologists are advised to practice telepsychology according to relevant ethics codes and applicable laws, for example, related to jurisdiction, licensure, and, where applicable, business associate agreements. Other notable guidelines include: verifying the identity of clients, that is, excluding imposters; obtaining fully-informed consent (including on possible risks); and ensuring privacy and confidentiality, but these guidelines fail to mention that psychologists should acknowledge the limits therein. Comment As with the telepsychology ethics article by Chenneville and Schwartz-Mette [15], the McCord et al. [3] article does not deal with high-stakes and related forensic assessments in the telehealth modality. There is no discussion of guidelines in the article that indicates when any such assessment should not be undertaken because of compromised reliability and validity for assessment tools and the consequent inability to arrive at defensible conclusions and recommendations for court.

2.2 Psychological Services in COVID-19 Times 2.2.1 Flattening the Distress The pandemic has increased personal and collective anxiety, in particular [16–20]. Although anxiety is the most frequent condition that the pandemic has induced, PTSD should not be overlooked [21]. Health care professionals are suffering shattered social identities and moral injuries [22]. Public trust is affected [23]. Frontline U.S. psychiatry has been disrupted [24]. The APA is advocating for better

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third party-reimbursement policies for tele-psychological services in these times [25]. Psychological psychotherapy services flow from reliable and valid assessments. The virtual environment in telepsychology is not equivalent to the quality, efficacy, reliability, and validity of these assessments generally relative to the standard face-to-face assessment. However, some studies have found an equivalent efficacy (see below). In the emergency context, the general balance of beneficence to maleficence, or benefit to risk, of tele-assessments might lead one to decide to continue with a required individual assessment remotely. However, suppose the particular client cannot be guaranteed the same quality, efficacy, reliability, and validity as would derive from a typical face-to-face assessment. That would do more harm than good to the client, despite the general value of such assessments.

2.2.2 Tele-Assessments This section of the chapter reviews several areas in which tele-assessments are being conducted, especially for forensics-related assessments. The first paper reviewed is on student assessments, but the message applies to the typical forensic one, and for the same reasons cited. Farmer, McGill, Dombrowski, Benson, Smith-Kellen, Lockwood, Powell, Pynn, and Stinnett [26] wrote on conducting virtual psychoeducational assessments. These types of assessments might be mandated, but psychologists should proceed cautiously, in that American national organization guidelines have recommended a delay in these regards (e.g., National Association of School Psychologists), and the assessments might be “ethically or legally questionable” (p. 1) and at risk for litigation against the responsible parties. Remote assessment procedures for special needs students have not been empirically, ethically, and legally vetted ([27]; see below). Beyond brief screening instruments, tests concerning cognitive ability, intelligence, neuropsychological skills, achievement, and the like are not “normed or validated” to use remotely. Even then, studies on test equivalence are conducted in controlled, clinical contexts, and the results of the studies cannot be generalized to the typical home situation of a client. Also, the methodologies of these few studies are “questionable” and even “substantially flawed.” Consequently, remote educational assessments might not meet the standards required for assessment reliability and validity. Wright [28] conducted an empirical study showing that remote digital administration of the intelligence test, the Weschler Intelligence Scale for Children, Fifth Edition (WISC-V), gives mostly equivalent results compared to standard face-to-face test administration procedures. For the remote condition, the study used trained, in-person proctors who stayed in the back of the room. The children between ages 6 and 16 were randomly assigned to conditions. The author concluded that remote WISC-V administration is a viable procedure in tele-assessment. Therefore, concerning the issues raised by Farmer et al. [26] about remote intelligence testing, it appears that progress is being made empirically.

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For tele-assessment for cases of early signs of autism spectrum disorder (ASD), Dahiya, McDonnell, DeLucia, and Scarpa [29] found some evidence for the effectiveness of early screening instruments administered remotely. ASD is diagnosed based on observations as much as testing, and the earlier screened, the better for the child. The authors found only a few studies that met the inclusion criteria of their review. The tests examined were the ADOS-2 and ADI-R, but only in two studies and by the same author group. The results showed positive accuracy, although not necessarily sufficient specificity or discrimination power. Clearly, in this area of tele-assessment, the research on the tests in the field is only beginning, which is a general theme in tele-assessment research. Dale and Smith [30] noted the limitations and exclusion criteria in conducting remote child custody evaluations. However, they argued that the courts should accept assessments in this modality because, if properly done, they would still meet the bar of admissibility evidentiary standards. The assessor can still validly conduct interviews and observations of the adults and children. Some psychological testing is still possible within the constraints, and other components of the assessment can be managed, for example, document retrieval and third-party collateral contacts. They cautioned, though, to proceed on an individual basis, both in terms of the client and the evaluator, assuring that all accept the technology and its limitations. Not every component of the standard assessment procedures can be delivered remotely, but alternatives might be possible scientifically and practically. In the field of neuropsychology, tele-assessment research also is just beginning. In survey research, Chapman, Ponsford, Bagot, Cadilhac, Gardner, and Stolwyk [31] found a reticence in practicing neuropsychologists to use the modality in Australia. Some of the reasons involved having sufficient validity and psychometric equivalence. The authors concluded that establishing the reliability and validity of neuropsychological instruments in the remote context remains a real issue. For tele-assessment of older adults, Marra, Hamlet, Bauer, and Bowers [32] reviewed 19 articles that met their inclusion criteria and found moderate to strong support for the validity of some instruments in tele-assessment. These include some screening measures, language tests, attention/working memory tests, and memory tests. Not one test was found invalid in the sample of tests examined in the review. The authors suggested a core tele-neuropsychological assessment battery based on their findings. This chapter author notes that the tests included do not cover the full range of neuropsychological tests that psychologists should administer, nor the full age range. Moreover, this type of research is only beginning, and generalizations to the forensic and related context are limited. In another forensic-related area, on capacity assessment, Halphen, Dyer, Lee, Reyes-Ortiz, Murdock, Hiner, and Burnett [33] described instruments for remote screening. They include the neuropsychological tests on trail making. The assessment needs an on-site caseworker. The assessments appear possible, but the authors do not discuss the instruments of choice and their psychometric properties in the remote context. In a related type of assessment, on forensic pretrial or adjudicative competency evaluations, Luxton and Lexcen [34] recommended consulting local courts on the

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feasibility of conducting remote assessments and called for caution (extra care). They maintained that no hard rule exists about whether tele-assessment for these types of cases is appropriate, so the professional must carefully consider empirical, legal, ethical, and practice standards and evaluate the pros and cons. If they use the modality in court, they must explain how the method’s limitations were eliminated or mediated so that the conclusions proffered to court are supported by the data gathered. This chapter author agrees with these conclusions but notes that the task is replete with sufficient limitations that impact the justice expected in such cases. For cases of early life trauma or maltreatment examined forensically, Goldenson, Barnes, and Josefowitz [35] reviewed the gamut of responsibilities that the assessor must consider, from professional training to obtaining consent to test selection and administration and to the impact of COVID-19. They noted that there is not one specific guideline for the forensic context, whether civil or criminal. The evaluator must be sufficiently knowledgeable to explain both the evaluee and the court how tele-assessment satisfies “reasonable” requirements for validity. For tests that are available online for testing these types of evaluees, the major personality tests in the field are available (the MMPI-2-RF, the PAI, for example; the Minnesota Multiphasic Personality Inventory Revised Version [36]; the Personality Assessment Inventory [37]). These tests include respondent validity (F family and related) scales, which are important in these evaluations. Other tests that are available include the TSI-2 [38], for traumatic reactions (Trauma Symptom Inventory-2), and dedicated SVTs (symptom validity tests; they refer to the Paulus Deception scale [39]); the chapter author now uses the IOP-29 (Inventory of Problems-29) [40], in this regard. This chapter author agrees that in these types of forensics-related assessments that are not psychoeducational or neuropsychological, or that do not use tests related to them, such as in the case of capacity assessments, while using only tests that are available online, tele-psychological assessment can take place with sufficient reliability and validity for the select instruments used. Next, this chapter shows that the recently published guidelines on the specific test and testing practices agree generally but not uniformly.

2.2.3 Guidelines The American Psychological Association [27] and the Canadian Psychological Association [41] have published guidelines on telehealth, but they do not deal with forensic evaluations. The APA guidelines deal with matters such as test security, data quality, tests, and confidence intervals. They consider tele-assessment “more challenging.” For test equivalence, some earlier very controlled but limited research supports an equivalence between standard face-to-face and tele-assessment results (see [42, 43]). That said, the guidelines acknowledge that group differences might be magnified in tele-assessment, testing administration is altered in tele-assessment, and consequently, data gathered might be altered, although only slightly. The guidelines suggest that psychologists do the best they can in the circumstances and “approximate” as closely as possible traditional face-to-face standardized test administration in tele-assessments. Tests can be substituted if they use ways that tap

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constructs similar to that of the original. Psychologists are asked to “think through” the quality of collected data and “detriments” to validity. Statistically, psychologists should widen confidence intervals to accommodate the greater uncertainties about collected data in tele-assessment. Psychologists need to be cautious in interpreting tele-assessment data and “deliberate and explicit” and transparent about a wider confidence interval and about possible errors that emanate from the novel circumstances of tele-assessment, for example, in their reports. Although carefully written by leading experts in the field, this chapter author maintains that the APA guidelines for tele-assessment do not consider the forensic context carefully. Rules and regulations in local laws and ethical documents such as professional ethics codes might suggest more caution than intimated in the APA guidelines. For example, how far can data deviate from the standard in tele-assessment, how does one establish test substitution standards, and how much loss of test validity is acceptable for testimony validity in court, such as in reports proffered to court? Questions such as these need more specific answers for proper and admissible forensic tele-assessments. The Canadian Psychological Association [41] guidelines do not offer answers to questions such as these. However, the Ontario Psychological Association and Canadian Academy of Psychologists’ remote assessment guidelines in disability assessments [44] deal with psychologists’ obligations when conducting remote civil forensic tele-assessments. In essence, they state that all face-to-face procedure changes should be described in reports and their limitations acknowledged. The assessor needs to examine each data point gathered and the overall data together for different possible weightings and confidence bands that might be apparent because of remote procedures used in comparison to equivalent data that would have been gathered face-to-face. The assessor needs to determine for each data point based on the methodological alterations in the assessment whether to attribute to any of the different weightings and confidence bands that might seem apparent in these regards divergent or altered interpretations/opinions. A section of the APA (Society for Personality Assessment) presented other guidelines on tele-assessment of personality and psychopathology [45, 46]. They indicated that tele-assessments would add errors to the evaluations. The unstructured interviews can be considered accurate for this modality [47], as are structured interviews [48]. Record and collateral information reviews pose no problems. For self-report measures, they also demonstrate the required equivalence [49]. For proper procedures in remote self-report assessments, Wright and colleagues referred the assessor to the document prepared by Corey and Ben-Porath [50]. For performance-based measures, such as the Rorschach, they referred to the guidelines prepared by Meyer, Viglione, Mihura, Erdberg, Bram, Gironimi, Grønnerød, Kleiger, Lipkind, de Ruiter, Pianowski, and Vanhoyland [51]. Essentially, the latter type of tests should not be administered without appropriate on-site proctoring. Meyer et al. added that scoring the Rorschach using the R-PAS would not be based on norms that have been obtained during the COVID-19 pandemic. “Thus, it is impossible to say what is normal, typical or expected right now” [51, p. 6].

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Corey and Ben-Porath [50] were quite circumspect about administering the MMPI-2-RF in tele-assessment. They maintained remote testing presents challenges and perils that risk violating ethical standards, compromising the test results and interpretation, and federal and local laws on test security and personal health information, let alone publishing company proscriptions. They cautioned that in the forensic context remote test administration might be “ill-suited” and could lead to “more problems than it solves” (pp. 202–203).

2.2.4 Comment What about the legal context? Does it support the use of forensic tele-assessments? Batastini, Pike, Thoen, Jones, Davis, and Escalera [52] found that attorneys and judges held negative views about the modality in a survey study. Judges might be prone to rule that tele-assessment evidence is inadmissible to court according to Federal Rules of Evidence ([53]; Rule 702) or based on the Daubert [54] legal decision. The legal respondents had been practicing law for almost 20 years. Nevertheless, they were generally open to the possibility that tele-assessments might be more probative than prejudicial. Burney [55] reviewed the legal arena for governmental regulations related to telehealth and found a disparate mixture. The recent literature review on tele-assessment considers the evaluation possible yet fraught with danger, including violating ethical, regulatory, legal, expected testing standards, and other practice standards. In some contexts, the decision to conduct forensic tele-assessment evaluations should be negative. This is partly due to the harm that could be caused to the client, including legally. However, after all the pros and cons have been weighed, and the psychologist believes that she or he has the requisite competencies for a tele-assessment evaluation, including forensically, when the psychologist decides to proceed with the evaluation, one of the first steps will be to obtain informed consent. The remainder of this chapter examines the best practices in this regard and navigates some of the conundrums and cautions required in creating an adequate informed consent form. This work adds elements to the sample informed consent form that have typically not been listed in the guidelines consulted.

2.3 Informed Consent 2.3.1 The APA Jacobs [56] and Jacobs and Taube [57] (within the reference: [58]), writing for the American Psychological Association, provided information toward developing informed consent forms for telepsychology and tele-neuropsychology. They maintained that a proper informed consent form could not protect the practitioner from criticisms that aspects of the tele-evaluation were either unreliable, invalid, or both! They noted, among other factors, some nonverbal information is lost. The results of the assessment might affect the data obtained in ways “not yet understood,” which could cause uncertainty about what they mean (“less certain”). The remainder of their suggestions are standard ones for an informed consent form.

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2.3.2 The Present Author The chapter author has written a sample informed consent form that can be appended to standard informed consent forms (see Appendix 1). It assumes that the latter has been explained first and that the client has the requisite technology to participate in tele-assessment. The form has several major sections: what is informed consent in telepsychology; what is telepsychology/telehealth; why it is needed; what technology is needed; does telepsychology include psychological testing; and most importantly, for present consideration, the risks, limitations, and implications of the modality. This section covers what to expect in sessions, the quality of the work, security, financials, and the evaluator’s risks. The section’s last point is not discussed in any material read and reviewed to date, but it could be the most important. Essentially, using language appropriate to the client indicates that, despite the risks and limitations involved, the assessor feels confident of the relative reliability and validity for the assessment undertaken and its data, results, conclusions, and recommendations while qualifying that there is increased uncertainty and reduced quality that is inevitable in tele-assessments. That said, this chapter does have concerns, and informed consent can be used only in certain types of forensic cases. The following specifies the psychological and legal arguments for and against deciding to undertake forensic tele-assessments in these COVID-19 times.

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Conclusion

Tele-assessment is being used increasingly in the health environment being encountered globally and most likely will continue once the pandemic subsides due to its advantages. However, limitations and risks inherent to it jeopardize its reliability and validity, as outlined in the chapter. Psychological injuries deserve fair assessments for court-connected purposes. The court has to be confident that all evaluation procedures used in a case at hand meet the minimum governmental, regulatory, and practice guidelines required of the assessment, or else it will be subject to admissibility challenges. When the court doubts the reliability of testimony and proffered reports to the court, it will either seek to strike the testimony/report from the record or reduce the weight given to it. The assessor can expect a withering cross-examination in the latter case. Tele-assessments in the forensic context and related work are especially problematic because of the altered methods used. Practitioners should proceed with caution when undertaking such assessments, including in the informed consent process, and openly acknowledge the limitations and risks in all evaluation phases and in the court process. Moreover, the court expert needs to know that the conclusions, opinions, and recommendations made to the court reach the expected bar on the balance of probabilities. Normally, the assessor vouches that their end arguments to testimony/ reports reach this bar and that the alternate possible conclusions/opinions/ recommendations in a case at hand have been considered and do not reach this

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bar. In the remote assessment environment, the task becomes more complex because the assessor expert must maintain that, despite the limitations and risks of tele-assessment, the bar of a balance of probabilities still applies to the end arguments. The bar has been met scientifically and methodologically and in terms of the remote modality used. The chapter author believes that tele-assessments can meet this bar when all the recommendations for its proper use have been implemented and all the restrictions on its use have been noted and not violated, e.g., for intellectual and neuropsychological testing. The present author has found it difficult to perfectly implement the literature’s recommendations, as reviewed in this article. It would be interesting to follow the fallout over time of the success in tele-assessments in reaching the bars described presently. It could be that cautionary articles will be written on the pitfalls in tele-assessment that render it quite difficult to implement well enough for court purposes, and its probative value in court will be diminished for some areas of practice, for some tests and methodologies, and even for some assessors who fail to proceed with the necessary care and precaution. Neither the clients subject to tele-assessments nor the court receiving testimony/reports based on them should be involved with tele-assessments that do not meet all the rigorous standards required. Moreover, assessors involved and the courts need to keep up to date on the rapidly changing literature in tele-assessments to ensure that the court can function optimally in its deliberations. Core Messages

• Psychological injuries deserve fair assessments for the purposes connected with the court. • Telepsychology brings risks that jeopardize the reliability of testimony and proffered reports to the court. • Tele-assessments in the forensic context and related work are especially problematic in these regards. • Practitioners should proceed with caution when undertaking such assessments, including in the informed consent process.

Acknowledgements The author would like to thank Julie Goldenson and Eric Drogin for helpful corrections to the informed consent form.

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Appendix 1: Informed Consent Explanation for Telehealth/Telepsychology Procedures and Technology What is Informed Consent for Telehealth/Telepsychology? As with the first part of the office brochure already explained to you, informed consent means that you have been provided a fair description in language that is understandable to you of the psychological services being provided, you have asked all questions that have come up, and I have answered them to the best of my ability. You end up agreeing to the psychological services being offered by remote/virtual, e.g., video or phone, means, and know the risks and benefits. You agree to the services voluntarily, knowing they will remain confidential except in the case of people or organizations that you authorize me to send the report that will be written, if any, the clinical notes taken, etc. Note, there are life-saving reasons for this office to violate confidentiality, as already explained in the original brochure (e.g., your clear threats of suicidality or homicidality; as well as reports of childhood abuse, elder abuse). By signing the end of the document, you are indicating that you agree with all its contents, which I will now proceed to explain. As with all aspects of this brochure, I am explaining them to you in detail as our first session proceeds or shortly thereafter, and you can ask questions as you find necessary. I have already discussed with you in the present client brochure—everything about our practice, assessment procedures, psychotherapy, and the regulatory and scientific basis for everything done in the office. Why Telehealth or Telepsychology? Telepsychology refers to providing psychological services securely and confidentially at a distance, not in person or face-to-face, or remotely, through computer, electronic, or telephone technology (e.g., including and not restricted to) virtual platforms and videoconferencing; mobile device/cellular phone/smart phone technology, the regular landline telephone), and in a way that follows existing professional, governmental, and legal rules and guidelines. It requires appropriate computer hardware, web cameras, software, and other equipment and technology that we have determined preliminarily that you do have or have access to. [It does not mean getting such services on the internet and openly for anyone to see and hear.] You may wish to get telepsychological services because of the convenience in staying at home, or you might be unable to get to my office for a face-to-face session. Moreover, in recent times, the challenges presented by COVID-19 (the “Coronavirus”) are changing the nature of psychological services, such as having myself and my staff to work from secure home offices. We do not want these altered circumstances to prevent you from obtaining the secure psychological services that you need.

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The societal challenge presented by COVID lockdowns and restrictions have changed session format for interviews and the procedures for psychological services and these changes will last for quite a while in the foreseeable future, and maybe even well into the future. These changes include changes to interview formats by remote means, and changes by using remote testing/filling in questionnaires. What Is Telehealth/Telepsychology? 1. Telehealth is about using remote means, such as through the internet and computers, to communicate with and care for patients/clients, instead of using the traditional face-to face means in a clinical office. By remote, we mean that the contact between you and I and/or the testing platform is virtual, at a distance, by computer, etc. Telehealth does not just refer to telephone communication. It involves gathering information about you, as in interviews by video, having you answer questionnaires online, and even reviewing documents or other materials with you by sharing them on the screen so we can discuss them. Also, in these times, psychological testing is moving from face-to-face to remote, virtual administration by internet/computer supervision and by internet/computer test administration and scoring. It involves gathering information about you, as in the interviews by video, by having you answer questionnaires online. 2. Telehealth is also about using secure, protected, private means to do so, such as in using platforms and programs that cannot be shared and remain confidential, or just ways of communicating safely between the professionals involved and the patients/clients like you. In these times, we are using telehealth and virtual, remote procedures in our office, while still following all regulatory, legal, government, and scientific protocols to ensure the best and safest psychological services possible for you. The security of your communications with us—including all of the information we share—remains a top priority of this practice. Properly conducted telehealth services do not call on us to cut corners when it comes to information security. All of us in the office are working from home, and we have instituted secure methods of communicating with you. These same secure methods are used for other people involved in your case or care, as the case may be, that is, with anyone else you authorize us to consult with. That is, we securely communicate with your designated family members, family doctors/primary care physicians, other professionals/specialists on your case/insurers and their staff, worker compensation boards and their staff, work site supervisors/reps/HR workers, school administrators/teachers, lawyers/attorneys/legal reps, etc. Your personal information is protected in working remotely with us as it would be had we been working face-to-face in the office. What Do I Need for Telehealth/Telepsychological Services? Here are the remote psychological services options that are available. (1). Most of you will have computers and access to remote communication formats, such as Zoom and Virtual Care by Think Research, which are common platforms. That means that you need to have a webcam; and you need to be able to

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follow instructions on how to log into sessions, as per e-mailed instructions sent by my staff. (2). Alternatively, sessions can be done by the phone, but that does not allow for initial face-to-face remote contact, which is essential for creating rapport. In those circumstances, we can use non-secure methods to chat briefly through methods that allow us to see each other (over non-secure video methods before we begin discussing confidential matters (such as using Face Time or What’s App). This can be a helpful way to communicate about practical matters before we begin to discuss personal information of the sort that is conveyed during actual sessions. (3). Only when there are no other remote options, will we use the telephone to communicate, assuming that is possible in your particular case. Your health is my priority. [For any assessments that are being undertaken for medico-legal purposes (i.e., with no follow-up therapy), such as for court actions/ disability claims, typically phone-only sessions are not allowed in these circumstances]. Can Psychological Testing Be Conducted Via Telehealth/ Telepsychology? Yes, many tests can be administered via this medium. In signing this informed consent client brochure, you are agreeing to virtual, remote testing, as well, should this apply to your case. (1). This means that we will send you a link to the testing company’s distal testing platform. Also, this means that I have to watch you by Zoom or Virtual Care (or an equivalent platform) as you answer the questions online, for example, being available if you have any questions. Or, my staff might watch you and I will be available by phone to answer the questions. When you get the test company’s link by email from my staff, you will have to click on the indicated spot to open the site and then click to answer the questions. The questions might be True-False, or ones like answering 0–3, with 0 being none and 3 being a lot (e.g., pain), or the like. As already indicated and explained, please answer all questions honestly, without exaggerating or minimizing, for example, without writing 3 out of 3 for each question just to get through the questionnaire very quickly. Read each question carefully and answer the question for what it is, taking the time required. (2). If you do not have a computer link, we will collaborate to establish alternate means, such as reading a questionnaire over the phone and writing down your answers, if this is permitted by the test company for their questionnaire. (3). Or, you might have to come to my office on the week-end when no one is in the building (on a Saturday or a Sunday, for example, when I go in to do paper work and other business), coming in only if the government allows it, and also that there are no other options and your health permits it). We have masks and hand sanitizers available. The building is a medical one and is scrubbed down very well everyday according to government advisories. We have followed all government regulations in opening the office after the COVID lockdown.

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The Risks, Limitations, and Implications of Telehealth/Telepsychology A. For the sessions (1)

(2)

(3) (4)

(5) (6)

(7)

(8)

For interviews/sessions, generally, Zoom or Virtual Care (or an equivalent platform) and phone sessions can go well, and rapport is created well enough. However, you might be uncomfortable being on camera. Please let us know if that is the case. The video quality might not be good because of the internet connection/service provider, the quality of your webcam on your computer, lighting difficulties, interruptions for multiple reasons, including technical ones (we can continue by phone if the interruption extend longer than 1–2 min), difficulty with technology that you might have and/or reluctance to use it, difficulty following instructions provided by my staff to set up sessions, and so on. You might not be able to find a quiet, confidential place in the home for sessions, family members might interrupt, and so on. Video Issues. You might not be comfortable seeing me on the screen, only seeing the upper body. You might see yourself on the screen and feel uncomfortable. You might find it hard to see both myself and yourself on the screen at the same time, distracting you. You might have visual or hearing difficulties or impairments that make it harder to use telepsychology. [You might worry that we are recording you, but there is no recording and storage of the session that takes place.] There is a slight delay in talking and hearing in virtual sessions, which could throw you off. I am a great note taker, and just as in a face-to-face session, I write down what you say, ask questions and wait for your response, writing down the answer, before asking another question, etc. But inevitably, it will be harder for me to see or capture all your nonverbal behaviour in the same way I would if you were in my office, e.g., clearly seeing eye upset, eye opening, creases on the brow, lip corners, shaking your leg, clasping your hands, hand shaking, noticing your cold hands, and other nonverbal information. Even auditory cues might be more difficult to interpret without seeing the face well. Was that a quivering voice or just a normal variation in the voice? Also, in videoconferencing, I could not use olfactory or smell cues, for example, or touch cues from hand shaking. You might be in crisis, making the remote contact more difficult, but we will have established our safety plan before beginning for emergencies. The video or remote modality might trigger a notable increase in your distress or any serious psychological issue that you might have,

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(10)

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but that could happen even in sessions in my office. That said, if it were to happen, unlike in my office, I could not be at your place and you would not get help until emergency help could arrive. You might think it is ok to use telecommunication means such as texts and emails to send me confidential information between sessions, but these technologies might not be secure in your system, so please limit the information sent by these means to administrative matters or requests for me to contact you. Note: You might want people who are taking care of you, for example, if you are severely disabled, to be present, but that should take place only if that would be the normal case in face-to-face office meetings.

B. For quality of telepsychology (1) The assessments that we do together include remote psychological testing, where you fill in questionnaires online. Not all tests that I would like to use are online, which is a limitation. (2) It might be harder for you to sit at the computer at home to answer questions compared to sitting at a desk in my office. (3) For these and other reasons involved in remote sessions, the data that I gather might not be as reliable or accurate for the purposes of my assessment. That is, I might be less certain compared to the face-to-face situation of what the information that I gather might mean. This refers to the information from our interview and the questionnaires you fill in online. (4) This limitation about the possible lesser quality of the telepsychology modality also applies to my diagnosis, treatment planning, and if therapy continues, your updating each week in our weekly sessions. That said, psychologists have the competence to take these factors into consideration and arrive at the best conclusions possible for any one case in the context at hand, which will happen for you, too, in this type of remote contact. C. Security (1) I have a legal and ethical duty to do my best to protect all communications in our remote psychological services. We use the most secure devices and services—we did not skirt quality or price in setting up virtual psychological services—but it might be possible for hackers to overcome these barriers. As you might know from listening to the news, despite using the best technology available that is secure and private, no technology can absolutely guarantee the privacy of our communications. You can do your share by using secure networks, password protected means, etc.

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D. Financials (1) Health service third party payors, such as insurance companies, might be reluctant to pay for telepsychological services, and you will have to decide financially how to proceed if that happens in your case. (2) You might find your technology inadequate and want to spend monies out of your own pocket to improve them. E. My Risks (1) There is a major risk at my end, of you recording our conversations or even questionnaire items. By agreeing to this form, you accept that you will not do anything like that. You will not record our sessions and you will not make copies of questionnaire materials/take snapshots of them online, and so on. (2) My work about you, such as in reports that I write, could be challenged easier by insurers, the court or related places, such as worker compensation, or by anyone who feels that telepsychology works less well, knows the limitations, and points them out. At the same time, I know these limitations and will proceed carefully in all my work using telepsychology. Therefore, I can defend very well my work about you and any conclusions that I come up with about you, including about the assessment procedures used, tests used, diagnoses, prognoses, and recommendations.

Informed Consent Form to Sign for Telehealth/Telepsychology Our usual client brochure has described our practice, the psychotherapy services we provide, the assessment procedures we conduct, and the basic legal rules that pertain to all of the services that we offer. Most of that information remains very much the same in remote, virtual telehealth, as modified or augmented by this addendum. You and I have reviewed this brochure, our usual consent form, and this addendum or supplement during our first telehealth meeting together. You agree to the following:

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I have read and reviewed this “Informed Consent Addendum for Telehealth” with Dr. YY, or a duly authorized and competent representative of his practice. I understand this Addendum’s contents, all the attendant risks, limitations, and implications, and all of my questions have been answered to my satisfaction. I agree to engage in telehealth services.

____________________________________ Signature (Client or Guardian)

____________________________________ Client Name

____________________________________ (Signature of witness)

____________________________________ Witness’ name if not Dr. YY

____________________________________ (If other than client, state relationship to client)

Dated: ______________________________

References 1. Camus A (1947/1948) La peste [The Plague] (Gibert S, Trans) Gallimard 2. American Psychological Association (2017) Ethical principles of psychologists and code of conduct. American Psychological Association. Published online https://www.apa.org/ ethics/code/#:%7E:text=Copyright%20%C2%A9%202017%20American%20Psychological %20Association.%20All%20rights,five%20General%20Principles%20%28A-E%29%20and %20specific%20Ethical%20Standards. Accessed Mar 2017 3. McCord C, Bernhard P, Walsh M, Rosner C, Console K (2020) A consolidated model for telepsychology practice. J Clin Psychol 76(6):1060–1082. https://doi.org/10.1002/jclp.22954 4. Young G (2014) Malingering, feigning, and response bias in psychiatric/psychological injury —implications for practice and court. Springer Science + Business Media

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5. Young G, Drogin E (2014) Psychological injury and law I: causality, malingering, and PTSD. Ment Health Law Policy J 3:373–416 6. Young G, Drogin E (2014) Psychological injury and law II: implications for mental health policy and ethics. Ment Health Law Policy J 3:417–470 7. Fitzgerald LF, Collinsworth LL, Lawson AK (2013) Sexual harassment, PTSD, and criterion a: If it walks like a duck ... Psychol Inj Law 6(2):81–91. https://doi.org/10.1007/s12207-0139149-8 8. Rogers R, Bender SD (eds) (2018) Clinical assessment of malingering and deception, 4th edn. The Guilford Press 9. Young G (2015) Malingering in forensic disability-related assessments: prevalence 15 +/ −15%. Psychol Inj Law 8(3):188–199. https://doi.org/10.1007/s12207-015-9232-4 10. Young G (2019) The cry for help in psychological injury and law: concepts and review. Psychol Inj Law 12(3–4):225–237. https://doi.org/10.1007/s12207-019-09360-y 11. World Health Organization (2020) Timeline of WHO’s response to COVID-19. World Health Organization. Published online https://www.who.int/news-room/detail/29-06-2020covidtimeline. Accessed 29 Jun 2020 12. Jenkins E, Gadermann A, McAuliffe C (2020) Mental health impact of coronavirus pandemic hits marginalized groups hardest. The Conversation. Published online https://theconversation. com/mental-health-impact-of-coronavirus-pandemic-hits-marginalized-groups-hardest142127. Accessed 26 Jul 2020 13. Martin JN, Millán F, Campbell LF (2020) Telepsychology practice: primer and first steps. Pract Innov 5(2):114–127. https://doi.org/10.1037/pri0000111 14. Varker T, Brand RM, Ward J, Terhaag S, Phelps A (2019) Efficacy of synchronous telepsychology interventions for people with anxiety, depression, posttraumatic stress disorder, and adjustment disorder: a rapid evidence assessment. Psychol Serv 16(4):621– 635. https://doi.org/10.1037/ser0000239 15. Chenneville T, Schwartz-Mette R (2020) Ethical considerations for psychologists in the time of COVID-19. Am Psychol 75(5):644–654. https://doi.org/10.1037/amp0000661 16. Hoerger M, Alonzi S, Perry LM, Voss HM, Easwar S, Gerhart JI (2020) Impact of the COVID-19 pandemic on mental health: real-time surveillance using Google Trends. Psychol Trauma Theory Res Pract Policy 12(6):567–568. https://doi.org/10.1037/tra0000872 17. Kaslow NJ, Friis EA, Cattie JE, Cook SC, Crowell AL, Cullum KA, del Rio C, Marshall-Lee ED, LoPilato AM, VanderBroek-Stice L, Ward MC, White DT, Farber EW (2020) Flattening the emotional distress curve: a behavioral health pandemic response strategy for COVID-19. Am Psychol. https://doi.org/10.1037/amp0000694 18. Holmes EA, O’Connor RC, Perry VH, Tracey I, Wessely S, Arseneault L, Ballard C, Christensen H, Silver RC, Everall I, Ford T, John A, Kabir T, King K, Madan I, Michie S, Przybylski AK, Shafran R, Sweeney A, Worthman CM, Yardley L, Cowan K, Cope C, Hotopf M, Bullmore E (2020) Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. Lancet Psychiatry 7(6):547–560. https:// doi.org/10.1016/S2215-0366(20)30168-1 19. Pfefferbaum B, North CS (2020) Mental health and the Covid-19 pandemic. N Engl J Med 383(6):510–512. https://doi.org/10.1056/nejmp2008017 20. Torales J, O’Higgins M, Castaldelli-Maia JM, Ventriglio A (2020) The outbreak of COVID-19 coronavirus and its impact on global mental health. Int J Soc Psychiatry 66 (4):317–320. https://doi.org/10.1177/0020764020915212 21. Horesh D, Brown AD (2020) Traumatic stress in the age of COVID-19: a call to close critical gaps and adapt to new realities. Psychol Trauma Theory Res Pract Policy 12(4):331–335. https://doi.org/10.1037/tra0000592 22. Kröger C (2020) Shattered social identity and moral injuries: work-related conditions in health care professionals during the COVID-19 pandemic. Psychol Trauma Theory Res Pract Policy 12(S1):S156–S158. https://doi.org/10.1037/tra0000715

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23. Sibley CG, Greaves LM, Satherley N, Wilson MS, Overall NC, Lee CHJ, Milojev P, Bulbulia J, Osborne D, Milfont TL, Houkamau CA, Duck IM, Vickers-Jones R, Barlow FK (2020) Effects of the COVID-19 pandemic and nationwide lockdown on trust, attitudes toward government, and well-being. Am Psychol 75(5):618–630. https://doi.org/10.1037/ amp0000662 24. Bojdani E, Rajagopalan A, Chen A, Gearin P, Olcott W, Shankar V, Cloutier A, Solomon H, Naqvi NZ, Batty N, Festin FED, Tahera D, Chang G, DeLisi LE (2020) COVID-19 pandemic: impact on psychiatric care in the United States. Psychiatry Res 289:113069. https://doi.org/10.1016/j.psychres.2020.113069 25. Greenbaum Z (2020) How well is telepsychology working? Am Psychol Assoc 51(5):46. Available online https://www.apa.org/monitor/2020/07/cover-telepsychology 26. Farmer RL, McGill RJ, Dombrowski SC, Benson NF, Smith-Kellen S, Lockwood AB, Powell S, Pynn C, Stinnett TA (2020) Conducting psychoeducational assessments during the COVID-19 crisis: the danger of good intentions. Contemp Sch Psychol. https://doi.org/10. 1007/s40688-020-00293-x 27. Wright AJ, Mihura JL, Pade H, McCord DM (2020) Guidance on psychological tele-assessment during the COVID-19 crisis. Am Psychol Assoc. Published online https:// www.apaservices.org/practice/reimbursement/health-codes/testing/tele-assessment-covid-19. Accessed 1 May 2020 28. Wright AJ (2020) Equivalence of remote, digital administration and traditional, in-person administration of the Wechsler Intelligence Scale for Children, Fifth Edition (WISC-V). Psychol Assess 32(9):809–817. https://doi.org/10.1037/pas0000939 29. Dahiya AV, McDonnell C, DeLucia E, Scarpa A (2020) A systematic review of remote telehealth assessments for early signs of autism spectrum disorder: video and mobile applications. Pract Innov 5(2):150–164. https://doi.org/10.1037/pri0000121 30. Dale MD, Smith D (2020) Making the case for videoconferencing and remote child custody evaluations (RCCEs): the empirical, ethical, and evidentiary arguments for accepting new technology. Psychol Public Policy Law. Advance online publication. https://doi.org/10.1037/ law0000280 31. Chapman JE, Ponsford J, Bagot KL, Cadilhac DA, Gardner B, Stolwyk RJ (2020) The use of videoconferencing in clinical neuropsychology practice: a mixed methods evaluation of neuropsychologists’ experiences and views. Aust Psychol. Published online 24 Jun 2020. https://doi.org/10.1111/ap.12471 32. Marra DE, Hamlet KM, Bauer RM, Bowers D (2020) Validity of teleneuropsychology for older adults in response to COVID-19: a systematic and critical review. Clin Neuropsychol 1– 42. Published online 10 Jun 2020. https://doi.org/10.1080/13854046.2020.1769192 33. Halphen JM, Dyer CB, Lee JL, Reyes-Ortiz CA, Murdock CC, Hiner JA, Burnett J (2020) Capacity evaluations for adult protective services: videoconference or in-person interviews. J Elder Abuse Negl 32(2):121–133. https://doi.org/10.1080/08946566.2020.1740127 34. Luxton DD, Lexcen FJ (2018) Forensic competency evaluations via videoconferencing: a feasibility review and best practice recommendations. Prof Psychol Res Pract 49(2):124–131. https://doi.org/10.1037/pro0000179 35. Goldenson J, Josefowitz N (2021) Remote forensic psychological assessment in civil cases: considerations for experts assessing harms from early life abuse. Psychol Injury Law 13:1–5 36. Ben-Porath YS, Tellegen A (2008/2011) MMPI-2-RF (Minnesota multiphasic personality inventory—2 restructured form): manual for administration, scoring, and interpretation. University of Minnesota Press 37. Morey LC (1991) Personality assessment inventory (PAI): professional manual. Psychological Assessment Resources 38. Briere J (2011) Trauma symptom inventory–2 (TSI–2). Psychological Assessment Resources 39. Paulhus DL (1998) Paulhus deception scales. Multi-Health Systems, Inc. 40. Viglione DJ, Giromini L, Landis P (2017) The development of the inventory of problems-29: a brief self-administered measure for discriminating bona fide from feigned psychiatric and

426

41.

42.

43.

44.

45.

46.

47.

48.

49.

50.

51.

52.

53. 54. 55.

G. Young cognitive complaints. J Pers Assess 99(5):534–544. https://doi.org/10.1080/00223891.2016. 1233882 Canadian Psychological Association (2020) Interim ethical guidelines for psychologists providing psychological services via electronic media. Published online https://cpa.ca/docs/ File/Ethics/CPAe-therapyGuidelinesUpdate2020.pdf Brearly TW, Shura RD, Martindale SL, Lazowski RA, Luxton DD, Shenal BV, Rowland JA (2017) Neuropsychological test administration by videoconference: a systematic review and meta-analysis. Neuropsychol Rev 27(2):174–186. https://doi.org/10.1007/s11065-017-9349-1 Wright AJ (2018) Equivalence of remote, online administration and traditional, face-to-face administration of the Woodcock-Johnson IV cognitive and achievement tests. Arch Assess Psychol 8(1):23–35 The Ontario Psychological Association and Canadian Academy of Psychologists in Disability Assessments Working Group on remote assessments (2020) Guidelines for best practices in psychological remote assessments Version 1. Available online http://www.psych.on.ca/ getattachment/6be07aee-299d-4bf5-a21a-199cea8312bd/OPACAPDA-Remote-AssessmentV8.pdf.aspx?ext=.pdf. Accessed 20 Aug 2020 COVID-19 Task Force to Support Personality Assessment (2020) Tele-Assessment of personality and psychopathology. Society for Personality Assessment. Available online https://resources.personality.org/www.personality.org/General/pdf/SPA_Personality_TeleAssessment-Guidance_6.10.20.pdf Wright AJ, Mihura JL, Buckingham K, David R, Giromini L, Hisatugo C, Ingram L (2020) Tele-assessment of personality and psychopathology: COVID-19 Task force to support personality assessment. Society for Personality Assessment. Available online https:// resources.personality.org/www.personality.org/General/pdf/SPA_Personality_TeleAssessment-Guidance_6.10.20.pdf Ramsden J (2018) “Are you calling me a liar”? Clinical interviewing more for trust than knowledge with high-risk men with antisocial personality disorder. Int J Forensic Ment Health 17(14):351–361. https://doi.org/10.1080/14999013.2018.1505789 Hyler SE, Gangure DP, Batchelder ST (2005) Can telepsychiatry replace in-person psychiatric assessments? A review and meta-analysis of comparison studies. CNS Spectr 10(5):403–415. https://doi.org/10.1017/s109285290002277x Menton WH, Crighton AH, Tarescavage AM, Marek RJ, Hicks AD, Ben-Porath YS (2017) Equivalence of laptop and tablet administrations of the Minnesota multiphasic personality inventory–2 restructured form. Assessment 26(4):661–669. https://doi.org/10.1177/ 1073191117714558 Corey DM, Ben-Porath YS (2020) Practical guidance on the use of the MMPI instruments in remote psychological testing. Prof Psychol Res Pract 51(3):199–204. https://doi.org/10.1037/ pro0000329 Meyer G, Viglione D, Mihura J, Erdberg P, Bram A, Gironimi L, Grønnerød C, Kleiger J, Lipkind J, de Ruiter C, Pianowski G, Vanhoyland M (2020) Recommendations concerning remote administration of the Rorschach. Available online https://r-pas.org/Docs/Remote% 20Administration%20of%20the%20Rorschach.pdf Batastini AB, Pike M, Thoen MA, Jones ACT, Davis RM, Escalera E (2019) Perceptions and use of videoconferencing in forensic mental health assessments: a survey of evaluators and legal personnel. Psychol Crime Law 26(6):593–613. https://doi.org/10.1080/1068316x.2019. 1708355 Michigan Legal Publishing Ltd (2016) Federal Rules of Evidence; 2017 Edition (2017th ed.). Michigan Legal Publishing Ltd. Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579, 589 (1993) Burney M (2020) Federal regulation of telemedicine: weighing benefits to patients with chronic illnesses against constitutional questions. Ann Health Law Adv Direct 29(2):111– 120. Available online https://www.luc.edu/media/lucedu/law/students/publications/ahl/pdfs/ Advanced%20Directive%20-%20Spring%202020.pdf

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56. Jacobs JA (2020) Information about the sample informed consent for telepsychology. Trust Parma. Available online https://parma.trustinsurance.com/Resource-Center/COVID-19Resources 57. Jacobs JA, Taube D (2020) Information about the sample informed consent for teleneuropsychological and telepsychological assessment. Trust Parma. Available online https://parma.trustinsurance.com/Resource-Center/COVID-19-Resources 58. Joint Task Force for the Development of Telepsychology Guidelines for Psychologists (2013) Guidelines for the practice of telepsychology. Am Psychol 68(9):791–800. https://doi.org/10. 1037/a0035001

Gerald Young is a Full Professor in Psychology at Glendon College, York University, Toronto, Canada. He is a Fellow of the Association for Psychological Science (APS) and the American Psychological Association (APA). He has received awards from the American Psychological Association and the Canadian Psychological Association (CPA), including for lifetime achievement. Young is Editor-in-Chief of the journal Psychological Injury and Law, which he founded, and his work in that area has led to invited speaker addresses at scientific conferences. His most recent books are Revising the APA Ethics Code (Springer, 2017) and Causality and Neo-Stages in Development: Toward Unifying Psychology (Springer, 2022). His next book is co-edited and on Handbook of Psychological Injury and Law (Springer Nature, in preparation). He has appeared as an expert witness for a case involving the Supreme Court of Canada. His practice covers rehabilitation and couples/families.

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Physical Inactivity, Sedentarism, and Low Fitness: A Worldwide Pandemic for Public Health Javier Bueno-Antequera and Diego Munguía-Izquierdo

Lack of activity destroys the good condition of every human being while movement and methodical physical exercise save it and preserve it. Plato

Summary

Physical inactivity, sedentary lifestyle, and low physical fitness are three major health problems worldwide. This chapter analyzes conceptual aspects, consequences, and temporal trends in the prevalence of physical inactivity, sedentary lifestyle, and fitness levels, based on the high-quality studies that included worldwide data and large samples to provide a global vision of these problems. In brief, physical inactivity, sedentarism, and low fitness negatively affect physical and mental health, which infer a substantial economic burden

J. Bueno-Antequera
D. Munguía-Izquierdo (&) Physical Performance Sports Research Center, Department of Sports and Computer Science, Section of Physical Education and Sports, Faculty of Sports Sciences, Research Group Actividad física, salud y deporte CTS-948, Universidad Pablo de Olavide, ES-41013 Seville, Spain e-mail: [email protected] J. Bueno-Antequera e-mail: [email protected] J. Bueno-Antequera
D. Munguía-Izquierdo Research Group in development Movimiento Humano. Universidad de Zaragoza, ES-50009 Zaragoza, Spain D. Munguía-Izquierdo Biomedical Research Networking Center on Frailty and Healthy Aging, Madrid, Spain © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 N. Rezaei (ed.), Integrated Science of Global Epidemics, Integrated Science 14, https://doi.org/10.1007/978-3-031-17778-1_19

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worldwide. Physical inactivity prevalence and sedentary behavior levels in children, young adolescents, and adults have been stable for different world regions over the last 15 years, with a decrease in the prevalence of physical inactivity among boys and an increase in adults from high-income countries. Also, there is an overall temporal trend of decline in cardiorespiratory fitness levels across all ages. In contrast, there is an increase in musculoskeletal fitness among children and adolescents with stable/mixed results among adults. Scientific evidence dissemination throughout society is necessary to raise awareness to achieve a more active lifestyle, reduce time spent in sedentary behaviors, and reach and maintain adequate fitness levels. This chapter calls for researchers and institutions to clarify scientific gaps (e.g., the situation analysis in the middle- and low-income countries) and harmonize work methodologies and lines of interest with professionals and entities from other countries to coordinate efforts more effectively. Graphical Abstract/Art Performance

Physical inactivity, sedentarism, and low fitness are associated with health and economic burden

The code of this chapter is 01101110 01001001 01110100 01101001 01111001 01100001 01100011 01110110 01101001 01110100.

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Physical Inactivity, Sedentarism, and Low Fitness …

Keywords




Cardiorespiratory fitness Economic Physical activity Physical inactivity Strength Trends




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Health
Musculoskeletal fitness

Prevalence
Sedentary behavior


Introduction

Physical activity applies to “any bodily movement produced by skeletal muscles that result in energy expenditure” [1]. The terminology “physical inactivity” refers to those with low physical activity levels or those that do not meet the minimum physical activity required for a healthy life. In the scientific literature, different units and thresholds have been used to define the minimum physical activity required for a healthy life. There are recommendations based on step numbers, energy expenditure, or time spent on daily or weekly physical activity. Other recommendations consider a total physical activity or, by intensities, only physical activities of a certain intensity (i.e., only moderate and vigorous) or consecutive physical activities in blocks of 10 min. Although the existence of different recommendations could confuse the population, there is a consensus on the suitability of the public health objective of 60 min of moderate and vigorous physical activity a day for children/adolescents and 150 min of moderate to vigorous physical activity a week for adults/older adults supported by the 2018 United States Physical Activity Guidelines Scientific Report of the Advisory Committee [2]. To date, a large amount of scientific works has researched physical inactivity. One of the first works that highlights that physical inactivity constitutes a major global issue was an editorial by Steve N Blair published in 2009 [3]. The author’s main concern was that the importance of physical activity was under consideration by both public health institutions and society. Another notable event was the Lancet Physical Activity Series Working Group, set to publish every four years (coinciding with the Olympic Games) a special volume on physical activity. The Lancet get out the first series in 2012 and reinforced why physical inactivity should be a major public health priority, based on scientific evidence. The series included papers evaluating how physical inactivity globally affected major non-communicable diseases, global levels of physical activity, physical activity determinants, and effective strategies for physical activity promotion based on scientific evidence.1 In 2016, the Lancet Physical Activity Series Working Group published the second series on physical activity, presenting the progress and challenges in global surveillance, epidemiological investigation, strategies for intervention, and the field’s policy actions.2 1 2

For more details, visit the website https://www.thelancet.com/series/physical-activity. For more details, visit the website https://www.thelancet.com/series/physical-activity-2016.

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Sedentary behavior applies to “any waking activity characterized by energy expenditure  1.5 metabolic equivalents while in a sitting or reclining posture” [4]. This definition includes both intensity (