Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis [1st ed.] 9789811585333, 9789811585340

Table of contents :
Front Matter ....Pages i-xix
Front Matter ....Pages 1-1
An Introduction to Computational Intelligence in COVID-19: Surveillance, Prevention, Prediction, and Diagnosis (Khalid Raza, Maryam, Sahar Qazi)....Pages 3-18
Role of Computational Intelligence Against COVID-19 (Simran Kaur, Yasha Hasija)....Pages 19-43
Front Matter ....Pages 45-45
Using Computational Intelligence for Tracking COVID-19 Outbreak in Online Social Networks (Sahar Qazi, Salim Ahmad, Khalid Raza)....Pages 47-59
Social Network Analysis for the Identification of Key Spreaders During COVID-19 (Ahmadi Hasan, Ahmad Kamal)....Pages 61-77
Mobile Technology Solution for COVID-19: Surveillance and Prevention (Shaban Ahmad, Pragya Chitkara, Fatima Nazish Khan, Avtar Kishan, Vaibhav Alok, Ayyagari Ramlal et al.)....Pages 79-108
The Role of Internet of Things (IoT) in the Containment and Spread of the Novel COVID-19 Pandemic (Ibrahim Babangida Mohammed, Salmi Mohd Isa)....Pages 109-119
Front Matter ....Pages 121-121
A Review on Predictive Systems and Data Models for COVID-19 (Fatima Nazish Khan, Ayesha Ayubi Khanam, Ayyagari Ramlal, Shaban Ahmad)....Pages 123-164
A Comparative Study of the SIR Prediction Models and Disease Control Strategies: A Case Study of the State of Kerala, India (K. Reji Kumar)....Pages 165-174
Computational Intelligence Approach for Prediction of COVID-19 Using Particle Swarm Optimization (R. S. M. Lakshmi Patibandla, V. Lakshman Narayana)....Pages 175-189
COVID-19 Insightful Data Visualization and Forecasting Using Elasticsearch (Hemant Kumar Tewtia, Deepti Singh)....Pages 191-205
Computational Intelligence Methods for the Diagnosis of COVID-19 (Sarra Akermi, Subrata Sinha, Surabhi Johari, Sunil Jayant, Anshul Nigam)....Pages 207-223
Rapid Computer Diagnosis for the Deadly Zoonotic COVID-19 Infection (Peter Mudiaga Etaware)....Pages 225-250
Computational Intelligence Methods in Medical Image-Based Diagnosis of COVID-19 Infections (Abdullahi Isa)....Pages 251-270
Front Matter ....Pages 271-271
Computational Intelligence in Drug Repurposing for COVID-19 (Manish Kumar Tripathi, Sujata Sharma, Tej P. Singh, A. S. Ethayathulla, Punit Kaur)....Pages 273-294
COVID-19: Hard Road to Find Integrated Computational Drug and Repurposing Pipeline (Ankita Sahu, Sahar Qazi, Khalid Raza, Saurabh Verma)....Pages 295-309
Computational Intelligence in Vaccine Design Against COVID-19 (Kaushik Kumar Bharadwaj, Ankit Srivastava, Manasa Kumar Panda, Yengkhom Disco Singh, Rojali Maharana, Kalicharan Mandal et al.)....Pages 311-329
Front Matter ....Pages 331-331
Big Data Analytics for Understanding and Fighting COVID-19 (Sandhya Verma, Rajesh Kumar Gazara)....Pages 333-348
IoMT Potential Impact in COVID-19: Combating a Pandemic with Innovation (Mohd Faizan Siddiqui)....Pages 349-361
Advances in Intelligent Based Internet of Medical Things (IoMT) for COVID-19: Olfactory Disorders (V. Evelyn Brindha, X. Anitha Mary)....Pages 363-371
Integrating M-Health with IoMT to Counter COVID-19 (Devansh Sharma, Ali Zaid Bin Nawab, Mansaf Alam)....Pages 373-396
Digital Image Analysis Is a Silver Bullet to COVID-19 Pandemic (Sakshi Aggarwal, Navjot Singh, K. K. Mishra)....Pages 397-414
Non Linear Tensor Diffusion Based Unsharp Masking for Filtering of COVID-19 CT Images (S. N. Kumar, A. Lenin Fred, L. R. Jonisha Miriam, Parasuraman Padmanabhan, Balazs Gulyas, H. Ajay Kumar)....Pages 415-436

Citation preview

Studies in Computational Intelligence 923

Khalid Raza   Editor

Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis

Studies in Computational Intelligence Volume 923

Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Computational Intelligence” (SCI) publishes new developments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. The books of this series are submitted to indexing to Web of Science, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink.

More information about this series at http://www.springer.com/series/7092

Khalid Raza Editor

Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis

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Editor Khalid Raza Department of Computer Science Jamia Millia Islamia (Central University) New Delhi, India

ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-981-15-8533-3 ISBN 978-981-15-8534-0 (eBook) https://doi.org/10.1007/978-981-15-8534-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

This volume is dedicated to my beloved wife Farhat and my son Arham

Preface

The novel coronavirus disease 2019 (COVID-19) pandemic has posed a major threat to human life and health. The COVID-19 had been documented in more than 210 countries, directly affecting people’s life. There is a great crisis all over the world, and scientific communities are rigorously looking for instant solutions to deal with COVID-19 problems. Computational intelligence (CI), being powerful tools, can be applied to fight against COVID-19. This edited volume aims to provide recent research and developments in CI to fight against COVID-19. This edited volume has 22 chapters which is divided into five parts. First part contains two introductory chapters that cover the basic concepts of CI methods and its applications in surveillance, prevention, prediction, diagnosis and therapeutic of COVID-19; and basics of coronaviruses, its epidemic, viral structure, its genome and so on. Second part comprises four chapters which cover applications of CI in surveillance and prevention of COVID-19 infection. It covers how CI and social network analysis can be used to track the COVID-19 outbreak and identify key spreaders. Further, this section also covers mobile technology solutions and the role of IoT for surveillance and prevention of COVID-19 spread. Third part is a collection of seven chapters that demonstrate how CI is helpful in the prediction and diagnosis of COVID-19. It systematically presents available predictive systems and data models, comparative study of SIR prediction models and disease control strategies, particle swarm optimization (PSO) for prediction of COVID-19, epidemic data visualization and forecasting using Elasticsearch, medical image-based diagnosis of zoonotic COVID-19 infection. Fourth part has three chapters that attempt to present the role of CI in computer-aided drug design, drug repurposing using machine learning and vaccine design. Fifth part contains six chapters that extensively cover the role of big data, Intelligent Internet of Medical Things (IoMT) and image processing to combat deadly COVID-19.

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The contributing authors of this volume are experts in their fields, and they are from various reputed universities and institutions across the world. This volume is a valuable and comprehensive resource for computer and data scientists, epidemiologists, radiologists, doctors, clinicians, pharmaceutical professionals, along with graduate and research students of interdisciplinary and multidisciplinary sciences. New Delhi, India

Khalid Raza

Acknowledgements

The editor and contributors are thankful to all the anonymous reviewers for their valuable expert comments and suggestions on the chapters. Special thanks go to the series editor, editors, publication and production manager, and other editorial staff of the Springer Nature for their overwhelming support despite ongoing COVID-19 pandemic.

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Contents

Computational Intelligence and COVID-19 Preliminaries An Introduction to Computational Intelligence in COVID-19: Surveillance, Prevention, Prediction, and Diagnosis . . . . . . . . . . . . . . . . Khalid Raza, Maryam, and Sahar Qazi Role of Computational Intelligence Against COVID-19 . . . . . . . . . . . . . Simran Kaur and Yasha Hasija

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Computational Intelligence in Surveillance and Prevention of COVID-19 Infection Using Computational Intelligence for Tracking COVID-19 Outbreak in Online Social Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sahar Qazi, Salim Ahmad, and Khalid Raza

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Social Network Analysis for the Identification of Key Spreaders During COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ahmadi Hasan and Ahmad Kamal

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Mobile Technology Solution for COVID-19: Surveillance and Prevention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shaban Ahmad, Pragya Chitkara, Fatima Nazish Khan, Avtar Kishan, Vaibhav Alok, Ayyagari Ramlal, and Sahil Mehta

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The Role of Internet of Things (IoT) in the Containment and Spread of the Novel COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Ibrahim Babangida Mohammed and Salmi Mohd Isa

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Computational Intelligence in Prediction and Diagnosis of COVID-19 A Review on Predictive Systems and Data Models for COVID-19 . . . . . 123 Fatima Nazish Khan, Ayesha Ayubi Khanam, Ayyagari Ramlal, and Shaban Ahmad A Comparative Study of the SIR Prediction Models and Disease Control Strategies: A Case Study of the State of Kerala, India . . . . . . . 165 K. Reji Kumar Computational Intelligence Approach for Prediction of COVID-19 Using Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 R. S. M. Lakshmi Patibandla and V. Lakshman Narayana COVID-19 Insightful Data Visualization and Forecasting Using Elasticsearch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Hemant Kumar Tewtia and Deepti Singh Computational Intelligence Methods for the Diagnosis of COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 Sarra Akermi, Subrata Sinha, Surabhi Johari, Sunil Jayant, and Anshul Nigam Rapid Computer Diagnosis for the Deadly Zoonotic COVID-19 Infection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Peter Mudiaga Etaware Computational Intelligence Methods in Medical Image-Based Diagnosis of COVID-19 Infections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Abdullahi Isa Computational Intelligence in Drug Design and Drug Repurposing Computational Intelligence in Drug Repurposing for COVID-19 . . . . . . 273 Manish Kumar Tripathi, Sujata Sharma, Tej P. Singh, A. S. Ethayathulla, and Punit Kaur COVID-19: Hard Road to Find Integrated Computational Drug and Repurposing Pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Ankita Sahu, Sahar Qazi, Khalid Raza, and Saurabh Verma Computational Intelligence in Vaccine Design Against COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 Kaushik Kumar Bharadwaj, Ankit Srivastava, Manasa Kumar Panda, Yengkhom Disco Singh, Rojali Maharana, Kalicharan Mandal, B. S. Manisha Singh, Dipanjali Singh, Mohinikanti Das, Devasish Murmu, and Sandeep Kumar Kabi

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Big Data, Intelligent IoMT, and Image Processing to Combat COVID-19 Big Data Analytics for Understanding and Fighting COVID-19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Sandhya Verma and Rajesh Kumar Gazara IoMT Potential Impact in COVID-19: Combating a Pandemic with Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 Mohd Faizan Siddiqui Advances in Intelligent Based Internet of Medical Things (IoMT) for COVID-19: Olfactory Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 V. Evelyn Brindha and X. Anitha Mary Integrating M-Health with IoMT to Counter COVID-19 . . . . . . . . . . . . 373 Devansh Sharma, Ali Zaid Bin Nawab, and Mansaf Alam Digital Image Analysis Is a Silver Bullet to COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Sakshi Aggarwal, Navjot Singh, and K. K. Mishra Non Linear Tensor Diffusion Based Unsharp Masking for Filtering of COVID-19 CT Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 S. N. Kumar, A. Lenin Fred, L. R. Jonisha Miriam, Parasuraman Padmanabhan, Balazs Gulyas, and H. Ajay Kumar

Editor and Contributors

About the Editor Dr. Khalid Raza is Assistant Professor in the Department of Computer Science, Jamia Millia Islamia (Central University), New Delhi. He has been honored with “ICCR Chair Visiting Professor” at the Faculty of Computer & Information Sciences, Ain Shams University, Cairo, Egypt. He has over 10 years of teaching and research experience in the field of computational intelligence and its various applications. He has contributed over 70 research articles in reputed journals and edited books, including one sole-authored book, and he has reviewed over 150 research articles for reputed journals/conferences in the last 5 years. He has delivered several keynote addresses, invited talks, public lectures and seminars in national and international conferences, workshops and chaired technical sessions at various conferences. He has also executed two Indian government-funded research projects. He is Member of MIR Labs (USA), CSI (India) and SCRS (India). His research interest includes computational intelligence methods and its applications in bioinformatics, viro-informatics and health informatics.

Contributors Sakshi Aggarwal Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, India Salim Ahmad Department of Computer Science, Jamia Millia Islamia, New Delhi, India Shaban Ahmad Department of Computer Science, Jamia Millia Islamia, New Delhi, India Sarra Akermi Annotation Analytics Pvt. Ltd., Amarpura, Gurgaon, India

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Mansaf Alam Department of Computer Science, Jamia Millia Islamia, New Delhi, India Vaibhav Alok Department of Biosciences, Jamia Millia Islamia, New Delhi, India X. Anitha Mary Department of Robotics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India Kaushik Kumar Bharadwaj Department of Bioengineering and Technology, Gauhati University Institute of Science and Technology (GUIST), Gauhati University, Guwahati, Assam, India Pragya Chitkara Department of Computer Science, Jamia Millia Islamia, New Delhi, India Mohinikanti Das Department of Botany, College of Basic Science & Humanities, Odisha University of Agriculture and Technology, Bhubaneswar, India Peter Mudiaga Etaware Department of Botany, Faculty of Science, University of Ibadan, Ibadan, Oyo State, Nigeria A. S. Ethayathulla Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India V. Evelyn Brindha Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India Rajesh Kumar Gazara Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Roorkee, India Balazs Gulyas Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore Ahmadi Hasan Department of Mathematics, Jamia Millia Islamia, New Delhi, India Yasha Hasija Delhi Technological University, Delhi, India Abdullahi Isa Department of Mathematical Sciences, Faculty of Science, University of Maiduguri, Maiduguri, Nigeria Salmi Mohd Isa Graduate School of Business, Universiti Sains Malaysia, Penang Island, Malaysia Sunil Jayant Annotation Analytics Pvt. Ltd., Amarpura, Gurgaon, India Surabhi Johari Institute of Management Studies (IMSUC), Ghaziabad, Uttar Pradesh, India L. R. Jonisha Miriam Mar Ephraem College of Engineering and Technology, Elavuvilai, India

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Sandeep Kumar Kabi Environment & Sustainability Department, CSIR-IMMT, Bhubaneswar, Odisha, India Ahmad Kamal Department of Mathematics, Jamia Millia Islamia, New Delhi, India Punit Kaur Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India Simran Kaur Delhi Technological University, Delhi, India Fatima Nazish Khan Department of Computer Science, Jamia Millia Islamia, New Delhi, India Ayesha Ayubi Khanam Department of Computer Science, Jamia Millia Islamia, New Delhi, India Avtar Kishan Department of Computer Science, Jamia Millia Islamia, New Delhi, India H. Ajay Kumar Mar Ephraem College of Engineering and Technology, Elavuvilai, India S. N. Kumar Amal Jyothi College of Engineering, Kanjirappally, Kerala, India A. Lenin Fred Mar Ephraem College of Engineering and Technology, Elavuvilai, India Rojali Maharana Environment & Sustainability Department, CSIR-IMMT, Bhubaneswar, Odisha, India Kalicharan Mandal Environment & Sustainability Department, CSIR-IMMT, Bhubaneswar, Odisha, India B. S. Manisha Singh Environment & Sustainability Department, CSIR-IMMT, Bhubaneswar, Odisha, India Maryam Department of Computer Science, Jamia Millia Islamia, New Delhi, India Sahil Mehta International Centre for Genetic Engineering and Biotechnology, New Delhi, India K. K. Mishra Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, India Ibrahim Babangida Mohammed Graduate School of Business, Universiti Sains Malaysia, Penang Island, Malaysia; Department of Management and Information Technology, Abubakar Tafawa Balewa University, Bauchi, Bauchi State, Nigeria

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Devasish Murmu Environment & Sustainability Department, CSIR-IMMT, Bhubaneswar, Odisha, India V. Lakshman Narayana Department of IT, Vignan’s Nirula Institute of Technology and Science for Women, Guntur, AP, India Ali Zaid Bin Nawab Department of Computer Science, Jamia Millia Islamia, New Delhi, India Anshul Nigam Amity University Mumbai, Somathne, Panvel, Mumbai, Maharashtra, India Parasuraman Padmanabhan Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore Manasa Kumar Panda Environment & Sustainability Department, CSIR-IMMT, Bhubaneswar, Odisha, India R. S. M. Lakshmi Patibandla Department of IT, Vignan’s Foundation for Science, Technology, and Research, Guntur, AP, India Sahar Qazi Department of Computer Science, Jamia Millia Islamia, New Delhi, India Ayyagari Ramlal Deparment of Botany, University of Delhi, New Delhi, India Khalid Raza Department of Computer Science, Jamia Millia Islamia, New Delhi, India K. Reji Kumar Department of Mathematics, N. S. S. College, Cherthala, Kerala, India Ankita Sahu Tumor Biology, ICMR-National Institute of Pathology, New Delhi, India; Department of Computer Science, Jamia Millia Islamia, New Delhi, India Devansh Sharma Department of Computer Science, Jamia Millia Islamia, New Delhi, India Sujata Sharma Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India Mohd Faizan Siddiqui International Medical Faculty, Osh State University, Osh, Kyrgyz Republic Deepti Singh Jaypee Institute of Information Technology, Noida, India Dipanjali Singh Environment & Sustainability Department, CSIR-IMMT, Bhubaneswar, Odisha, India Navjot Singh Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, India

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Tej P. Singh Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India Yengkhom Disco Singh Department of Post Harvest Technology, College of Horticulture and Forestry, Central Agricultural University, Pasighat, Arunachal Pradesh, India Subrata Sinha Centre for Biotechnology and Bioinformatics, Dibrugarh University, Dibrugarh, Assam, India Ankit Srivastava Department of Ophthalmology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India Hemant Kumar Tewtia Birla Institute of Technology and Science, Pilani, India Manish Kumar Tripathi Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India Sandhya Verma Shri Vaishnav Institute of Science, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, Madhya Pradesh, India Saurabh Verma Tumor Biology, ICMR-National Institute of Pathology, New Delhi, India

Computational Intelligence and COVID-19 Preliminaries

An Introduction to Computational Intelligence in COVID-19: Surveillance, Prevention, Prediction, and Diagnosis Khalid Raza , Maryam, and Sahar Qazi

Abstract Coronavirus disease 2019 (COVID-19) has been declared as pandemic which took the lives of more than 500 thousand people till mid of 2020 worldwide. Since the coronavirus is highly contagious in nature, COVID-19 is spreading rapidly despite observing social distancing and taking other recommended precautionary measures. Computational intelligence, a powerful tool that mimics human intelligence and learns specific tasks using data, is widely deployed to combat the COVID19. This chapter briefly covers the computational intelligence methods and its applications in the surveillance, prevention, prediction, and diagnosis of COVID-19. Further, the limitation of current systems and prospects are also discussed. Keywords Computational intelligence · Machine learning · COVID-19 · Coronavirus

1 Introduction Coronavirus disease 2019 (COVID-19), also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first reported in China in December 2019, and it was announced as a global epidemic on 11 March, 2020 by World Health Organization (WHO). This virus is transmitted through humans to humans by coughing, sneezing, etc. Till the end of June 2020, approximately 10 million cases have been reported with more than 500 thousand death (https://www.worldo meters.info/coronavirus/). Currently, COVID-19 diagnosis is done using RT-PCR that is time-consuming, however, rapid antigen test (RAT) is also being deployed for COVID-19 diagnosis as it gives results as fast as 15–20 min and is pocket friendly. As far as prediction is concerned, many mathematical models have also been developed that can aid in predicting the risk of getting infected with the novel coronavirus (nCoV-19) [1]. For instance, the susceptible infected exposed recovered (SEIR) K. Raza (B) · Maryam · S. Qazi Department of Computer Science, Jamia Millia Islamia, New Delhi 110025, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 K. Raza (ed.), Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, Studies in Computational Intelligence 923, https://doi.org/10.1007/978-981-15-8534-0_1

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model has been extensively used to track the individuals in four different modes namely—(i) susceptible, (ii) infected, (iii) exposed to infection, and (iv) recovered from COVID-19 [2]. Life worldwide has changed drastically as COVID-19 progressed from early 2020 to mid-2020. Wearing masks and gloves, maintaining social distance, and sanitizing hands regularly are the new normal globally. Since most of the hospitals have been converted into COVID-19 centers, medical consultants are deploying tele-based healthcare for periodic check-ups, diagnosis, prognosis, and treatment that can be easily achieved at the comfort of their couches [2, 3]. Also, patients with co-morbidities namely—diabetes, hypothyroidism, high blood pressure, heart problems, etc. are monitoring their healthcare using smart biosensors at their homes [4]. Researchers have also developed some specific biosensors for tracking COVID-19 namely—(i) Chip-based biosensors, (ii) Film-based biosensors, (iii) Paper-based biosensors, etc. [5]. Furthermore, government agencies have been tracking and keeping an eye on the spread of this infection since early 2020. The best way for surveillance is by tracking the online social networks (OSNs) of every user from various geographic regions. Since COVID-19 has been highlighted by the local and the global media, people have been updating their personal accounts be it on Twitter, Facebook, Whatsapp, etc. [6] by using hashtags—“coronavirus”, “COVID-19”, “China”, “Wuhan”, “Epidemic” etc. These updates have been retrieved by various researchers to predict the geographic location where the coronavirus infection is highly likely or less likely [7, 8]. All these utilities that exist already or are being developed have a strong underlying basis of computational intelligence (CI)-based algorithms that work like humans however, reduces the human intervention to a great extent. With the progression of such a cataclysm, nations have realized how much we lack in surveillance, prevention, prediction, diagnosis, and prognosis of diseases to a larger extent in such critical situations. Nevertheless, computational intelligence (CI) is a blessing and has paved a way for the standard healthcare which incorporates the four essential dimensions in crucial situations like the current COVID19 scenario. So, researchers are moving ahead with the concept of computational intelligence for combating the lethal COVID-19 disease. Computational intelligence refers to a branch of computer science that incorporates the trained algorithms that have been developed by taking inspiration from human intelligence to make suitable decisions in complex problems by eliminating human interference [9]. As far as the current pandemic COVID-19 is concerned, CI-based approaches have been broadly deployed in mainly four dimensions namely—(a) Surveillance, (b) Prevention, (c) Diagnosis, and (d) Prediction. Figure 1 depicts the essential four dimensions of computational intelligence that has been effectively deployed in combating and overcoming COVID-19. Computational intelligent systems comprise of myriad techniques such as— machine learning, deep learning, fuzzy logic systems (FLS), evolutionary algorithms (EA), hybrid algorithms (HA). Each of these learning strategies leads to an exceptional outcome [10]. CI-based approaches have the ability to identify highrisk patients and also predict mortality risk by satisfactorily evaluating the previous

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Fig. 1 Four dimensions of computational intelligence in combating COVID-19

data of the patients [10]. CI algorithms can work on any type of data –visual, audio, genomic sequenced data, geographic data, from medical devices, and is easily retrievable from hospital servers, electronic health records (EHR), and wearable devices. Presently, all the medical data is being stored and retrieved on cloud-based systems in real-time allowing rapid utility for various purposes [4, 11]. The purpose of this Chapter is to present an introduction to computational intelligence and how it can be better utilized to combat COVID-19 in the four major dimensions, including surveillance, prevention, prediction, and diagnosis.

2 Overview of Computational Intelligence Methods Computational Intelligence (CI) allows the study and forming a strong understanding of real-life complex problems by taking advantage of computational resources. It is an umbrella term and comprises of widely popular computational paradigms [12]. It mainly pays to heed on the regular cycle of learning, adapting, and evolving, but it relies heavily upon numerical information provided to it. As defined by the IEEE Computational Intelligence Society, Artificial Neural Networks (ANNs), Fuzzy Logic Systems (FLS), and Evolutionary algorithms(EAs) combine to form the main skeleton of computational intelligent systems (CIS),

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Fig. 2 Computational intelligence methods

howbeit; it also encompasses techniques like Swarm intelligence, Fractals and Chaos Theory, Artificial immune systems, Wavelets, and so on. CI algorithms are widely appreciated and used because these approaches utilize statistical methods in an extensive manner. Figure 2 demonstrates some of the prominent techniques used in CI systems. CI systems try to mimic natural beings to empower their functioning with rationality, adaptation, and learning [13].

3 Computational Intelligence in COVID-19 Surveillance and Prevention Since coronavirus spreads through human-to-human contact, safety measures are mandatory to control the spread of COVID-19. It includes the effort to break this chain of transmission, involving contact tracing, i.e., surveillance. Essential measures to minimize the spread and management of the disease include, as part of a holistic approach, case detection, separation, diagnosis and treatment, and contact tracing and quarantine [14]. Surveillance is simply an organized gathering, review, and distribution of health data for the planning, execution, and assessment of public health programmes [15–17]. Established surveillance systems should be periodically checked based on clear requirements for effectiveness, expense, and efficiency [18,

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19]. The effectiveness of the surveillance is calculated by whether it leads to avoidance or regulation or a greater understanding of negative health events. The strength of the surveillance is its ability to track safety incidents. Surveillance has been an essential policy that is deployed by every nation to prevent COVID-19 as it helps in prophesying the long-term transmission trends in the populace along with virus evolution [17, 20, 21]. Any disaster management, for instance, the current coronavirus pandemic, requires regular checks and monitoring of myriad geographical regions. The best technology for maintaining a track is by deploying Geospatial Technology that is inclusive of the Geographic Information System (GIS), Remote Sensing (RS) and Global Positioning System (GPS) and are highly effective as they allow making accurate and timely decisions concerning natural resources in the times of cataclysms [22]. The computationally intelligent technologies take actual data as input from various geographic locations and analyses them accordingly to predict the models of the disaster aftermath. Moreover, the high-quality geospatial images provided by satellites revolving around the Earth can aid in modeling and understanding the dynamics of the virus. Once a model is developed, the satellite images can further be used for surveillance, prediction of the disease in areas, tracking, etc. (https://www. planet.com/pulse/how-satellite-data-can-help-with-covid-19-and-beyond/). All such measures have been useful in monitoring contact tracing and also keep a track of the disease spread. To prevent the local transmission, lockdowns were the very first measures which were deployed worldwide. As far as national lockdown in India is concerned, there were mainly 5 phases. Unfortunately, the local transmission could not be controlled [23]. Several countries have come up with vital surveillance strategies to cope with and track transmission [24]. A few examples of successful surveillance models are as follows (Fig. 3).

3.1 GIS System Model South Korea’s government deployed the GIS system to give the accurate originality of the COVID-19 scenario, and it was able to identify the hotspots/red zones and predicted high susceptible and exposed areas in the country. Also, the GIS-based strategy of tackling the current pandemic was useful as it provided information to the general public about the medical facilities and pharmacies near them. With such advanced information, the country was able to develop efficient and effective plans of quarantine and manage the entire healthcare crisis easily with less chaos [25].

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Fig. 3 Various surveillance models used by different nations to track the spread of COVID-19

3.2 GPS-Based Models The Indian government has recently launched a new application named— “AarogyaSetu” that is a contact tracing digital service aiding in monitoring the health status of Indian citizens concerning COVID-19, self-assessment capability, wherein the user can him/herself check for signs of COVID-19, allows E-pass availability (if the user has applied for it already) and provides with the latest updates on COVID-19 (https://www.mygov.in/aarogya-setu-app/). Another GPS-based application to track COVID-19 is “TraceTogether” developed by the Singaporean government for contact tracing that uses Bluetooth (https://www.tracetogether.gov.sg/). Stanford University in collaboration with the app developers have developed an application named— “COVID Watch” and uses Bluetooth signals to identify potentially infected individuals when they are in close proximity to one another (https://www.covid-watch. org/).

3.3 Color-Code Model China and India are two nations that have deployed a color-coding scheme to highlight the susceptible, infected, and exposed and containment individuals/regions. China

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has collaborated with Alibaba groups and has launched a new payment application named—“AliPay” wherein Chinese users get a QR code that represents their health status concerning COVID-19. The users have to simply fill a form that comprises details such as –ID number, recent travel history, and probable co-morbidities. Once they submit the online form, a QR code is generated accordingly. India, on the other hand, has used the color-code model differently. Unlike the Chinese government, the Indian government employed the model for marking the various regions—red zone (highly infected zone), orange zone (clustered cases only), and green zones (COVID-19 free zones).

3.4 IoT-Based Model Internet of Things (IoT), along with other prominent technologies like CI, cloud computing, big data analytics, is an important tool for COVID-19 surveillance and prevention. Overlaying GIS on IoT mobile data can help epidemiologists identify non-infected people and may trace people who may have come in contact with infected patients. It can also help in monitoring high-risk patients, track quarantine, pre-screening diagnosis, cleaning and disinfecting, reducing in-home infection, and helping healthcare workers to take necessary actions [26]. Some of the IoT devices used to monitor the pandemic are smart wearable devices [4], connected thermometers, IoT buttons, and other sensing devices [27, 28]. As far as prevention of disease is concerned, CI is useful for disease prevention and prediction as it holds the potential to determine the symptoms, causes, and factors responsible for infection and provides insights to fight against viral diseases, including COVID-19. With real-time data analysis, CI systems aid the prevention of diseases. It can be further be used to predict infected organs in the body, the viral entry, necessity for patient beds, and medical staff that is required during a crisis [29].

4 Computational Intelligence in COVID-19 Diagnosis and Prediction COVID-19 has become a global pandemic and a huge amount of RT PCR testing kit is required for the diagnosis, especially for a populated country like India. Hence, computation intelligence methods, being powerful tools for training and calibrating a prediction model, can be utilized as a cheaper and faster option to COVID-19 diagnosis [30]. Researchers around the globe are able to distinguish between SARSCoV and COVID-19. Salivary based diagnosis seems effective in the virus. Apart from molecular assays and test CT of chest help physicians to look over the severity

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and response to treatment [29]. X-ray is considered as the first line of imaging to diagnose COVID-19. It is less sensitive in comparison to 3D chest CT images [31]. As far as CI-based diagnosis is concerned, medical imaging like X-ray, CT-scan, etc. is widely utilized for the COVID-19 diagnosis purpose. CT report of COVID19 patient is divided into 4 stages. During 0–4 days, ground-glass opacity (GGO) is observed in lower lobes bilaterally or unilaterally. The 5–8 days is a progressive stage where diffuse GGO crazing pattern and consolidation are noticed in bilateral multilobes. During peak stage, which is more common (9–13 days), there is dense consolidation. After 14 days (absorption stage), crazy paving pattern and consolidation are absorbed and only GGO is left. These patterns help in the efficient classification of COVID-19 cases [31]. Many researchers and computational biologists have developed detection systems using CI-based approaches mainly—deep learning (DL), machine learning (ML), convolutional neural networks (CNNs), etc. to diagnose COVID-19 [32–37]. Most of these prediction models use CT scans, radiographs, Xrays of the chest of the infected patients [38]. Lunit, a French teleradiology company that provides AI solutions to hospitals, has developed an AI solution for the analysis and interpretation of COVID-19 during patient triage and monitoring by simply examining chest X-rays (https://www.lunit.io/en/). Table 1 depicts the most effective CI-based prediction models that have been developed specifically for COVID-19.

5 Other Aspects: CI in Therapeutic and Treatment At present, there are no promising therapeutics available for COVID-19. The Hydroxychloroquine and chloroquine are antimalarial drugs that work on the inhibition of DNA and RNA polymerase enzyme and ACE2 cellular response [49]. Some supportive drugs like tocilizumab, siltuximab, metronidazole, NSAID, etc. are used with regular drugs and even convalescent plasma therapy is seen to be effective in critically ill patients (https://clinicaltrials.gov/). Around 13 traditional Chinese herbal medicines were also reported to contain anti-COVID-19 compound and at present, four different types of vaccines like 1273 mRNAs are under clinical trial [29]. IgA is considered a novel therapeutic antibody that does not have an Fc receptor binding site and may form polymer as mucosal vaccine secrete IgA which might stimulate immune response and can be a promising therapeutic approach for the treatment of SARS- CoV-2 [50, 51]. CI is considered as the most decisive tool which helps to identify potential drugs in a limited period [52–55]. Some of the studies ruled out the solution for the most common problem related to ventilator setting for the patients. In their studies, they analyzed different methods to determine pressure-volume curves in artificially ventilated patients suffering from COVID-19 and deployed CI strategies to rectify the method for a given pressure-volume curve and also help to predict the volume of given pressure-volume, method, and even patient data [56–62]. This advanced technique would be very useful in the recent future for the treatment of respiratory diseases

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Table 1 CI-based prediction models for COVID-19 S. No.

Name

Description

References

1.

COVIDiagnosis-Net

A deep Bayes-Squeeze Net model that uses the chest X-rays of COVID-19 patients for diagnosis

[39]

2.

Infervision

An AI-based startup that helps in diagnosing COVID-19 with specific lung depth features

https://www.itnonline.com/ content/infervision-frontl ines-against-coronavirus

3.

COVID-SegNet

A deep convolutional neural network for segmenting the COVID-19 chest CT visuals

[40]

4.

Thoracic VCAR

A software heavily utilized by radiologists for qualitative measurement of respiratory tract and lungs

[41, 42]

5.

COVNet

A neural network model that [43] works on chest CT images to detect COVID-19

6.

Prognostic prediction algorithm

An AI-based algorithm that predicts the death risk of an infected person

7.

AI-based diagnosis

Uses CT scans and X-rays to [45] screen COVID-19 patients

8.

Deep learning-based model

Uses CT scans and X-rays to [46] diagnose COVID-19

9.

Deep convolutional network

Uses chest radiographs as input and screens for COVID-19

10.

CNN based VBNet

Screens for COVID-19 using [48] CNN transfer learning by taking X-rays as input

[44]

[47]

like COVID-19 [63]. The Benevolent AI (https://www.benevolent.com/), a Londonbased drug development startup, utilized AI to discover new drugs for several serious diseases including COVID-19, and discovered six promising molecules for COVID19 which include baricitinib (https://clinicaltrials.gov/). Nuritas is a biotechnology company in Dublin, Ireland is trying to identify therapeutic peptides through CI and genomics for treatment of SARS-CoV-2 (https://www.nuritas.com/). AI-VIVO is a leading company that combines CI and system pharmacology and identified Dexamethasone in April 2020 as a promising drug to fight against COVID-19 (http://www. aivivo.co/). Gero (https://gero.ai/),a Singapore-based company, identified 9 potential drugs with the help of CI, including Niclosamide and Nitazoxanideshowing promising efficacy against COVID-19 [64].

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Apart from drug treatment CI help in mental and psychological treatment too. Due to extreme pressure during COVID-19 pandemic patient show some psychosomatic symptoms which can be cured by the concept of virtual reality which can be quiet helpful too [65]. Apart from managing psychosomatic symptoms virtual reality also helps to manage the pain. Virtual reality helps in the rehabilitation of patients with a shorter period with satisfaction feeling in the patient [65]. UW Medicine Information Technology Services (UWMITS) was the first CI-based healthcare management platform in the United States which helps in the treatment of COVID-19 which was started in the last of February 2020 [66]. This technology would be helpful if people would share the skillful response which will be helpful during the global public health emergency condition [6]. Digital Learning package was developed in the UK by a group of health workers to maintain the psychological condition of workers during the COVID-19 pandemic [67]. This learning package aims to provide support to healthcare workers working in community settings or hospitals within the release of just 7 days [41]. A list of companies leveraging AI for the drug repurposing and development of new drugs for COVID-19 treatment are listed in Table 2. This list of identified drugs is still under in vivo, in vitro, and/or clinical trials.

6 Limitations of Current Systems and Future Challenges Computational intelligence (CI) had shown promising potential with the initiative of Digital Mammography DREAM challenge which was based on CI based algorithms by reviewing around 640,000 digital mammograms. Although various CI-based systems are deployed to track, check, monitor, and manage the current pandemic of COVID-19, there are still a few limitations of CI that need to be resolved [69–72]: The need for large datasets: Without large datasets, CI algorithms are usually less reliable. Hence, to track, prevent, predict, and diagnose COVID-19, we need large datasets for training and model calibration. Black-box in nature: Most of the CI-based methods, including ANN, and deep leaning, lack visibility as to how results are determined, which is termed as “Black box model”. Privacy Issues: With the advancing technologies in CI systems deployed for medical healthcare, people have trust issues. They tend to believe that their private data may get misused by researchers for their own benefit. However, there is an urgent need to gain people’s trust in CI systems and must initiate awareness about its pros and cons. The onus lies on the shoulders of medical researchers and healthcare affiliations to bring awareness about the positives of CI systems in the general public [73]. CI has not only enhanced the quality of diagnosis, treatment, and management of disease but also contributed equally in the field of biomedical research including regenerative medicine, gene editing, and 3D bioprinting improved medical education, and curriculum as well [4, 10]. CI also helps us to understand human biology, and it

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Table 2 List of companies leveraging AI for drug repurposing and novel drug development for COVID-19 treatment S. No.

Companies

Development type

Description

Companies URL

1.

BenevolentAI

Drug repurposing

A UK-based company determined six most probable candidates including baricitinib, a rheumatoid arthritis drug.

https://www. benevolent. com/

2.

Cyclica

Drug repurposing

A Canadian firm used its AI-based drug repurposing platform, “MatchMaker” which uses a deep learning engine to screen the drug library.

https://www. cyclicarx.com/

3.

Deargen

Drug repurposing

A Korean AI firm, in https://dearge collaboration with Dankook n.me/ University, predicted atazanavir (a drug for HIV treatment) using AI-platform including few more drugs.

4.

Gero

Drug repurposing

A Singapore-based firm discovered nine drugs using its AI platform including niclosamide and nitazoxanide (anti-parasitic and anti-viral drugs) as high efficacy against COVID-19

5.

Healx

Drug repurposing

A UK-based firm utilized its https://hea own AI platform, called lx.io/ “Healnet”, to discover bi-and tri-combinations of existing drugs leveraging data on why death is greater along with comorbidities

6.

IDentif.AI

Drug repurposing

A project led by the National [68] University of Singapore to come up with an AI platform that can rectify a combination of different drugs

7.

Innoplexus

Drug repurposing

An Indo-German firm applied its AI platform and found higher efficacy for three combinations: (i) chloroquine with tocilizumab, (ii) chloroquine with remdesivir, and (iii) hydroxychloroquine with clarithromycin or plerixafor

https://gero.ai/

https://www. innoplexus. com/

(continued)

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Table 2 (continued) S. No.

Companies

Development type

Description

Companies URL

8.

VantAI

Drug repurposing

A New York-based firm utilized systems biology and AI approach to apprehend interplay between viral proteins with over 500 human proteins for drug repurposing

https://www. vant.ai/

9.

Insilico Medicine

New drugs

A Hong-Kong based firm https://insilico. utilized AI and virtual reality, com/ and published structure of 100 small drug molecules against COVID-19, out of it 10 are reported as most promising

10.

Exscientia

New drugs

A UK-based firm planned to https://www. screen all FDA approved and exscientia.ai/ investigational drugs (~15,000 clinical molecules) against key COVID-19 targets

11.

Iktos and SRI International

New drugs

A French AI firm Iktos https://www. (using deep generative sri.com/ models)partnered with US-based SRI Biosciences to identify drug candidates for COVID-19

can be combined with Human Intelligence in the future [10, 74, 75, 76, 77]. In the future, there is a possibility that CI and Human Intelligence based assisted robots may replace physicians for a better disease treatment which could be cost-effective and strengthen the field of medicine [78].

7 Conclusion The COVID-19 is the global health problem that is affecting not only the life of the human but also the economy of the globe. Computational intelligence is a modern field and is a powerful tool for predictive analytics, playing a significant role in combating the deadly COVID-19. In this chapter, we presented the role of CI in four different aspects to fight against COVID-19, including surveillance, prevention, prediction, and diagnosis. We have seen that CI has revolutionized the way of surveillance during epidemics, devising new preventive measures, diagnosing and

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treatment, howbeit with persistent limitations too. Aggressive research needs to done to overcome the limitation of CI so that it can be helpful to fight future pandemics.

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Role of Computational Intelligence Against COVID-19 Simran Kaur and Yasha Hasija

Abstract The recent COVID-19 pandemic caused due to the notorious SARS-CoV2 virus has caused widespread loss of human lives across the globe. It has affected more than 213 countries, with a count of 11,669,259 cases and 539,906 deaths, as per WHO report on 9 July 2020. The epidemic has brought the world to a halt and has pointed out the shortcomings of the healthcare system and the flaws in epidemic management. The traditional ways of healthcare management have collapsed under these exigent circumstances. In these trying times, there is a dire need for better implementation of available resources and technology to accelerate the management of the pandemic. Hence, a systematic and thorough assessment of available technology, resources, tools, and techniques will point us in the right direction for finding potential solutions for the control of the severity or spread of the disease and ultimately finding a cure. The use of the advanced field of computational intelligence, which includes Artificial Intelligence, Machine Learning, and Big Data analytics, for clinical/healthcare data can serve substantial solutions in the current scenarios. This chapter aims to discuss the role of AI, ML, as well as Big Data analytics, in healthcare and epidemic management. The chapter elaborates on various applications of computational intelligence in speed tracking of spread, identifying patients with critically low immunity/high-risk patients, assistance in treatment and diagnosis, controlling the spread, and future predictions using the current dataset. The chapter addresses a significant application of Computational intelligence in the research area of drug discovery and repurposing in the hunt for a cure. It briefly discusses technologies like Blockchain and AI-empowered image acquisition that have proved their significance in diagnosis and treatment in various countries, globally. The chapter proposes short-term action plans for combating the virus using computational intelligence while outlining future strategies of applying CI in healthcare management and its long-term benefits.

S. Kaur · Y. Hasija (B) Delhi Technological University, Delhi, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 K. Raza (ed.), Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, Studies in Computational Intelligence 923, https://doi.org/10.1007/978-981-15-8534-0_2

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Keywords COVID-19 · Artificial intelligence · Machine learning · Computational intelligence · Big data analytics

1 Introduction COVID-19, an epidemic caused by a novel coronavirus, SARS-CoV-2, originating from Wuhan, China, in late December 2019. The pandemic, which emerged at the beginning of 2020, has affected more than 213 countries, with 11,669,259 cases and 539,906 deaths globally, as on 9 July 2020. COVID-19 has impacted physical health as well as mental wellbeing and brought many unexpected changes in our lives. The ripple phenomenon of the disease has caused a drop in the economy, and healthcare systems are failing, the education sector is disturbed, businesses are collapsing, etc. Various developed, as well as developing countries, are facing recession and critical fall in the GDP of the country. The situation has raised concerns in the global market as well. This pandemic has pointed to the shortcomings of our healthcare system as well as epidemic management. WHO has released 170 situation reports as of 9 July 2020, sticking to the fact that virus spread can be controlled by following strict measures like early detection, isolation, treatment, and promoting social distance among citizens. This pandemic is not the first one to affect mankind; we have seen such pandemics in the past as well. The Black death during 1346–1353, which was caused by bubonic plague, led to the death of around 200 million, cholera pandemic between 1852 and 1860 caused the death of nearly 1 million, H2N2 flu during the late nineteenth century and mid-twentieth century costed lives of 1 million and 2 million people respectively, the Flu pandemic in 1918 also led to a death toll of 20–50 million people, HIV/AIDS epidemic (2005–2012), at its highest, caused the death of approximately 36 million people. Viral epidemics like SARS-CoV (2002–2003), H1N1 flu (2009), and MERS-CoV have infected and affected the human population in various ways, causing approximately 916, 150,000, and 800 deaths, respectively. Novel diseases resulting from RNA viruses will continue to emerge and impact us in the future as well. Despite two recent SARS and MERS pandemic in the past, the world was still underprepared for COVID-19. These pandemics and devastations caused by them have called for the application of Computational intelligence in healthcare as well as epidemic management. Traditional methods used in healthcare require human resources, which involves risky exposure to viruses, drugs, and patients, as well as takes an uncertain amount of time to reach a conclusion. Figure 1 shows numbers of cases and deaths reported in every 10 days. It is clear from the graph that, despite high transmission rate, mortality rate of the virus is low. Proper testing, preventive measures like isolation of infected patients, treatment, etc might able us to control the pandemic. However, performing this in real-time requires healthcare staff, security staff, lab technicians, etc. There are other problems like ill-management of data, inaccuracy, inefficient, and delayed testing. The trend-lines in the graph shows that the same trend will be followed in the coming future as well, indicating surge in

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Fig. 1 Trend-line graph depicting number of confirmed cases and deaths every 10 days. Trend-lines (dotted) in the graph show current trend and predicted trend in number of cases and deaths

cases. Hence, it becomes important to apply computational intelligence techniques in COVID-19. Computational intelligence analyses problems in real-time. This field includes Artificial intelligence, Big Data, data acquisition, Machine learning, and other intelligent fields. CI can be applied in patient screening, predictions, announcements, planning improvement based on similar datasets from the past. Drug development is a time-consuming process and does not ensure results; hence the application of Artificial Intelligence in the development and repurposing of the drug is very crucial to develop a cure for this disease. Tools like ML can be applied in determining the spread of infection using public interactions on social media platforms. This will evaluate the contagious nature of the disease, hence can predict the spread of the pandemic in real-time as well as predict the spread of infection in the future. Some examples of incorporating technology in Data management and assisting in further research on the pandemic are as follows—OpenWHO launched a web-based platform, Introduction to Go.Data which includes a collection of data field, understanding transmission chains, and follow-up of contacts or contact tracing. This tool helps in collecting, investigating, and tracing the spread of disease. WHO launched two tools on 10 April 2020, Health workforce assessment tool for estimating demand of health workers against numbers and severity of cases and Adapt tool for planning assistance during the surge capacity, also listing infrastructure and human resources required. WHO launched ACT Accelerator on 25 April 2020 to allow scientific access to new COVID-19 diagnostics and treatments. The chapter will discuss various forms of CI and their application in different aspects of research on COVID-19. A brief discussion on the biological aspect of the disease is followed by understanding the

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application of Computational intelligence in that particular field. A separate section for each computational intelligence technique has been listed under the CI section, later in the chapter.

2 Sources of Transmission While studying SARS-CoV-2, we came across a lot of theories about its origin. Andersen et al. [1], explained that SARS-CoV-2 might have undergone a process of natural selection just before its zoonotic transfer, or it could have undergone natural selection after its zoonotic transfer to a human. Analysis of data in various countries pointed out that direct contact is a necessity for the spread of the virus. It has been found that bats are natural hosts to coronaviruses [2], and the source of coronavirus to humans is also the bat reservoir [3]. There is no clarity on the transmission of the virus from bat populations to animals/human population. In early 41 cases, 14 cases had no contact with the seafood market, from which the spread of the virus is suspected [4]. Another report suggested that 5 out of 7 early cases had no association with the infected area [5, 6]. Hence, the virus did not originate in the seafood market of Wuhan, nor was the only site for the outbreak. It might have either been imported and amplified in the region or resultant of a lab accident/leak. According to Liu et al. [7, 8], this pandemic spreads via contact with the respiratory droplet. According to the WHO report on 29 March 2020, COVID19 is transmitted via close contact with a patient or via the contaminated surfaces through indirect contact. There might be a chance of airborne transmission in the case of aerosol generation, for example—bronchoscopy, tracheostomy, etc. Hence, precautions for airborne transmission are necessary. Besides direct transmission, the fecal-oral transmission is also being studied. Zheng et al. [9] isolated species of SARS-CoV-2 from a sample of the stool of an infected person. Tissues of 205 patients were tested using RT-PCR, and 32% of pharyngeal swabs and 29% of fecal samples were found to be positive [10]. Rectal swabs could easily show results even after nasopharyngeal swabs showed negative results. For understanding the zoonotic transmission of 2019-nCoV, comparison studies are conducted between the three coronaviruses, which have caused outbreaks in recent past—SARS-CoV-2, SARS, and MERS, as these fall under the same category and have a similar mechanism of action on host’s body. The SARS-CoV virus and MERS virus are bat-originated viruses, which then moved on to mammals, further transmitting to humans. Himalayan palm civet and dromedary camel were mammalian hosts for the two, respectively. All the literature studied suggests that bats are a reservoir for coronavirus, but there is a missing link between the natural host (bat) to intermediate hosts (mammals), eventually leading to human-human transmission (Fig. 2). It is difficult to find the missing link using regular methods. Applying CI techniques to study transmission rate and calculate R0 value might help in speeding up process of tracking. CI can be used to study the transmission cycle

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Fig. 2 A graphic depiction of transmission cycle of SARS-COV-2 from reservoir host to humanhuman transmission

in order to find other hosts and carriers. Some techniques and application of CI in studying the complex transmission of the virus is listed below.

2.1 Application of CI to Understand Transmission of COVID-19 Mathematical models, along with computational intelligence techniques, can be used to infer transmission chain and transmission rate, which will help in making plans to reduce the effect of the pandemic. Various mathematical models like the Bats-HostsReservoir-People (BHRP) the transmission network model (BHRP), the SusceptibleExposed-Infectious-Recovered (SEIR) model, etc. can be applied in the calculation of basic reproduction number (R0) [11]. BHRP transmission network model given by Chen et al. [12] estimated an R0 of 2.30 from a reservoir to a person. However, R0 increased to 3.58 from person to person transmission. The author assumed that COVID-19 first occurred in bats and then populated in the city. SEIR model was based on public health data. It suggested R0 of 6.47, indicating the seriousness of the outbreak [13–16]. By studying Time-dependant Dynamic Model (TDDM) and taking the contact rate as a function of time, Tang et al. [13] suggested that R0 can be lowered using appropriate strategies. The conceptual model is given by Lin et al. [17] estimated an R0 value of 2.8 whereas the Migration and Emigration model by Wu et al. [18–20] gave an R0 value of 2.68. Another research work identified potential strains that could jump from reservoir host to human host using ML tools [5].

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3 Spectrum of Disease SARS-CoV-2, among 7 other Corona viruses, is the most fatal and widespread, followed by SARS and MERS. Other viruses of this class like HKU1, NL63, OC43, NL63, OC43, and 229E pose very little threat to infected persons causing mild symptoms only [21]. The incubation period of the disease can be from 3 to 7 days to up to 2 weeks [5]. Studying the CI data, it was observed that the number of patients doubled every week, indicating that each patient transmits the virus to 2.2 individuals, i.e. basic reproduction number is 2.2 [22]. In the case of the SARS-CoV epidemic earlier, it was nearly 3 [23]. The peak of viral load is observed at 5–6 days after the symptoms are visible, with nearly 104–107 copies/mL. The highest viral load is found in the throat and sputum, i.e. 7.99 × 104, and 7.52 × 105 [24]. Viruses of this class cause several complications, including an impact on respiratory, hepatic, and neurological disorders in humans as well as other mammal hosts. Similar to the action mechanism of SARS and MERS virus, it leads to viral pneumonia by causing a lower respiratory system infection in severe cases leading to organ failure as well [25]. As per UN policy brief on COVID-19 and WHO report, mental health concerns have risen, especially 11–15-year-olds. Another case report notifying Multi-system inflammatory syndrome (MIS) in people aged under 14, temporally related to COVID-19, was published on 20 May 2020. The severity of symptoms decides the level of the disease or infection. Wu et al. [18–20] and Cascella et al. [26], listed clinical manifestations concerning the disease, as suggested by Chinese CDC are as follows: Mild Disease The patients who fall under this category show symptoms like a viral infection in the upper respiratory tract, including dry cough, low fever, congestion, body pain (mainly muscle pain), dyspnea absence, and radiograph features. 81% of cases of SARS-CoV-2 fall under this category. Patients under this category quickly move to other stages. Severe Disease These patients show severe pneumonia, ARDS, and sometimes septic shock. This is classified by severe dyspnea, respiratory distress, greater than 50% lung infiltrates in a span of 24–48 h or tachypnea. 5% of patients can lead to critical respiratory failure. Critical Disease This is the most critical stage. This is accompanied by multi-organ dysfunction resulting as a consequence of dysregulated host response to infection. Patients who are in the septic shock stage show hypotensive behavior. They might hold a level of serum lactate more than 2 mmol/L. Amongst various impacts, severe pneumonia, ARDS, sepsis, and septic shock are also observed in COVID affected patients. Patients with medical concerns had shown a low survival rate, with diabetes mellitus and coronary heart disease pointing out a link with more number of deaths [27, 28]. It was found that ferritin levels reportedly increased in infected patients [29]. While evaluating HScore, patients should also be

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screened for symptoms of hyper-inflammation using ferritin levels, platelet count, etc. [30]. Understanding the immune response is a vital part of the understanding mechanism of action of the virus designing drugs and curating therapies. Coronavirus mainly affects the epithelial cells in the respiratory tract, and gastrointestinal tissue is the primary target. Hyperimmune response leads to a cytokine storm, which results in tissue damage, organ dysfunction, and fever. The rise in various inflammatory parameters like IL-2, IL-7, etc. is responsible for cytokine storm. This can induce cytopenia, fever, pulmonary and cardiac involvement like ARDS [30]. The major parameter responsible for the reaction is IL-6 that is made by activated leukocytes. IL-6 has major pro-inflammatory roles but also has anti-inflammatory effects. Qin et al. [31, 32] reported that many severe cases reported a high ratio of neutrophil-lymphocytes, lower percentages of basophils, eosinophils and monocytes, and elevated inflammatory biomarkers. However, a reduction in the number of T cells, B cells, and NK cells is observed. It can be inferred from various papers that SARS-CoV-2 affects different organs of the body and manipulates their functioning. Authors suggest that patients who can detect COVID-19 at an early stage, like mild stage, might have better chance of survival. To diagnose the infection at an early stage, regular monitoring of body functions is necessary. Regular physical check-ups are not possible, hence alternatives like IoT, CI or smart equipments can be used to monitor health of the citizens.

3.1 Application of IoT in Point-of-Care (PoC) Monitoring As seen in Fig. 3, COVID-19 affects various organs and organ systems of our body. The use of smart devices or devices incorporating biosensors can be used to keep a check on the patient’s health profile [33]. Biosensors are simple to operate, having high sensitivity, and allow integration with different functions by using the same chip [34]. Interferometric reflectance imaging (IRIS) is applied in Quantification of accumulated bimolecular mass. It has flexible properties that help in diagnostic studies, for example, immunoassays, biomolecular kinetic studies, etc. [11, 35]. As per Qazi et al. [36], personalized medicine is in demand. Radio-frequency identification-tagged gelatinous capsule (RFID) is one such example of the application of biosensors to study drug adherence and side-effects on the body. It maintains connectivity with the user while passing information to a cloud server [37]. In the case of COVID-19, such devices can be beneficial, as the patient-doctor ratio is very high, and hence, monitoring each patient is difficult. However, using IoT devices can help in providing personalized treatment to patients without any exposure to the virus. Biosensors can be combined with Artificial intelligence, Machine learning, big data analytics, which will help doctors study and observe a patient’s treatment more carefully [11].

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Fig. 3 Impact of SARS-COV-2 on various organs of human body. Source Wadman et al. [38], Dariya and Nagaraju [39], Hu et al. [40], Akhmerov and Marban [41], Liu et al. [7, 8], Zeng et al. [42], McIntosh [43], Wong et al. [44], Zaim et al. [45], Li et al. [5], Tang et al. [13–16], Mao et al. [46], Zhou et al. [27, 28], Rajkumar [47], Wang et al. [48], Xiao et al. [49]

4 Structure of Coronavirus Coronavirus is a part of the Coronaviridae family, in Orthocoronavirinae subfamily of the Nidovirales order. Coronaviruses possess positive-sense ssRNA, and they can be easily isolated from various animal species [50]. This class of viruses is responsible for respiratory infection, including the common cold and other deadly diseases like MERS, SARS, etc. The spike proteins present over the envelope, are responsible for inducing host immune response and attachment to host cells. Host cells have ACE2 (Angiotensin-converting enzyme 2) receptor proteins over the surface that binds to S proteins to mediate cell invasion [51] (Fig. 4). This interaction results in the initiation of the infection. SARS-CoV-2 is usually found in round, elliptical, and sometimes pleomorphic forms. It possesses a diameter of around 60–140 nm. This class of viruses, similar to other viruses, can be easily deactivated by 75% ethanol, chlorine disinfectants, UV rays, etc. The genome of SARS-CoV-2 is composed of 29891 nucleotides, which codes for nearly 9860 amino acids. The largest gene in SARS-CoV-2 is orf1ab that encodes the pp1ab protein and 15 nsps, whereas pp1a protein is encoded by gene orf1a that, in turn, contains 10 nsps. Homotrimers of S proteins compose the spikes over the viral surface, guiding to host receptors [52]. Of note, the S2 subunit is highly conserved—containing a fusion peptide, a transmembrane domain, and a cytoplasmic domain. Thus, it has the potential to serve as a target for antiviral compounds. Some structural components which can contribute to research studies are ORF3b, which has no similarity with that of SARS-CoVs and an emanated protein (encoded by ORF8), which is structurally

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Fig. 4 A graphic depiction of structure of SARS-COV-2 highlighting ACE-2 receptor and its receptor binding domain

different from those of SARS-CoV. Xu et al. [53], reported that SARS-CoV-2 and betacoronavirus of sub-genus Sarbecovirus founds in bats showed high genetic similarity, nearly 96%. In a research study of 103 genomes of SARS-CoV-2, it was found that it mainly has 2 lineages, i. e, L and S. L lineage is more adamant than S lineage, i.e. 70 and 30%, respectively [13–16]. Computational intelligence techniques like Big Data Analytics combined with Artificial intelligence can be used in studying the structure of the virus, and observing specific features [54]. By understanding the properties and characterstics of this virus, we can design best-suited preventive measures and treatment against the virus. Since not much study and research has been established on COVID-19, a comparative study of its structure, properties, and interaction with environment, will help in establishing drugs and therapies against the virus. The next section of the chapter discusses and compares properties of SARS-CoV-2 in comparison to SARS-CoV and MERS-CoV.

5 Comparison of SARS-CoV-2 with Other Viruses Based on Infection, Transmission, Structure According to the current dataset present, it is reported that SARS-CoV-2 has more contagious value than SARS-CoV. It was also revealed that S-protein binding to the ACE2 receptor is 10–20 times higher in SARS-CoV-2 as compared to SARS-CoV [13–16]. SARS-CoV-2 and SARS-CoV are more than 95% similar with respect to RdRp and 3CLpro protease, PLpro sequences share approximately 83% similarity and similar active sites as well, and they are 79% similar at the genomic level [55– 57]. Wu et al. [18–20] pointed out some distinguishing features between these two— absence of 8a protein in SARS-CoV-2 as well as fluctuation in the number of amino acids in 8b and 3c protein [58] (Table 1). SARS-CoV-2 properties are not yet known to us. It is known to us that SARS and MERS virus can stay in a dry environment for 48 h, survive under 20o C with 40–50%

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Table 1 Comparison between SARS-CoV, MERS-CoV, and SARS-CoV-2 Criteria

SARS-CoV

MERS

SARS-CoV-2

Reference

Place of Origin

Guangdong province of southern China.

Saudi Arabia

China

WHO

Mortality rate

9.6%

35.9%

6.31%

Confirmed cases

8439

2274

55, 93, 631 (as of 29 May 2020)

R0

2–4

(where k is the average degree of network) to all other susceptible nodes. The recovery probability of infected node will be α = 1. At the end of spreading process, the number of recovered nodes acts as the spreading capacity of initial nodes. After recording the influence of initial nodes in the network, Kendall’s tau correlation coefficient [17] is used to evaluate the performance of different centrality measures. Kendall coefficient value can be determined by comparing the ranked list of degree centrality, betweenness centrality and our proposed algorithm with the ranked list generated by SIR model. The ranking list calculated by above mentioned methods is more similar to the real spreading ability of node if τ is close to 1. Kendall rank correlation coefficient can be defined using Eq. (15) as given below. τ (R, RE ) =

nc − nd n(n − 1)/2

(15)

where nc and nd are the concordant pairs and discortant pairs respectively. R is the list of nodes generated by evaluation method, and RE is the expected rank list of nodes.

4.3 Experimental Results and Discussion This section discusses the results of four state networks in detail. The comparisons of ranking results are summarized in Table 3 using different centrality measures. Finally, the evaluation results of the proposed CovidKeySpreader algorithm for node influences are briefly discussed. The comparison has been performed on the statewise networks and their corresponding results are listed in Table 2. β denotes the infection probability used in SIR process for each network and τ (.) represents Kendall’s tau correlation coefficient of the corresponding measures. For the state network Maharashtra, the τ of CovidKeySpreader, degree centrality and betweenness centrality are obtained as 0.80 , 0.20 and 0.60 respectively. Thus, the CovidKeySpreader exhibits best result concerning τ . For the state network Karnataka, the best methods for evaluating node influences are both betweenness centrality and CovidKeySpreader, with their τ values remain same as 0.60. Further, the τ value of degree centrality remains lowest in all three methods, i.e. 0.20 only. For both states Kerala and Delhi, τ values of all three methods reveal the same result of 0.20 and 0.60 respectively. Thus, it has been observed that the τ values of CovidKeySpreader are significant in Maharashtra, Karnataka, Delhi, and less significant for state Kerala.

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Table 2 The correlation of degree centrality, betweenness centrality and CovidKeySpreader Networks β τ (Ck ) τ (Cb ) τ (CovidKeySpreader) Maharashtra Karnataka Kerala Delhi

0.94 0.96 0.95 1.25

0.20 0.20 0.20 0.60

0.60 0.60 0.20 0.60

0.80 0.60 0.20 0.60

Table 3 The top-5 ranked nodes by SIR, degree centrality, betweenness centrality and CovidKeySpreader Networks SIR Ck Cb CovidKeySpreader Maharashtra

Karnataka

Kerala

Delhi

4.3.1

57 58 13 7 12 217 223 9 231 61 56 169 18 167 173 23 25 24 11 9

57 58 61 13 59 216 220 217 231 233 169 167 170 173 175 19 21 22 23 24

57 58 61 12 13 61 216 223 90 220 100 169 178 32 167 25 9 11 23 24

57 58 12 7 6 216 220 217 231 9 173 169 18 56 69 23 24 25 9 11

Identifying Key Spreaders in Maharashtra

The first confirmed case of COVID-19 was reported on March 9, 2020 in Pune. A couple had traveled from Dubai [18]. Later on, their contacts were traced and found positive. Figure 2a shows the disease’s network in Maharashtra, whereas Fig. 2b shows the formation of communities by infected individuals. Table 4 provides the rank list of key spreaders for the state Maharashtra. It forms 9 communities with 69 nodes. Initially, it has been observed that most cases are directly or indirectly associated with two districts Pune and Mumbai. Node 57 of Pune turned out to be the key spreader in Maharashtra.

Social Network Analysis for the Identification of Key …

(a) Network of COVID-19 in Maharashtra

(b) Communities showing process of infection propagation in Maharashtra

Fig. 2 Depiction of spreading mechanism in Maharashtra, India Table 4 Ranking of top 15 spreader nodes for Maharashtra Rank Node Foreign nations/Contracted source node 1 2 3 4 5 6 7 8 9 10 11

57 58 12 7 6 1 2 3 4 5 17

12 13 14 15

18 20 24 27

Main source who spread virus in Pune Main source who spread virus in Mumbai Travelled from US Travelled from US Related to node 1 Travelled from Dubai Related to node 1 Related to node 1 Related to node 1 Related to node 1, 6 Family members or relatives of the group of 40 returned from Dubai Travelled from Thailand Travelled from Japan and Dubai Travelled from France and Netherlands Related to patient travelled from Philippines

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Identifying Key Spreaders in Karnataka

The first confirmed case in Karnataka was recorded on March 9, 2020, where a 46 years male who had traveled from US [19] was detected with coronavirus. Since then, the cases spread across the state. Local transmission of virus affects a large number of individuals, as shown in Fig. 3. Figure 3a shows the network of the disease in Karnataka, whereas Fig. 3b shows communities’ formation by a single infected person. Node 216, 220, 217 and 231 form the major communities, while districts Bengaluru Urban, Mysuru, Kalaburagi, Belagavi are the worst affected. Table 5 provides the rank list of top 15 spreaders for the state Karnataka. Among them node 216 of Bengaluru Urban is the key spreader in Karnataka.

4.3.3

Identifying Key Spreaders in Kerala

Kerala witnessed the first case of COVID-19 of India in the Thrissur district in the form of a patient who returned from Wuhan [20]. Figure 4a shows the network of COVID-19 in Kerala, and Fig. 4b shows the formation of communities of infected patients. Both Kannur and Idukki districts of Kerala formed the largest communities associated with node 173, 169, and 173 in the network. They can be seen as key spreaders who are responsible for the initial outbreak in Kerala. Table 6 provides the rank list of the top 15 key spreaders in Kerala.

(a) Network of COVID-19 in Karnataka

(b) Communities showing process of infection propagation in Karnataka

Fig. 3 Depiction of spreading mechanism in Karnataka, India

Social Network Analysis for the Identification of Key … Table 5 Ranking of top 15 spreader nodes for Karnataka Rank Node Foreign nations/Contracted source node 1

216

2 3 4 5 6

220 217 231 9 90

7

117

8 9 10 11 12 13 14 15

1 2 3 4 5 8 10 11

(a) Network of COVID-19 in Kerala

Main source who spread virus in Bengaluru Urban Main source who spread virus in Mysuru Main source who spread virus in Kalaburagi Main source who spread virus in Belagavi Travelled from Dubai History of severe acute respiratory infection (SARI) History of severe acute respiratory infection (SARI) Travelled from US Travelled from US Related to patient travelled from US Related to patient travelled from US Travelled from Greece Travelled from UK Travelled from Spain Related to patient travelling from Saudi Arabia

(b) Communities showing process of infection propagation in Kerala

Fig. 4 Depiction of spreading mechanism in Kerala, India

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Table 6 Ranking of top 15 spreader nodes for Kerala Rank Node Foreign nations/Contracted source node 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

4.3.4

173 169 18 56 69 86 87 96 97 98 99 153 154 155 156

Main source who spread virus in Idukki Main source who spread virus in Kannur Travelled from Dubai, British citizen Travelled from Dubai Unknown travel history Friend of node 69 Teacher who had come in contact with node 69 Mother of node 86 Wife of node 86 Son of node 86 Son of node 86 Travel history to Mysore Mother of node 153 Travel history to Pollachi Travel history to Chennai

Identifying Key Spreaders in Delhi

The first case of COVID-19 was reported on March 2, 2020 in East Delhi district [21]. The patient was 45 years male returned from Italy, the epicenter of COVID-19 after Wuhan. However, In Delhi, there is very less number of nodes due to limited information revealed by sources, and the network is completely disconnected. As per the data available in the earlier mentioned data set, the COVID-19 network of Delhi shown in Fig. 5a. Figure 5b shows the communities’ formation of infected individuals in Delhi. Table 7 provides the rank list of the top 15 key spreaders in Delhi, and node 23 can be identified as key spreader and highly responsible for the initial outbreak in Delhi.

5 Conclusion India is experiencing a sharp increase in infectious diseases caused by the most recently discovered coronavirus. Major cities are shutting down, and millions of people stay at home to protect themselves from the ongoing pandemic. We have proposed a CovidKeySpreader algorithm to identify key spreaders in Indians’ region. In this chapter, social networks are modelled on the basis of patient interaction data for various states. The information derived from these networks can be easily analyzed to identify spreading networks across states based on communities and key spreaders. Such information is beneficial for various government agencies which take benefit

Social Network Analysis for the Identification of Key …

(a) Network of COVID-19 in Delhi

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(b) Communities showing process of infection propagation in Delhi

Fig. 5 Depiction of spreading mechanism in Delhi, India Table 7 Ranking of top 15 spreader nodes for Delhi Rank Node Foreign nations/Contracted source node 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

23 24 25 9 11 7 8 13 15 21 1 18 19 22 16

Related to patient travelled from Saudi Arabia Related to patient travelled from Saudi Arabia Related to patient travelled from Saudi Arabia Travelled from UK Related to patient travelled from Saudi Arabia Travelled from Saudi Arabia Travelled from London Mohalla clinic doctor (Contact Transmission) Related to UK returnee Related to patient travelled from Saudi Arabia Travelled from Italy Related to patient travelled from Saudi Arabia Related to patient travelled from Saudi Arabia Related to patient travelled from Saudi Arabia Safdarjung Hospital Doctor posted in COVID19 unit

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and implement necessary steps to elevate impact on those affected by the mass emergency. The performance of our method is evaluated using the basic SIR model. Experimental results show that our proposed method performs better in identifying key spreaders than basic centrality measures. The proposed method will help control the spread of the disease by tracing the contracted individual and quarantining all its associated links.

References 1. WHO Timeline—COVID-19. (2020). Retrieved on June 25, 2020, from https://www.who.int/ news-room/detail/27-04-2020-who-timeline---covid-19/. 2. COVID-19 CORONAVIRUS PANDEMIC. (2020). Retrieved on June 25, 2020, from https:// www.worldometers.info/coronavirus//. 3. Heymann, D. L. (2020). Data sharing and outbreaks: Best practice exemplified. The Lancet, 395(10223), 469–470. 4. Yang, X., Yu, Y., Xu, J., Shu, H., Liu, L., Wu, Y., et al. (2020). Clinical course and outcomes of critically ill patients with sars-cov-2 pneumonia in Wuhan, China: A single-centered, retrospective, observational study. Lancet Respiratory Medicine. 5. Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., et al. (2020). Early transmission dynamics in Wuhan, china, of novel coronavirus—Infected pneumonia. New England Journal of Medicine. 6. COVID19-India API. (2020). Retrieved on June 25, 2020, from https://www.covid19india. org//. 7. Countries where COVID-19 has spread (2020). Retrieved on June 25, 2020, from https://www. worldometers.info/coronavirus/countries-where-coronavirus-has-spread//. 8. Sharma, D., & Surolia, A. (2013). Degree centrality. Encyclopedia of Systems Biology, Dubitzky W, 558. 9. Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239. 10. Shimbel, A. (1953). Structural parameters of communication networks. The Bulletin Mathematics and Biophysics, 15(4), 501–507. 11. Shaw, M. E. Group structure and the behavior of individuals in small groups. Journal of Psychology, 38(1), 139–149. 12. Pons, P., & Latapy, M. (2005). Computing communities in large networks using random walks. In International symposium on computer and information sciences (pp. 284–293). Springer. 13. Tong, H., Faloutsos, C., & Pan, J.-Y. (2008). Random walk with restart: Fast solutions and applications. Knowledge Information Systems, 14, 327–346. 14. Zhan, J., Guidibande, V., & Parsa, S. P. K. (2016). Identification of top-k influential communities in big networks. Journal of Big Data, 3(1), 16. 15. Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. International Journal of Complex Systems, 1695(5), 1–9. 16. Hethcote, H. W. (2000). The mathematics of infectious diseases. SIAM Review, 42(4), 599–653. 17. Mao, C., & Xiao, W. (2018). A comprehensive algorithm for evaluating node influences in social networks based on preference analysis and random walk. Complexity. 18. COVID-19 pandemic in Maharashtra. (2020). Retrieved on June 25, 2020, from https://en. wikipedia.org/wiki/COVID-19_pandemic_in_Maharashtra//. 19. COVID-19 pandemic in Karnataka. (2020). Retrieved on June 25, 2020, from https://en. wikipedia.org/wiki/COVID-19_pandemic_in_Karnataka//.

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20. COVID-19 pandemic in Kerala. (2020). Retrieved on June 25, 2020, from https://en.wikipedia. org/wiki/COVID-19_pandemic_in_Kerala//. 21. COVID-19 pandemic in Delhi. (2020). Retrieved on June 25, 2020, from https://en.wikipedia. org/wiki/COVID-19_pandemic_in_Delhi//.

Mobile Technology Solution for COVID-19: Surveillance and Prevention Shaban Ahmad, Pragya Chitkara, Fatima Nazish Khan, Avtar Kishan, Vaibhav Alok, Ayyagari Ramlal, and Sahil Mehta

Abstract The twenty-first century, a century would be known for profound technological advancements and unfortunately also for a global economic and health crisis due to SARS-CoV2, the causal organism of respiratory syndrome COVID-19. Due to the huge crisis in every sector, ‘Technological or Digital way’ is the brightest hope to fight this pandemic. Analysis of the data obtained from the past few infectious months, the spread is more likely to become a seasonal threat to mankind. As an effort to level up the technology and other associated aspects, various researchers and developers are developing mobile applications and mobile controllable devices to provide quality information which can help in flattening the curve of this pandemic. Practically, it becomes a great method to prevent close contacts with diseased individuals by providing virtual visits and through robotic technologies. In the current chapter, the technological background of computational intelligence-controlled smartphone S. Ahmad (B) · P. Chitkara · F. N. Khan · A. Kishan Department of Computer Science, Jamia Millia Islamia, New Delhi 110025, India e-mail: [email protected] P. Chitkara e-mail: [email protected] F. N. Khan e-mail: [email protected] A. Kishan e-mail: [email protected] V. Alok Department of Biosciences, Jamia Millia Islamia, New Delhi 110025, India e-mail: [email protected] A. Ramlal Deparment of Botany, University of Delhi, New Delhi 110007, India e-mail: [email protected] S. Mehta International Centre for Genetic Engineering and Biotechnology, New Delhi 110067, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 K. Raza (ed.), Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, Studies in Computational Intelligence 923, https://doi.org/10.1007/978-981-15-8534-0_5

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applications for monitoring an individual’s health and tracking the geographical spread of the virus, along with the research scope and data security concerned with these applications have been discussed. This could help the government to understand the potential risk circumstances towards early exposure and timely medical intervention to prevent it from further spread to other regions. Keywords Apps · Android · GPS · Data security · Geo-tracking · Contact tracking · Privacy concerns

1 Introduction Have you ever wondered if we can really monitor the health status of an individual accurately or can a patient be treated at his home so wisely? It has been a challenge for years to improve our health system to gain better health services and treat patients in bad situations. Here, a simple term ‘Mobile technology’ comes into play which has the capability of improving the issues faced by the medical sciences and is therefore commonly known by the name of “mHealth”. It is an emerging discipline which is nowadays boosted up to a new and bigger level due to ongoing pandemic coronavirus diseases (COVID-19) and has somewhere resulted in better output in health services [1]. The technological advancement and integration of health data are creating a huge difference in the records. Considering, the pace at which the number of mobile users in developing countries like India is increasing, there has been a parallel increase in mobile and cloud technological developers. This has resulted in timely and effectual data assembly for the systematic delivery of healthcare to millions of people [2]. The pace at which severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) is spreading among the population, each individual is at great risk. After analyzing data obtained from this pandemic, it does not seem to be controllable [3]. By keeping this in mind it is the need of the hour to level up the technology which will not only track geographical hotspots but also helps in the efficient screening of the population [4, 5]. There is an increasing demand for mobile technology solutions for the allocation of resources and tracking of infection. On the contrary, increased cases of infected individuals led to more innovation toward technological solutions for COVID-19. Due to the advancement in technologies, humans are somewhere more equipped than ever before in history to cope with the pandemic. When the SARS outbreak occurred in 2002 we were not well equipped with these technologies, and it took over a decade to code the genome of that virus. But now we have coded the genome of COVID-19 within a month by the technological advances [6]. Internet-based applications will help a lot during critical time as it helps in precise positioning of the hotspots, pinpoint most risky areas and carry out relief approaches. There will be great communication between the public and its government to ensure prevention beyond disease spread. When we come across a word Mobile technology, each of us thinks about a technology which a mobile phone use. But it is not true at all, it is a type of technology which moves with the user, i.e. in simple words, a technology which goes where

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the user goes. It generally has many portable wireless devices such as iPad, tablet, smartphone, smartwatch etc. which are generally connected via the networks like Wi-Fi, bluetooth, Global Positioning System (GPS), and cellular networks and share the voice, data, applications, files etc. On the ground level, if we see a modern smartphone, it generally provides too much specification tools such as internet connectivity, GPS, Wi-Fi, computer-like functionality, bluetooth etc. To deal with the question of how mobile technology works, we first take an example to understand how mHealth works? In Fig. 1 we have diagrammatically represented how mHealth works and how medical staffs can get the benefits to treat the patients in less time in serious cases. For instance, suppose a person got a viral attack from the communal spread and he wants an emergency treatment and wants to go to the hospital but a hospital is far away from that place and also there is poor road infrastructure along with bad weather conditions, or no ambulance services. In these kinds of worst situations, as

Fig. 1 mHealth in Various Domains; explaining the importance of mHealth (Mobile health) and how it is working. (i) By text messaging and video conferencing, a patient can consult a doctor without travelling such a large distance and can get proper guidance and direction for the treatment of a particular disease. The report of the patient can further be stored in a Government database which can later be used for contact tracing in case of communal spread like in the case of COVID-19. (ii) Telemedicine (caring facilities via long distances and can prevent the transmission of infectious diseases which hence reduces the risks to both health care workers and patients. Telemedicine can be further pursued after the Doctor consultation and hence gives the clue to the patient about how to treat the disease. (iii) Health applications can lead to self-monitoring of the user itself by tracking his/her daily activity by using certain apps related to health status. (iv) Certain apps that work on special Networking and Connectivity like Aarogyasetu, Trace together, CovidWatch, HaMagen, etc. Can be used to track a particular individual based on his/her health status to keep people safe during communal spread

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described, a patient will either deteriorate or die. But if an affected person is having a mHealth facility, he/she could get quick treatment via his smart mobile device via video conferencing with a doctor or a nurse in lesser time. Similarly, these mHealth services can be also useful in tracing a person’s location; for instance, in case of communal spread of COVID-19 (we can easily monitor the health status of an individual) and finally lead to the safe environment. Hence, mHealth is the use of mobile and wireless cloud-based technology to support the achievement of health objectives, or the use of mobile and wireless devices (i.e. cell phones, tablets etc.) to improve health outcomes, health research and health care services [7, 8]. But now let us see the detail definition of mHealth. Going on further, it is important to know that network is the most precious component in mHealth technology. Network connectivity generally refers to the linkage of different parts of a network via routers, gateways, and multiple APIs (Access Points). A network connection must be reliable and fast, if it is low, then there would be high chances of a connection failure with medical/healthcare staffs. This will eventually cause the death of the patients or put them in a critical condition. Medical staff and patients depend upon the connected device to give and receive data and instruction with care. Since the information acts as liquid gold because the right information at the right time can save the patient’s life. Patients depend on the network when they are in the hospital or nursing home to check various activities of themselves and also to get in touch with nurses as well as other staff. Networks are transmitted via Wi-fi, bluetooth, cellular or through the wired connections. Risk awareness is the best way to slow down or prevent and the transmission risk of COVID-19. This can be achieved by potential risk statement depending on various mobile applications. mHealth applications provide tracking the affected individual or diagnosing probable conditions. On seeing on the ground level about any disease (i.e. viral or bacterial) it is necessary to know that current system of publishing quality research is inefficient in these emerging COVID-19 situations and result in huge challenges to the medical staffs and are disturbing medical services, hence to avoid these happenings Mobile technology or mHealth is a potential solution. This chapter aims to discuss the various applications, preventive measures, research gaps, privacy issues and the limitations of the challenge of mobile technology to combat the ongoing pandemic.

2 Processes of Communication There are many processes of communication by which we can monitor the patients as well as suspect those people who are more prone to infectious diseases. Governments and private sectors are participating in this whole procedure to control this pandemic, still, there are huge chances of improvements. • AI satellites are designed to analyze the geographical data, visualization of treated patients and mapping the geographical reach of the virus.

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• Mobile-based apps are using a sophisticated network for the benefit of the public. Colour-coded health rating systems based on the medical condition and travel histories should be made and the only person assigned green colour will be eligible to go outside in public places. • Drones are also being used to transport medical equipment and for transporting patient’s sample which is ideal to prevent contamination. • GPS serves the best method for real-time display of epidemic data. • Users can easily access the academic risk distribution of the surroundings. • There can be various modes of communication-based on the motive, some of the important technology of tracing and monitoring are described below.

2.1 Bluetooth Based The technological development we call as ‘Bluetooth’ was first introduced in the market by Dr Nils Rydbeck in 1989 while working as the chief technology officer at Ericson Mobile, and inventor Dr Johan Ullman had this world-shattering idea to develop a pair of wireless headsets, to eliminate the annoying and disturbing wires. Well at that time, all three tech giants were working separately to innovate shortrange radio technology that would connect computer and different devices using short wave frequencies in the nearby locative devices. But while the research was going on during its early stage, Intel, Ericsson, and Nokia decided to proceed to create a single wireless standard. A well-renowned technician, Jim Kardach, who was working for Intel, happened to be reading a historical book about how the Viking king Harald “Bluetooth” Gormsson united Denmark and Norway. That story inspired Kardach to propose his idea of calling this single short-link device that would unite communications “Bluetooth”. It is the technology for a PAN (Personal Area Network) and it is important to note that Bluetooth device generally works at 2.4 GHz frequency in the same range where many other wireless technologies peruse, thus it is more prone to the hackers which may further lead to the privacy interference. So, the question arises that how to overcome this problem? To overcome this privacy interference, A user may prefer to do a secure pairing with a password key. Bluetooth supports four pairing models namely: Passkey Entry, Numeric Comparison, just works and Out of Band. Bluetooth, as we all know, is a very popular technology which is being used from decades in the mobile devices which had replaced the wired technology [9, 11]. For the benefit of COVID-19, bluetooth is used as a new privacy-protective protocol which generally supports contact tracing. With the help of contact tracing, it is possible to fight the spread of COVID-19 viral infections by clueing and alerting the public about people whom they were recently in contact with and who have already been tested positive with the virus [9, 10]. Contact tracing service is like the vehicle for distinguishing suspected versus normal. And this become possible by bluetooth LE (Low Energy) for proximity detection of nearby smartphones, and the mechanism of the data exchange. In mHealth sensing applications, bluetooth is

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the first preference of an application developer because it has a massive user base and is currently available in almost every cell phone. It collects data via sensors into mobile devices. As we know that medical data is sensitive so privacy should be an important aspect of the mhealth sensing. In the discoverable mode, the Bluetooth devices generally respond to a query by disclosing the information like name, address, and other function that connects to it. In mHealth while both the users turned it on, it gets the signal whether the other user is safe or infected, based on this it provides the notification for the precautions [11].

2.2 Wi-Fi-Based The technology of Wi-Fi started back in the 1970s with electrical engineer Dr John O’Sullivan, also known as the father of Wi-Fi. At the time, the team were trying to detect radio signals from distant black holes in space, they came up with a complex equation called Fast Fourier transforms. Unfortunately, they could not detect those black holes and they put all their equipment back on the shelves to sit and collect dust. Surprisingly twenty years later, Dr O’Sullivan and his co-workers decided to give wireless networking a chance and those forgotten complex equations would play a key role in the invention of Wi-Fi [12, 13]. After a lot of experimentation, they took their fancy fast Fourier transform, added them in the mixture with the data equations they’d previously tried to send over the radio, and thus they formed the basis for the Wi-Fi, that we all know and love today. But again, that was just the basics. Later in the year of 1996, they further developed their original key patent and by 1997 they finally cracked the code and came up with the first version of the 802.11 protocol. Wi-Fi is a pun for the word Hi-Fi which means “high fidelity” a technical term used for best quality audio technology. Dealing with the question how Wi-Fi works, we all know that wireless internet helps us sending and receiving files, pictures, messages and many more, but did you know that this all happens because of radio waves. To transfer the data Wi-Fi uses radio waves between router/device (Wi-Fi source) and your device (or the receiver). These frequencies are measured in Gigahertz. To put all the science stuff behind that in laymen’s terms. Imagine that you are sitting on the beach enjoying the sun and watching the wave’s crash into the shore. If you were to calculate the time between each wave-crash, you would be calculating the frequency of the wave. Let us assume that the time it takes for each wave to hit the shore is one second: that second is calculated by hertz. In other words, 1 Hzequals 1 s. Now, 1 GHz equals 1 Billion waves per second. If you could see that many ocean waves moving so fast, then you might want to scoot out there and head for the hills. So, the frequencies Wi-Fi routers use are 2.4 or 5 GHz per second, which is why data gets transferred so quickly to your device. When it comes to speed, the 5 GHz frequency sends information faster over shorter distances, whereas the 2.4 GHz router covers farther distances but goes slower. We also have to keep in mind the interference from another device in our

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homes that also use 2.4 GHz radio frequencies. For example, if you have baby monitors, garage doors, microwaves, cordless phones, and wireless cameras, they can interfere with your Wi-Fi. And this kind of interference can reduce the speed, or you might lose your internet connection altogether. Although the latter 5 GHz wireless frequency has 23 channels available to send all your information [10, 13]. Therefore, it does not let any of those home devices cause connection issues. In the tough time of the ongoing pandemic, Wi-Fi can be used for the tracing the location as well as to provide a secure connection to users.

2.3 GPS Based As we all know, GPS stands for Global Positioning System which is improving from decades to track the location of an individual in a closed environment. Due to GPS, it has become possible to monitor and record the frequency of interaction between the infected and non-infected person [14]. GPS works with the help of wireless technology like bluetooth and Wi-Fi. We all are familiar with the term GPS as it appears most of the time during our studies and research, however, it is more important to know its actual working and original concept of development and how it is useful in the domain of Medical sciences to deal with, especially for COVID-19 patients (symptomatic as well as asymptomatic). It is a type of navigation system which is based on satellite (around 24 satellites),referred to as NAVSTAR (Navigation Signal Timing and Ranging) which is also the official DOD (Department of Defense) GPS name. It was first developed by the United States especially for monitoring the global activity (Fig. 2). To trace the patient’s activity and person who came in contact with the infected person, applications use GPS. It helps applications to monitor the user movements and alert them by notifying the zonal presence i.e. whether you are in the green zone or red zones [15].

3 Advantages and Limitations of the Communication Process If we analyse the advantages and the limitations of the communications mechanisms described above there are uncountable benefits of the technological development but almost everything in this world needs improvements, which can be updated later after the testing of the technology. As an effort, a short view of the advantages and the limitations of these techniques is described as follows: Advantages:

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Space Segment

GPS components

User Segment

Control Segment

Contains GPS receiver equipment. Calculates 3D Position and Time.

Control Stations Maintained by Commands. Adjustment of Satellite Clocks.

Fig. 2 (a) Space Segment: Composed of 24 Satellite, these satellites transmit one-way signals by which current GPS satellite position and time are predicted. This segment is present in the space. (b) Control Segment: These are actually the control stat, a working model in COVID-19 tracking. To answer this question, the first need to have an understanding of the structure of the GPS. It is composed of three segments namely space segment, control segment, and user segment. In Fig. 2 a flowchart on the same has drawn to understand its various level. GPS working is based on the principle of Trilateration’. Since the distance is measured via satellites, it is important to know that 4 minimum satellites are used to determine the position of an object/receiver on the earth. More the satellites, more accurate is the location prediction. As out of four, the three satellites are used to trace the place of the location and the fourth satellite is used to determine the target of the receiver as shown in Fig. 3. GPS systems contain a total of 32 satellites, out of which, 24 are core satellites and the rest eight serve as emergency replacements as Stefany when something happens to the others. Life of these satellites is considered to be 10 years after the high maintenance. It works in any weather, rain, or shine but there is one important condition i.e. a receiver on the earth must have to see four satellites for calculating an accurate point because the GPS uses a trilateration method. 2D trilateration and 3D trilateration plays an important role here. We have plotted the basic working methodology for GPS

• The communication processes making use of bluetooth, GPS, and Wi-Fi provide a more scalable approach which relies on patient’s monitoring of recent exposure to others. • It provides a valuable role in informing local and state-level policymaking and monitoring their effectiveness. • The tracing done using Bluetooth, Wi-Fi or GPS location are a more practical and faster approach than manual recording and traditional contact tracing. • The communication processes assisted by Bluetooth, GPS or Wi-Fi augments contact identification by identifying potentially unknown contacts thereby proving their effectiveness. Limitations:

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Fig. 3 Locating the Object on Eart via GPS Technology. This figure explains how Objects are located on the Earth via GPS technology, e.g. suppose you are 15 km away from satellite A, then you are at some point inside an imaginary sphere A (Red circle). You are also sure that you are 20 km away from satellite B (yellow). Now, two circles will overlap (red and yellow) and you will see. Take the distance from third satellite C to build another sphere (violet) and you will be getting two points of intersection. Similarly, repeat the same procedure with satellite D (grey color) for getting an accurate and precise location on the Earth denoted with a small star

• The communication processes through mobile applications involving surveillance through location data raise a question for the privacy of the users and might intervene with data security by making the users vulnerable to malicious interference. • Potential gaps in data capture occurring due to inaccurate detection, or failure to communicate a brief interaction might turn problematic. • Data collected by the different communication processes face a major concern of accuracy and accountability due to the false positives. • The communication means through Bluetooth or Wi-Fi have inherent socioeconomic and technological biases as the usage of such requires users to have smartphones. • Any communication process involving Bluetooth, Wi-Fi, or GPS is ineffective until a critical mass of users utilize it.

4 Mobile Technology During the Ongoing Pandemic The COVID-19 has continued to devastate most countries on the globe and has so far claimed over 12,625,156 affected (till 10 July 2020) people worldwide and is still spreading. If we analyse the Indian COVID-19 data it is getting out of box day by day. To deal with the problem of physical distancing mobile application plays an important role. There are various types of mobile technologies that are being

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implemented to cope up with the novel coronavirus pandemic [16]. Various mobile app industries have helped during the crisis which includes.

4.1 Grocery Apps Anyone cannot live without a grocery. In most countries, lockdown implementation hampered people to buy necessary items. Grocery apps are evolving to keep up the pace with onslaught of demand and to feed people in the safest ways. The most popular grocery apps like Walmart, Instacart, Target, and H-E-B experienced a startling number of downloads amidst the global crisis. Grocery apps have undergone a breathtaking metamorphosis from merely being a choice to become an absolute necessity during COVID-19.

4.2 Health Care Apps We can implement various medical care apps for providing essential advice of doctors to the public for the prevention of the disease and to cure the disease. Doctors can provide consultations, prescriptions, and guidance to the patients using these apps, along with the delivery of medical help to such individuals. For detecting the onset and spread of disease in a particular area, many countries have made their mobile apps using artificial intelligence for the detection and spread of this epidemic. These apps take basic data from the consumer like age, sex, medical history and any further foreign visits in last month and provide colour for prone individual, only people provided with green colour are allowed to go outside in public places. For exampleArogya Setu app is launched in India to control the pandemic loss.

4.3 Educational Apps The education sector is no exception to what the entire economy is going through. While education institutes are closed, online education apps and e-learning have gained a huge momentum among faculties and students. Every institution has tapped digital platforms to arrange online classes for their students to make learning uninterrupted. E-learning platforms help teachers to teach their students remotely and communicate with them over live video chats. Educational apps like edX, Alison, Harvard University mobile app etc., are helping students from all around the world to get uninterrupted education. Institutes are conducting online lectures and webinars to help students hone their skills with video apps such as Zoom, Skype, Google Duo, WhatsApp, and Facebook video calls [3]. The efficiency of these technological solutions is not that high, for example, Bluetooth based mobile apps which determine the

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proximity of the individuals may show the risk of more individuals as false positives. Self-positives then result in unrequired self-isolation.

4.4 Telemedicine Apps Telemedicine can be defined as the use of electronic communication and the information sharing for providing support to the patients staying distantly from the healthcare facilities by the professional in the healthcare systems. The government all over the world is taking efforts to promote Telemedicine services as they are used for monitoring patients and reducing the risk of the spreading virus by a visit to hospitals. • US-based Telemedicine app Amwell is extensively used and has shown a usage growth of 158%. • Italy launched an online chatbot, to promote telecommunication. • Healthcare communications of the UK have deployed a chatbot available to NHS service to instantly answer the most common question regarding symptoms from reliable sources. But some apps are highly efficient in its action, COCOVID developed by European companies uses big data analysis and artificial intelligence to supports the hospitals and the health systems by permitting the users of high risk to book a test slot for COVID-19 and uses both the geolocation of the individual and Bluetooth contact data and hence is highly efficient.

5 Tracking/Monitoring COVID-19 Mobile software applications which are being used during the tough time of ongoing pandemic are called COVID-19 apps. Many applications were developed or proposed and are under the review process with the officially supports of the government in some territories and jurisdictions. Many countries including India have developed contact tracing apps [17]. During the time of the ongoing pandemic, it is a better opportunity for the computer scientist and bioinformaticians to help medical staffs, therefore in a very short period, many applications have been launched to track and monitor the COVID-19 cases. These applications also help to keep the public safe and provide better precautious steps to live a COVID-19 free life. Few applications have been well described below.

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5.1 Aarogya Setu Aarogya Setu app, developed by the National Informatics Centre (NIC) under the Ministry of Electronics & Information Technology (Govt of India), is an application built up to track and monitor the COVID-19 patients in India. The government-owned application is the primary contact tracing tool in India and the source code is uploaded on the GitHub. The application whose primary function is to trace contacts and help the Government to recognize potential COVID-19 casualties also helps in spreading awareness about the disease, provides updates about the disease, helps users in selfdiagnosis, and even stores and displays e-pass for the user. The application uses both Bluetooth and GPS location data for determining the proximity of a user with a COVID-19 affected patient. The algorithm used for setting up the application includes: • Arogya Setu utilizes artificial intelligence for calculating the degree of risk of contracting coronavirus by basic information about the user including age, profession, medical history, and foreign travel history. This application works by using static identity, which is a fixed digital ID assigned to a user during the time of registration. The information provided by the user is securely stored using this ID on the server and is managed by the government of India. • The Bluetooth and location data of the user are stored on the centralized server in an encrypted format. The Bluetooth data is stored for 30 days. The location data is shared only for a positive user and the data is made available for 60 days. Bluetooth helps in establishing close-range proximity between two registered users who come in close contact. This helps to determine user’s social graph as the application automatically exchanges digital IDs for the two users and hence the devices exchange information about the Media access control (MAC) address, the distance between the devices, signal strength, and the GPS latitude and longitude information which alerts the user if the other registered is positive. Along with it, the application also instructs the user how to self-isolate and what to do in case the user develops symptoms. The information that is recorded from the application of the user at the time of contact is stored on the mobile device of another registered user and if the registered user tests positive soon, the information could be uploaded from his/her mobile device to the server.

5.2 Quarantine Monitor The application is built by the e-Government agency of Tamil Nadu (India) to monitor the people in quarantine. The app uses the official database of Tamil Nadu to assist the department of health and Tamil Nadu Police in effective Tracking and Information Management of COVID-19 patients. The app uses live-location tracking of

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the patients and generates alerts when patients leave their quarantine area. These applications also record the foreign travel history for people of the state.

5.3 MahaKavach The application is set up by the Maharashtra State (India) Innovation Society and the National Health Authority to track people of Maharashtra who are in quarantine. It is also a contact tracing app that tracks the location of the users to determine if the user came in contact with infected people by sharing location history and contacts of people.

5.4 Quarantine Watch Quarantine Watch is an application built up by the Revenue Department of the Government of Karnataka and helps authorities to keep a track of people in quarantine by keeping a watch over them. The app tracks the patients using GPS and checks for any change in the location.

5.5 COVA Punjab This app is set up by the Government of Punjab, helps in proximity tracking, helps in providing a real-time dashboard of COVID-19 cases, helping in self-assessment for symptoms and shows updates and preventive measures for the disease.

5.6 South Korean App The South Korean application, developed by the Ministry of Interior and safety, is an initiative to keep a check on the positive patients in the country. The application uses GPS location for keeping a track of affected individuals to make sure they are not breaking a mandatory 2 weeks quarantine. This is the reason why the application is named as “self-quarantine safety protection”. The authorities maintain data by monitoring financial transactions, GPS location and CCTV footage. When an individual is tested positive, a record of their movement, travel course, age and sex flows to all the individuals in that area. If the patient enters any area outside their quarantine area, an alert is sent to both the subject and the case officer, who reports the symptoms of the patient.

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5.7 NHS COVID-19 Tracking App NHS COVID-19 is the UK’s contact tracking application developed by the National Health Service to trace the proximity events forward 6 users with coronavirus patients. The UK application allows users to self-report symptoms for COVID-19 and calculate the risk factor for the patients. The application uses Bluetooth low energy to find contact tracing between users and gives information about the time duration of the contact and distance between the devices during the contact. In this application, the contact data is stored initially on the user’s device and is shared centrally only when a user chooses to report themselves after having disease symptoms. This information is used by an NHS clinical algorithm developed to estimate the risk of infection. The UK application allows users to self-report symptoms of COVID-19 which could lead to many false alerts being generated. The contact data for the user for the past 28 days that is shared on the central server if the user tests positive, might contain information about identifiers other user’s identifier which cannot be deleted after the fact. This might raise questions for the application’s privacy concerns. This may encourage users to ignore alerts if the ratio of false alerts exceeds true alerts.

5.8 TraceTogether Application TraceTogether is one of the most reliable contact tracing applications and is developed by the Singapore ministry to curb the spread of COVID-19. This app uses Bluetooth for proximity tracing and exchanges short-distance Bluetooth signals when two registered users come in contact. This triggers the records to be stored in respective phones of the users for 21 days. If a user tests positive for COVID-19, the Singapore health ministry can access the app data to identify people who had close contact with the infected individuals. The privacy of the user is safeguarded by the application as no personal details are collected at the time of registration. The application does not record location data and access the user’s phone contact list, thereby strengthening the privacy policy set up by the app. The data logs are stored on phone rather than on mobile number in form of cryptographically generated temporary ID and the source code for the application is made available publicly allowing the experts to test the application’s functionality.

5.9 HaMagen HaMagen is an application developed by the ministry of health, commercial companies, and volunteers from various organizations as a joint initiative. The application is designed to alert people of Israel by alerting users for sharing GPS location with COVID-19 patients. The application stores the GPS history and information about

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the wireless network (Wi-Fi) that one came across by registered mobile user on the mobile phone of the user for two weeks. This information is uploaded on the server only when the user tests positive. The application downloads a file with an anonymous list of locations at a regular period and cross-refers this file against the user’s location on the mobile phone. If the user crosses the path to the location of COVID19 patient then the application shares the time and location of contact and alerts by messages. The GPS history of the user is stored and cross-referred on the user’s mobile phone and is not sent to any third party thereby safeguarding the privacy of the user and the open-source code for the application is made available.

5.10 Contact Tracing App of China The contact tracing application of China is not a standalone tool but is embedded and managing data from popular payment, managing, and searching engines. The application uses a QR code system designed to show the user’s health status. The primary goal of the application is to curb the disease spread by restricting and permitting the user’s accessibility to go anywhere. The application stores information about national identity number, passport number as well as a phone number for the registered user. The user is also asked about travel history and health status. Using this information, the application provides a colour code for the user. Green code grants unrestricted movement, while the yellow code assigned to the user shows that the user has been in the contact of an affected person and hence assigns a quarantine period of 7 days. While the red zone is assigned to people who are either affected or are at risk of getting affected. The application uses GPS, Wi-Fi, Bluetooth, and details of the transactions, calls and messages in a centralized way.

5.11 COVIDsafe COVIDsafe is the approved contact tracing application of Australia. The application helps users to trace contact links with affected patients. The application creates a unique encrypted reference code for a registered user. As soon as a registered user comes in contact with another user’s reference code with the help of Bluetooth tracking, the application notes the date, time, distance and duration of contact and is stored securely on the user’s mobile phone for 21 days. The information will be uploaded to a highly secure information storage system as soon as the user tests positive. The application helps health officials to alert those who may need to quarantine or get tested. In Table 1, we have listed the name of the applications and countries having mobile apps along with the operating system.

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Table 1 List of countries having apps to track and monitor COVID-19 S. No. Country

Name

Platform

Functionality

1

Angola

COVID-19AO

Web-based

Quarantine enforcement with self-diagnostic information

2

Australia

Coronavirus Australia

Android, iOS

Isolation registration with the information

COVIDSafe

Android, iOS

Contact tracing

3

Austria

Stopp Corona

Android, iOS

Contact tracing, medical reporting

4

Brazil

The Spread Project

Android, iOS

Medical reporting and contact-tracing application

5

Canada

COVID Shield

Android, iOS

Contact tracing

6

China

Alipay Health Code

Android, iOS

Contact tracing

7

Czech Republic

eRouska

Android, iOS

Contact tracing

8

Denmark

Smitte

Android, iOS

Contact tracing

9

Finland

Ketju

Android, iOS

Contact tracing

10

France

StopCovid

Android, iOS

Contact tracing

ROBERT

Unknown

Contact tracing Contact tracing

11

Georgia

Stop COVID

Android, iOS

12

Germany

Ito

Android

Contact tracing

OHIOH Framework

Android, iOS

Contact tracing, scientific research

Corona-Warn-App

Android & iOS

Contact tracing 24/7 online doctors with self-diagnosis information

13

Greece

DOCANDU COVID-19 Checker

Android, Web-based

14

Ghana

COVID-19 Tracker App

Android, Windows, iOS

15

Hong Kong

Stay Home Safe

Unknown

Quarantine enforcement

16

Hungary

VírusRadar

Android, iOS

Contact tracing

17

Iceland

Rakning C-19

Android, IOS

Tracing

18

India

AarogyaSetu

Android, iOS

Contact tracing

COVA Punjab

Android, iOS

Contact tracing

COVID-19 Feedback

Android

Feedback

COVID-19 Quarantine Monitor

TBA

Contact tracing, geofencing

Corona Kavach

Android

Information (continued)

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Table 1 (continued) S. No. Country

Name

Platform

Functionality

GoK Direct

Android, iOS

Information

Mahakavach

Android

Contact tracing

Quarantine Watch

Android

Contact tracing

Test Yourself Goa

Android

Self-diagnostic

Trackcovid-19.org

Web

Self-diagnostic, syndromic surveillance

Test Yourself Puducherry

Android

Self-diagnostic

19

Indonesia

PeduliLindungi

Android, Apple

Contact tracing

20

Israel

Hamagen

Android, iOS

Contact tracing

21

Italy

COVID Community Android, iOS, web Alert

CovidApp-citizens, CovidDoc-doctors, web-based dashboard for epidemiologists

diAry “Digital Arianna”

GPS based location tracking, awareness-raising and exposure notification,

Android, iOS

Immuni [it]

Android, iOS

Contact tracing

SM-COVID-19

Android, iOS

Contact Tracing

Android, iOS

Contact Tracing

22

Japan

COVID-19 Contact-Conf. App

23

Jordan

AMAN

Android, iOS

Exposure Detection

24

Latvia

Apturi COVID

Android, iOS

Exposure Detection

25

Malaysia

Gerak Malaysia

Android, iOS

Contact tracing, border crossing registration

MySejahtera

Android, iOS

Information

MyTrace

Android, iOS

Contact tracing

Wiqaytna

Android, iOS

Contact tracing

26

Morocco

27

Netherlands

PrivateTracer

Android, iOS

Contact tracing

28

New Zealand

NZ COVID Tracer

Android, iOS

Journal with a point of interest

29

N. Macedonia

StopKorona!

Android, iOS

Contact tracing

30

Norway

Smittestopp

Android, iOS

Contact tracing, route tracking

31

Poland

ProteGO Safe

Android, iOS

Contact-tracing and info for medical reporting (continued)

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Table 1 (continued) S. No. Country

Name

32

“Social Monitoring” under development

Contact tracing

Contact Tracer

Android

Digital Contact Tracing and Alerting

Android, iOS, Web

Self-diagnostic

Russia

Platform

Functionality

33

Saudi Arabia

Corona Map Tabaud

iOS

Contact Tracing

34

Singapore

TraceTogether

Android, iOS

Contact tracing

35

South Korea

Corona 100 m

Android

Contact tracing

Self-Diagnosis app

Android, iOS

Self-diagnostic

Self-Quarantine app

Android, iOS

Isolation registration

36

South Africa

Covi-ID

Android, iOS, Web

Health credential management, GPS-based tracking

37

Sri Lanka

Self Shield

Android

AI-driven breathing assessment, Quarantine Monitoring, Self-Health monitoring

38

Switzerland

SwissCovid

Android, iOS

Contact tracing

39

United Kingdom

COVID Symptom Study

Android, iOS

Self-diagnostic

NHS COVID-19

Android, iOS

Multipurpose

40

United States

COVID-19 Screening Tool

Web

Self-diagnostic

CovidSafe

Android, iOS

Self-diagnostic, contact tracing

How We Feel

Android, iOS

Self-diagnostic

Private Kit; Safe Paths

Android, iOS

Tracing

COVID Watch

Android, iOS

Anonymous exposure alerting

coEpi

Android, iOS

Self-reporting

NOVID

Android, iOS

Contact tracing

41

Vietnam

NCOVI

Android, iOS

Medical reporting

Bluezone

Android, iOS

Contact tracing

42

Global

WHO COVID-19 App

Android, iOS

Information

Coalition App

Android, iOS

Contact tracing

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6 Health Technology Behind These Apps Government and health authorities across the world, are working in collaboration to find a solution to the COVID-19 pandemic and to protect people. Software engineers are giving their efforts by drafting technical tools to help to combat the virus. Digital technology has led to the development of many mobile applications that serve as surveillance systems capturing critical data for their COVID-19response, though guaranteeing transparency [18, 19]. These mobile applications used publicly for health observation to support fast reporting, data administration, and its analysis. These technologies are helping the Government to track active cases, recoveries, and quarantine citizens; and helping citizens to connect essential services [19, 20]. The contact tracing, which is the process of managing people after the identification and assessing, who have been exposed to the disease to prevent its transmission is an essential public health tool for controlling infection along with patient monitoring. Contact tracing can help break the chain of transmission of such disease outbreaks. The mobile applications involve the use of application programming interfaces and operating-level technology to assist in enabling contact tracing. One form of digital technology using contact tracing for surveillance is proximity tracking [20]. This technology is used in most of the mobile applications as the disease can be transmitted through proximity to the affected patients. This technique measures the strength of the signal strength to determine whether two devices were close enough to spread the virus from an infected individual to an uninfected individual. If one user is infected, the other user in proximity is notified and informed about proper steps to be taken to reduce health risks by the ministry of Health. The alerts on user’s phone are generated by scanning through government-owned, databases which stores data with the location specificity. Thus, this digital technology can limit potential transmission and even provide data for researchers to prepare for future epidemic outbreaks [20]. Different applications have been set up by technicians for contact tracing patient monitoring, for compacting spread of misinformation, for telemedicine, and for maintaining mental health during the quarantine phase.

7 Applications for Combating Misinformation Along with the fear of disease transmission through contact, there has been havoc being wrenched on social media, with thriving misinformation called infodemic. Many technicians are also working for the development of applications that could help to combat the spread of misinformation.

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7.1 Who Covid-19 The app WHO COVID-19 is the official application launched by the World Health Organisation (WHO) supported on both android and iOS interface. The application’s primary goal is to keep people informed during the coronavirus pandemic and to protect people from misinformation. It also offers alerts and notifications specific to the user’s location as well as helping with self-diagnosing the symptoms. The app also provides a link to donate to the WHO’s COVID-19 Solidarity Response Fund.

7.2 WhatsApp Chatbot WHO has launched a chatbot in partnership with WhatsApp to combat misinformation and spreading the proper guidelines regarding the COVID-19. As messages are encrypted on WhatsApp and are not traceable, meaning claims can be viewed by tens of millions of people without being fact-checked by any authority or media as a whole organisation.

7.3 GoK Direct GoK Direct is the application set up by the Government of Kerala in partnership with a local social communication platform. The app is available in different languages and is focused on spreading awareness and disseminating reliable information related to the disease. The app provides users with quarantine protocols, travel guidelines and general safety tips.

8 Tracking and Prediction of COVID-19 Patients Previously we have described the functioning and the algorithm behind the applications. Tracking of the patients is made easier by the mobile application which is helping to contain the virus. This GPS and Bluetooth based technology not only helps people to stay safe but also provides them with confidence to walk in any place by notification alert for vicinity near any red zone. These applications take basic data like the travelling history, health condition and give the result based on the assessment. These applications are providing facility to reassess yourself if there could be any threat to health and suggest user to keep location and Bluetooth services turned on. Many tracking applications are storing the data of the public, which help them to trace all person who was in contact with the person if get infected and it also helps to keep or suggest those people for self-quarantine. These applications serve

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various other functions like providing breathing assessment, temperature assessment, precautions, and medical contacts as well as authoritarian contact who are helping as a volunteer in a tough time.

9 Preventive Measure to Be Taken After Getting Notification from the Applications We all know that the supporting data and information on public health is important for knowing the trend of the outbreak during the early and late phase of the pandemic. Mobile technology data helps assist the spreading of pandemics. Hence, the government and most of the researchers in collaboration with private companies use mobile technologies for the estimation of the effect of controlling measures through these apps. Therefore, this section provides preventive measures to stop the transmission of COVID-19 outbreak [3]. The data of the mobile phone helps in the identification of COVID-19 infection (Fig. 4). The details of call records of mobile apps include the information of time and the cellular tower to which the mobile phone is joined, which are used for the tracking of individual behaviour of mobility. After the notification from the mobile apps, firstly we should aware of the situations, which helps in the understanding of the dynamic nature of the environment during a pandemic. The data from the smartphones also Fig. 4 Diagrammatic representation of the tracking of COVID-19 using mobile apps, mobile applications uses Wi-Fi, Bluetooth, and GPS to monitor the movements and giving alert to the healthy person to stay safe

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help the stakeholders to better understand the effect of COVID-19 transmission by utilizing the previously known dataset [21]. The second questions after the notification are the cause and effect, that helps in the identification of mechanisms and impact of COVID-19 spreading. With this, we all should know the variables that increase the difficulty during a pandemic and might be responsible for causes. Third, we should also know the chances of future outbreaks by understanding the count of infected cases and death of individuals. Last but not least, the assessment of patients helps in the determination of what are the various effects of COVID-19 spread during future epidemics. During the initial phase of COVID-19infection, the government announced for infected individuals and members of their family, to take the preventive measures of quarantine, adopting surveillance and various procedures of testing. The data of mobility of individuals as well as contact can help in knowing the information about individuals infected with the disease during future outbreaks. During the acceleration phase of COVID-19 infection, we should focus on the containment interventions, involving the social contact and restrictions on mobility, within the community. Finally, the deceleration and preparation phases of COVID-19 infection denotes the full restrictions on diseased individuals. The real-time data of hotspots and mobility will be helpful in understanding behaviour of COVID-19 infection and general measures have been taken such as restrictions on mobility, closing of schools, and banned on gathering of people especially in the red zones [22, 23].

10 Potential Application in Research Area and Future Perspectives There are many difficulties and challenges in taking face to face consultations from doctors during the COVID-19 pandemic. Therefore, an alternative method known as smartphone technology has played an important role in an outbreak. First and foremost, methods to reduce the risk of spread of the disease have been taken by the government such as lockdown and maintaining the social distancing norms. But the impact of COVID-19 has significantly increased day-by-day and disrupted the economic and social wellbeing of individuals. Recently the use of smartphone technology has continuously increased, as the individuals regularly update the data and information of COVID-19 outbreak [23]. The mobile technology contains many features such as Global Positioning Service (GPS) navigation, camera, sending and receiving email, video recording, and web-based applications for different purposes. This technology is also utilized in the field of medicine, education, health, business, social life, and so on. Mobile health (mHealth), has been utilized for delivering the information of health records. Recently, these technologies have proved to be helpful in the diagnosis of individuals with diabetes, and also the patients who are self-isolating in their homes. In today’s era, there is the revolution of fifth-generation (5G) mobile technology, which helps in the improvement of care of patients as well as the competence of doctors. It reduces the utilization of resources and equipment

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which further decrease the number of costs spent on healthcare. The use of 5G provides the virtual face to face clinic for monitoring of patients. As with the high speed of this technology, it increases the reliability of delivering and transfer of individual data files. With the utilization of 5G mobile technology, the surgeons can easily monitor the disease through the facility of high throughput 3D video, which creates the same environment as in the ICU with the use of artificial intelligence. The mobile technology by using AI can play a key role in six ways to fight against COVID19: (1) alerts and early warnings, (2) tracking and prediction, (3) data dashboard (4) diagnosis and prognosis (5) treatments and cures and (6) social control. During this pandemic, these technologies can also be utilized for the development of a vaccine against COVID-19 [23]. There are various other applications, uses mobile technology are listed as follows:

10.1 Radiology and Diagnosis The smartphone technology has been utilized as a tool for the diagnosis of COVID19cases and hence emerged as telehealth communication technology. Familiar to each individual, WhatsApp, has been utilized as a tool for imaging in medical (mHealth). In their study, Naqvi et al. examined the radiographic fractures with the use of mobile technology, where they sent the radiographs through multimedia messaging (MMS), and hence concluded that 97.7% is the accuracy having a sensitivity of 100% and specificity of 94.4%. They also concluded that WhatsApp is an accurate app for the application in radiology [24, 25].

10.2 Medicine and Healthcare During the lockdown situation, it is challenging for patients to go to hospitals. For the sake of the victims, most of the hospitals have initiated the services of telemedicine for infected individuals. These facilities provide the individuals of doctor’s advice and consultation through mobile technology.

10.3 Applications in the Surgery of Orthopaedics The injuries of muscles and skeleton are treated and managed by Virtual Fracture Clinics (VFC). The mobile technology uses VFC and helps in the monitoring of orthopaedic situations like osteoarthritis during COVID-19outbreak.

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10.4 Tertiary Referral Most of the studies described that the use of mobile technology helped in keeping away from irrelevant visits to the hospitals, which further helped in the diagnosis of individual patients.

10.5 Counselling Counselling of self-isolation and protecting themselves from infected individuals is regularly done with the use of smartphone technology.

10.6 Education and Training There is a significant impact of COVID-19 on education and training. Due to this pandemic most of the activities such as seminars, and workshops are either postponed or cancelled. Numerous online seminars have significantly increased with these mobile technology facilities. Almost all the Universities and institutions have closed and therefore all the teachers take the online classes with smartphone technology.

10.7 Group Counselling People in the group made counselling of advice on health by using apps such as Skype and Zoom and also providing the guidelines to maintain social distancing and other health problems.

10.8 Monitoring of Patients with COVID-19 To fight against COVID-19, it is necessary to find the exact location of the infected individual, which can be monitored with the use of GPS and Bluetooth present in smartphones. These might help know the information related to the death of individuals and the trending pattern of COVID-19 spreading.

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10.9 COVID-19and Mobile Technology in India The mobile application developed by the NIC (Ministry of Electronics and Information Technology, Government of India), tracks the COVID-19 patients is an ‘AarogyaSetu’. Access from iOS and Android, it uses the GPS and Bluetooth features of on smartphone for tracking of infected individuals.

11 Limitations and Challenges of mHealth Applications For mobile technology, its most rapidly evolving research area for computer scientists especially in terms of healthcare. While users are reporting problems, developer modifies in the source code and comes up with the updated versions of the app [26]. It is stated by a group of scientists in the UK from the University of Oxford, a take-up threshold of 60% of the population can bring an outbreak under control. In a yet digitalising country like India, where people are still not equipped with technology, mHealth has a major limitation. The accessibility of the applications for telemedicine has a technical dependency on the device’s supportive environment and also on the network connectivity in certain areas. Even the shortage of mHealth experts having the necessary in-depth knowledge has led to a decreased rate of mHealth technology usage. On an applicability level, managing personal health data online has raised a lot of questions about data security and privacy of mHealth means. The data storage for unsecured cloud computing providers with unknown locations, and the use of unsecured wireless networks to transmit protected information can lead to the threatening of data by a cybercriminal. So, security and privacy is a huge barrier for having a trusting acceptance of these mobile health technologies. Therefore, one must be cautious about the breaching of safety and security of the information and medical conditions. Below we enlisted a few major limitations of the mHealth application. • A key practical challenge to contact tracing over-phone is to make measurements with the accuracy of the distance between two devices. As per the reporting of Daniel Weitzer, a leader of the Privacy Automated Contact tracing (PACT) group at the MIT, USA, ‘signal strength can vary based on the orientation of the phone [27]. • To allow application work properly, there should be enough downloads. In most of the country, the application installation is voluntary which might cause low adaptation. The drawback of the application for excluding anyone who does not own a smartphone, including the older people and migrant workers has led to a decreased adaptation rate [28]. • Digital proximity used in most of the mobile applications has a limitation as it cannot store all situation of the user and may acquire COVID-19 and is effective only in terms of providing data to help with the response. These technological

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systems would need to include health services personnel, testing services and the GPS-based manual contact tracing infrastructures. • The location and personal data collected by such applications threaten fundamental human rights. Contact tracing apps for countries including China, Taiwan and Israel have also reportedly using CCTV footage and credit-card transactions. Researchers stated that technological interventions set up by these applications can be a great cause of state intrusion into people’s lives after the pandemic. • The source code of most of the applications used for surveillance is not available publicly. Users should be well informed to not develop a false sense of security while using these technologies. • Another problem faced by Bluetooth tracing apps is the large number of false positives resulting from aired Bluetooth chips that might ping two devices even when they are more than 6 feet away [29].

12 Effectiveness and Privacy Concern with Mobile Applications The effectiveness of digital proximity tracking applications largely remains unknown, and it could understand by using it in contact tracing. It also depends upon the level of uptake and the level of confidence that people have in the applications [26]. To overcome infectious disease like COVID-19, the best plausible method is social distancing. The contact tracing can be done using some mobile applications like TraceTogether, an application developed the Government of Singapore on March 20, 2020, for both iOS and Android. This app is used to detect infected persons [30]. However, there are many privacy-related issues which are associated with this like some implications could be that it discloses the name of the affected persons, therefore another alternative of simply displaying a unique user ID can be used [31]. The Government could reach the public health with the help of medical and other staffs while protecting the fundamental rights, by integrating the ethical considerations into the programming of new technologies. Data safety and privacy laws need to be in place, supported by additional legislation to provide a legal basis for data processing, and restrictions on data use. Also, the managers of the app should take these issues into account so that the details of the customers remain safe and protected [30]. Similarly, there are other applications like WeChat, QQ etc. which are developed by Chinese people to trace and track the infected individuals by taking mobile numbers, name, and ID of the people [31]. Both the electronic information and communication technologies have culminated in a new field known as telemedicine. WhatsApp is the most widely used social media application. Therefore, the usage of this app will surely help in the advancement and propagation of news about this pandemic COVID-19 [32]. There are several limitations due to various privacy issues arising through these applications, like leakage of phone numbers and other security problems [33]. The Indian Government recently launched an app called AarogyaSetu, an application which helps in the tracking of individuals suffering from COVID-19. This app

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provides an insight into the health status of the individual. But at the same time, there are certain lacunae related to this application as well like discrepancies in the exact or right status of the person [34]. Mass surveillance is done from the COVID-19 apps. Civil liberties union of America has given a set of rules for assisted contact tracking of the patients. Surveillance must have to be “lawful, necessary and proportionate”; extensions of monitoring and surveillance would have to only used with limited to COVID-19 purposes; data security and anonymity would have to be protected and report while asking for the protection with evidence; digital surveillance would have to address the risk of exacerbating discrimination and marginalization; sharing of the data to the third party is not allowed and described well in the low; there would have to safeguard against abuse and the rights of citizens to respond to abuses; “meaningful participation” by all “relevant stakeholders” would be required, including that of public health experts and marginalized groups [23, 35]. Device competence is also a big issue for most of the underdeveloped nations across the globe. Almost all the applications we have talked about requires a particular type of device for the app to function for example Arogya Setu app is applicable for iOS and Android devices [36]. The way Bluetooth works raises a few security questions as well just like all wireless networking setups, there’s always the legitimate concern of sending personal data using radio waves and that data falling into the wrong hands. When Bluetooth first came out, it was really easy for someone to access your data without your permission. But over time this technology has become more secure. Bluetooth manufacturers are aware of the risks, so they’ve already done a lot to make devices more protected against security threats. You see, in almost all of our gadgets, there’s the “trusted devices” option that enables you to share data without permission while others need permission to access your device. But let’s not forget about spam, there’s this trend called bluejacking where a person or company can send you their electronic business card or an ad as a text message using Bluetooth, of course when you see that you will either ignore it or you will panic. This is something that mostly happens in public places where everyone is using their phones. You can prevent it from happening to you by making your Bluetooth de-vice non-discoverable when you’re out and about. On the behalf of COVID-19 Blue-tooth is used as a new privacy-preserving Bluetooth protocol which generally supports contact tracing. Due to Contact tracing, it is possible to fight the spreading of COVID-19 viral infection via clueing and alerting the public peoples who they were recently in contact with and who have already been tested positive with the virus. And this becomes possible by Bluetooth LE (Low Energy) for proximity detection of nearby smartphones, and the data exchange mechanism. According to the WHO ‘There is no scientific evidence to confirm that exposure to low levels of electromagnetic fields has any negative effects on our health’. In the case of Wi-Fi sometimes even thick brick walls and concrete can hinder the signal- you just probably won’t notice the difference it is teeny-tiny, another highly technical scientific measurement. Just smaller than a “tad” and there you have it, folks. Now that you know little more about Wi-Fi you spend hours and hours each day, perhaps you’ll appreciate it that much more. The phones with GPS enabled technology works accurately within a radius of around 4.9 metres (16 ft)

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under the open sky. And hence sometimes GPS takes people to unexpected places or traces the wrong location in the rural areas because of too congested residences. So this technology is accurate in the places where the houses are less congested. Sometimes GPS traces the wrong location, it is because of Satellite signal blockage by the concrete ceiling and walls present in the Buildings, highways, Tunnels, Bridges, etc. Since Signals are reflected by these Concrete walls and ceilings. Many other factors, by which this technology fails is, in the Storms or there could be radio interference/jamming. Also, GPS hard-ware works fine but the location of an object is not accurate, this can be happened due to software faults like incorrect maps drawn, missing roads, buildings, and communities, etc. Also, errors could be due to minor changes in satellite symmetry, atmospheric conditions, receiver design, and quality.

13 Conclusion In the present time of the COVID-19 pandemic, the biggest challenge for individuals as well as the government in global health. As an effort to flat the curve and reduce R-value, various governments opted for multiple steps including lockdown and homequarantine which even proved to be effective in many countries. As an effort, every computer scientists and bioinformaticians focused on mobile technology as a solution for serving the health of a society as a whole. The reason for exploring up this option was its efficiency, rapid nature, guarded user privacy as well as the ability to bridge the data gap between healthcare staffs and individuals accurately. It also enabled the rapid tracing of infected person location as well as and alerting the user visits to the red and containment zones to make sure the ultimate containment of the virus. As proof of support, Table 1 presented in this present article enlisted the various countries along with the name of their mobile applications currently in use. However, there are still many applications that need a license from the respective app store because of huge privacy concerns. As of now, only the big technology giants have researched on these mobile applications to combat and prepare for the future risk of any pandemic. But, on the long way to the future, a new generation of better and accurate mobile applications will be developed by small companies too for prevention and surveillance at the local scale. This is because it is a new field for research and entrepreneurship for mobile app developers. Lastly, to acknowledge the ongoing COVID-19 pandemic, the flexibility, and robustness of individuals, organizations and various governmental bodies as one unit is need of the hour for data sharing and its coordination for managing this current and future undesirable outbreaks.

References 1. Standing, S., & Standing, C. (2008). Mobile technology and healthcare: The adoption issues and systemic problems. International Journal of Electronic Healthcare, 4(3–4), 221–235.

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The Role of Internet of Things (IoT) in the Containment and Spread of the Novel COVID-19 Pandemic Ibrahim Babangida Mohammed and Salmi Mohd Isa

Abstract The novel COVID-19 pandemic is hitting the strongest economies in an unprecedented manner leading to the crippling of most economic sectors globally. Movement restriction order profoundly affected many industries, including manufacturing, transportation, aviation, education, tourism, and trade and investment, among others. The consequences resulted in people losing their jobs, corporate organizations and the Government experiencing a sharp drop in income and revenue. Similarly, the global crude oil market prices crash to the lowest rate of less than USD30/barrel. In recent times, the world has not witnessed a pandemic that threatened human existence without any sigh of relief as no cure has been found for the disease. The most effective recommended measure in containing the chain of transmitting the virus is through social distancing as a large gathering of people is highly discouraged. Internet of Things (IoT) alongside other related technologies such as artificial intelligence (AI), drones, robotics, Big Data, and e-learning related technologies were found as platforms that can play a critical role in breaking the chain of the virus transmission. This study highlighted the role of IoT related technologies as a measure that enhances human-machine interaction, which supports the social distancing among people. Keywords Internet of things · Artificial intelligence · Robot · Drone · COVID-19 pandemic

I. B. Mohammed (B) · S. M. Isa Graduate School of Business, Universiti Sains Malaysia, 11800 Penang Island, Malaysia e-mail: [email protected] S. M. Isa e-mail: [email protected] I. B. Mohammed Department of Management and Information Technology, Abubakar Tafawa Balewa University, Bauchi PMB 0248, Bauchi State, Nigeria © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 K. Raza (ed.), Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, Studies in Computational Intelligence 923, https://doi.org/10.1007/978-981-15-8534-0_6

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1 Introduction Advancement in cloud computing technology, Big Data, artificial intelligence (AI), smart sensors technology, 5G telecommunications network, and IoT have made a tremendous contribution to remote or virtual health care services [1] and digital health in general [2]. The coronavirus popularly known as Covid-19 came to the public limelight in December 2019 at the Huanan seafood market in Wuhan city in the Republic of China, where an individual was reported to have been infected with the virus from an animal. Statistics from world health organization (WHO) revealed that the total confirmed cases are approaching 10 million markswith almost half a million-death toll recorded across the globe. The United State of America, Brazil, Russia, India, United Kingdom, Spain, Peru, Chile, Italy, and Iran are the top ten countries with the highest number of infections. Furthermore, the global economy is adversely affected by the impact of the pandemic because of partial and total lockdown imposed by countries. The consequences resulted in people losing their job, corporate organizations and Government having a drop in income and revenue. The global crude oil market witnessed a sharp fall in prices. These are clear indicators of an imminent global financial crisis and economic recession facing the world. However, new-age technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data are considered technologies that can help in reducing the rate of transmission of the virus through social distancing. Health practitioners have recommended good hygiene and social distancing where there is less physical contact to break the chain of spread. These are measures to minimize the infection rate as no drug has been found to cure the virus. Technology has been a critical component that is helpful to the safety of human beings on the planet. The health service sector keeps involving as technology continued to play a prominent role [3]. As social distancing and lack of physical contact are highly encouraged during this period, drones can be used in supplying medical and food support to areas that are identified as red zones. Similarly, AI technologies such as robots can play a critical role in identifying individuals infected with the virus without human contact. Furthermore, sensors are equally crucial in generating records or data regarding individuals’ health records or status. These are components of IoT technologies that can play a critical role in breaking the chain of transmitting the virus as a machine replaces human interaction. Therefore, the fast spread of the COVID-19 can be contained using the various IoT smart devices, as explained in the chapter. This chapter, as a reviewed paper, described IoT and the three major phases of IoT. It also highlights the application of IoT in new-age technologies such as robots, drones, AI, Big Dataas tools that support less physical interaction during the COVID19. Furthermore, factors that contribute to the success and widespread usage of IoT devices across industries were also discussed. The chapter ended with a conclusion by highlighting the challenges and future direction for further studies on IoT.

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2 Internet of Things The IoT has been described “as a concept that gathers all sorts of different applications based on the convergence of smart objects and the Internet, establishing integration between the physical and the cyber worlds” [4]. It allows seamless interaction between devices and the physical environment. Healthcare delivery [5] and public safety or environmental monitoring are among the key areas where IoT is highly applicable. It includes critical aspects related to medical data and records, patient care, and telemedicine in general. It is estimated that by the year 2025, there will be more than 24.9 billion IoT connections [6] across the globe. Therefore, it means that the total number of IoT connected devices will surpass the estimated world population of 8.1 billion people. It highlights how computer-computer interaction will have an exponential increase to minimize human-human interaction. Similarly, digital technology has, over the years,have been found to improve access to resources and adds value to the whole society [7]. With the ravaging Covid-19 pandemic that shuts down countries and their economic activities, IoT is a critical component of ICT that can be leveraged to minimize the rate of transmitting the virus across. Table 1 highlighted some factors that contributed to the widespread use of IoT devices across industries.

2.1 Phases of IoT According to [8], there are three phases in which the physical-cyber world or virtual reality interaction exists. In a nutshell, IoT operations took place in three distinct stages. The phases are the data collection phase, data transmission phase and data processing, management, and utilization phase. At the data collection phase, many technologies are applied to detect or collect data from the physical environment. The data collected using the devices can be communicated through various channels known as the data transmission phase. Finally, the transmitted data must be effectively processed and managed to make meaning or value. Each step or stage is described below as related to the novel COVID-19 management. Table 1 IoT usage factors

S. No.

Factors

1

Decreasing the cost of sensors

2

Increased connectivity

3

Advancement in computing power

4

Broadband penetration

5

5G network technology

6

Miniaturization of chips embedded system

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Data Collection Phase

Data collection is the first stage, and as the name implies, it involves the use of different communication technologies for sensing and retrieving data from the physical world. Real-time physical environment information can be captured using several technologies such as radio-frequency identification (RFID), wireless sensor networks (WSN) (sensors, cameras) near field communication (NFC) technology, and Bluetooth technology. Sensor nodes such as tolls, cameras, global positioning system (GPS) terminals can play a critical role in collecting data on COVID-19 patients remotely, monitors the environment, and keep track of records based on the peoples’ interaction. They are essential in the data collection as there will be less human physical interaction, which is a cardinal point in breaking the chain of the virus transmission. It is widely believed that COVID-19 is best transmitted through close person-toperson contact [9]. The use of the mentioned data collection devices can play a critical in minimizing physical contact between the potentially infected people and medical personnel. Such smart IoT data collection devices allow human-machine interaction, which reduces the high chances of transmitting the virus from one person to the other. Therefore, they can be used as tools containing the spread of the pandemic.

2.1.2

Data Transmission Phase

The collected data must be transmitted via a specific medium and standard protocol for it to be relevant and useful. Immediately the data has been captured from the physical environment; it is to be communicated across the network for onward transmission to the host system or servers for end users processing. The transfer of the data can be done using different means such as wired, wireless or satellite depending on the data type, distance, and location. Ethernet (IEEE 802.3) standard is one of the wired technologies that can transmit data via IoT devices using different communication technologies such as twisted-pair copper wire, coaxial cables, and fiber optics. The data transmitted via such means were more reliable. However, it involves a lot of costs as a physical connection is required for the primary system or host server application. Similarly, wireless local area network (WLAN) such as WiFi (IEEE 802.11a/b/g/n) is used for shorter distance communication and WiMAX (IEEE 802.16) for long-range transmission. Furthermore, broadband technologies, cellular networks (global system for mobile (GSM) communications, general packet radio service (GPRS), long term evolution (LTE)), satellite communication technologies and power line communications (PLC) has been for decades as the mainstream form of data transmission in IoT. The emerging fifth-generation (5G) network technology designed to connect virtually everything (machines, objects, devices, human) due to its flexible nature and other related wireless technologies will be the central communication paradigm for the future IoT world. These technologies can play a critical role in the containment of the dreaded COVID-19 pandemic across various sectors. These data transmission

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technologies can assist in wireless contact tracing of infected people, attending to COVID-19 patients virtually, and providing relief materials such as food and drugs to affected areas. It supports all forms of data (text, audio, video, multimedia) that is transmitted over the network. Therefore, the overall aim of the transmission phase is to transmit the data collected to respective applications and, subsequently, to the end users [4]. The wired transmitting channels can transmit more accurate and reliable data as tendencies for distortion is minimal. However, for long-distance communication, wired channels may not be feasible. Similarly, it is highly expensive compared to wireless means. It is essential to understand the best transmission channel that is suitable to the nature of the data and the terrain of the environment

2.1.3

Data Processing, Management, and Utilization Phase

In the last phase, the collected data that is transmitted over the network needs to be correctly processed and effectively managed for decision making. The real information collected from the physical environment that is transmitted through IoT devices is gone through several processes such as sorting, filtering, analysis for it to make meaning. Concerning the containment of the COVID-19 pandemic, IoT applications will receive the transmitted data based on the captured information. This information will form the basis upon which critical decisions can be made on the way forward. Services can be remotely managed [8] using cloud computing technology. Cloud is termed as the home of IoT applications [10], where applications use virtual storage and computational resources. It is essential in the effective management and utilization of data collected. Similarly, IoT terminals can make good use of the data streamed from the cloud space. End-users of the data collected through various IoT applications can take good advantage of cloud computing technology to effectively processed and managed the data related to COVID-19 without physical human interaction. Figure 1 depicts the graphical presentation of the three IoT phases of data collection, data transmission and data processing, management, and utilization phase.

3 Application of IoT in Containing the COVID-19 The healthcare industry is increasingly witnessing the emergence of smart IoT devices that have a significant influence on the daily activities of both medical practitioners and their patients [11]. Therefore, IoT technology can be applied in many ways to contain the spread of the novel COVID-19 pandemic. Such areas where IoT can be relevant in support of social distancing as a measure is discussed in the sections below.

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Fig. 1 Phases of IoT

3.1 The Use of GPS in Contact Tracing of Infected People Health experts said that a person carrying the virus has a high chance of infecting a minimum of 3 or more people that interacted with the carrier of COVID-19. It implied that tracing the places visited by an infected person to identify the contacts is essential. The global positioning system (GPS), provides geolocation and time information to a GPS receiver based on the movement of the user. The GPS does not require the user to transmit any data, and it operates independently of any telephonic or internet reception. However, these technologies can enhance the usefulness of the GPS positioning information. It usually shows the places visited by an individual through their mobile phones. As most people use smartphones that are connected to the internet, this is a good and easy way of tracking individuals by identifying their locations and the people they mingled. It can be a great way of tracing the possible contacts made with an infected individual. However, others argued that GPS is not highly secured and can be susceptible to attack [12]. They further said that in a situation where GPS-enabled device is switched off, the device could not track anymore. That can make monitoring difficult as the owner keeps moving from one point to the other. Despite such shortcoming of GPS-enabled devices, it can still be used as a mechanism of containing the spread of the COVID-19 pandemic in such a way that it keeps a record of the places visited.

3.2 The Use of Drones for Supplies to Infected Areas Drone refers to an unmanned aerial vehicle that is operated either autonomous or remotely [13]. Military forces initially used it during the war for surveillance and

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attack on enemies’ territories. However, its’ usage has now been extended to healthrelated issues such as medical and food supplies, disaster relief, and fumigation of infected areas. This technology can be applied as a measure in containing the spread of the COVID-19 pandemic through the application of drone as an AI device to supply medication, food items, and other relief materials to an area identified with a high rate of infection. Such will ensure less contact and minimize the spread of the disease. Therefore, drone technology can be considered as one of the IoT smart devices that is helpful in the face of such a global pandemic where less physical contact is encouraged. It can similarly be applied in taking care of the infected COVID-19 patients where physical contact with the medical practitioners is not needed. This is one of the many ways in which IoT supports social distancing as a measure of containing the virus spread.

3.3 The Application of Telemedicine in Consultation The term telemedicine is defined as “a platform that allows patients to be seen by healthcare providers from any location using a smartphone, tablet, and computer with audio and video capabilities” [14]. It allows an infected patient to interact with a physician virtually irrespective of their geographical location. It has been reported that medical personnel got infected through physical contact with their patients due to a lack of protective equipment. Therefore, for cases that are not severe, consultations through video technologies will help in breaking the chain of transmission from patients to health workers. Similarly, the non-COVID-19 patient can be consulted through telemedicine technology as many people that have other illnesses fear to have physical contact with their physicians. This concept is useful at this critical moment of the pandemic as less physical interaction is highly encouraged. Similarly, senior citizens suffering from other deadly diseases such as diabetes, high blood pressure, kidney issues can be attended to via various telemedicine technologies from home. This will make sure that they stay far away from hospitals where COVID-19 patients are treated because the COVID-19 is more deadly to older people, as most of them have one or more health issues. Therefore, telemedicine technology can be used as a tool for social distancing that can contain the spread of the COVID-19 pandemic.

3.4 The Use of Robotics for Patience Examination and Monitoring Artificial intelligence(AI) can be defined as “a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific

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goals and tasks through flexible adaptation” [15]. Robots are one of the AI devices that can interact with human beings in real-life simulation. As predicted, that AI will have a tremendous impact on many aspects of human life [16] of which health is inclusive. The use of robot technology in examining individuals and monitoring confirmed cases of COVID-19 would go a long way in minimizing the rate of transmission because there will be less physical contact between suspected cases, confirmed cases, and medical personnel. It is therefore identified as one of the areas where the IoT technology can be used in containing spread of the disease. Furthermore, smart biosensors are now considered as an IoT technology that is applied in detecting and diagnosing ailment [17]. They have been widely appreciated by both medical practitioners and patients as well. Therefore, the biosensors as smart IoT devices can be a major tool that can be used in diagnosing or taking a sample of individuals suspected of having COVID-19. This will ensure less physical contact and minimize infection rates. It can a good way of containing the spread of the virus.

3.5 The Use of E-learning Technologies for Social Distancing As physical interaction is highly discouraged, e-learning platforms can serve as an alternative channel of teaching and learning process. One of the benefits the students can derive from the use of the online learning platform is the social value [18]. As highlighted in the literature, the new generation of digital-native students that can quickly adapt to the digital environment [19] without much difficulty is critical in the face of this global pandemic. Students can participate in online classes via platforms such as WebEx meetings, Zoom video communication, Skype meeting, and other related video conferencing technologies. Similarly, viva voce sessions, academic board meetings, workshops, seminars, and academic conferences can be held virtually without physical contact. Leveraging on such technology is an alternative way of sustaining the social distancing as a measure in containing the spread of the novel COVID-19 among the academic community. Furthermore, computer-based assessment systems can be used for online examination. It is to ensure that the system keeps functioning, and the health status of the stakeholders (students, teachers/lecturers, administrative staff) is not compromised. With such technology, student assessment can be done safely as physical interaction has been avoided. Promotion interviews, staff performance assessment, and surveys can be carried out using an online platform in the workplace to ensure the safety of the staff. This is an essential technology in containing the spread of the COVID-19 pandemic.

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3.6 The Use of Big Data Technology in IoT Data science is the extraction of knowledge from high volume data-sets by the use of computing science and statistics [20]. Big Data is generally described in terms of the 5 Vs of volume (data size), veracity (speed) variety (structure/type), value (data analytics) velocity (data privacy, and governance. We are living in a world that is becoming data-driven where everything is about data. In such a situation, the voluminous data generated from multiple sources through smart devices such as sensors, smart cards, tolls, e-platforms (e-commerce, e-government) provides an excellent opportunity for institutions and businesses. In the face of the COVID-19 pandemic that crippled many companies, there was a gradual shift to a more digitally economy based where products and services are delivered electronically. Applications such as Food Panda, Airbnb, Grab car are complementing the conventional system of service delivery. In this perspective, Big Data can play a critical as businesses can leverage it as a pillar for transactions between clients. Such will allow customers to transact with less human-human interaction. It is also another way of breaking the chain of transmitting the virus. IoT is regarded as one of the major domains where Big Data technology is widely applied as such corporate institutions can leverage Big Data into IoT. Similarly, Big Data Analytics (BDA) tools can be used to extract valuable information from the large data-set generated through multiple sources. Critical decisions can be made that will make a significant impact on people’s life. Furthermore, Big Data technology can provide valuable information on the citizen’s health status and level of compliance with standard operating procedures (SOP) during the pandemic. Such information can be useful to the Government in making decisions on designing policies that will suit the nature of each area concerning the information available. Age, gender, income, previous health status can provide a hint on how prone individuals are to the disease. Therefore, Big Data can be an essential IoT domain that can support the containment of the spread of the virus.

4 Conclusion In the face of such a global pandemic that continues to register casualties in terms of infection and death toll, it is essential to find the best way of minimizing the rate of transmission without interruption in service delivery. This is because in the long-run lockdown may not be a feasible and lasting solution. Corporate organizations, businesses, and institutions can leverage IoT and other related technologies that support less human physical contact to sustain social distancing. The chain of transmitting the virus can be broken using technology, while business activities are not halted. Similarly, medical health practitioners will be more comfortable attending to patients virtually in this period to minimize fear of infection. Therefore, technology

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in general and IoT specifically can play a significant role in containing the spread of the COVID-19 pandemic that is wreaking havoc on human life. However, the application or use of IoT in containing the spread of the pandemic can face particular challenges, especially in developing countries where ICT infrastructure is not readily available. Telecommunications technology remains a critical component that IoT applications thrive. Developing countries from sub-Saharan Africa where broadband penetration is low cannot fully utilize IoT applications in containing the spread of the virus. Similarly, IT skill competency is also essential in handling IoT applications or devices. Individuals are more comfortable interacting physically with a fellow human being than a machine. Therefore, virtual interaction may look strange, as there is no human feeling or touch. Others might perceive that their privacy can be breached while disclosing medical records or health status to the machine. Such issues pose significant challenges in IoT application usage in containing the spread of the pandemic. Despite the challenges, IoT is still considered as a technology that can support the containment of the virus. As such,the area provides opportunities for further investigation on issues related to human-computer interaction in health-related fields. Privacy concerns of end-users concerning virtual examination or medical recordkeeping can be addressed through such studies. Therefore, further studies can investigate the role of IoT in minimizing the effect of the post-COVID-19 pandemic in critical sectors such as agriculture. This is because recent advancements in sensor technologies gave rise to DNA-reading sensors that can be applied to food production [21]. Furthermore, Electronic Medical Records (EMRs) data can be used for research studies in areas related to medical, biomedical, or epidemiological research.

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Computational Intelligence in Prediction and Diagnosis of COVID-19

A Review on Predictive Systems and Data Models for COVID-19 Fatima Nazish Khan, Ayesha Ayubi Khanam, Ayyagari Ramlal, and Shaban Ahmad

Abstract Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or novel Coronavirus, responsible for the transmission of Coronavirus disease (COVID19), represents the causative agent of a conceivably deadly sickness, and a global public health concern. In December 2019 in China (Wuhan), the spread of SARSCoV-2 has taken the shape of a pandemic and affects the respiratory system and manifests as pneumonia in humans, influencing more than 216 nations so far. On January 12th, 2020, the World Health Organization (WHO) gave the name “2019nCoV” for 2019 novel Coronavirus, and the infection further on February 11th, 2020, is authoritatively named as COVID-19. Instead of using various predictive systems and data models, the prevalence of COVID-19 is continuously increasing, affecting millions of individuals. This chapter focuses on predictive systems and data models utilized from the beginning of COVID-19 outbreak that helped in predicting the cases and deaths qualities of COVID-19 in the desire for giving a reference to future investigations and help in controlling the spread of further epidemics. And also suggest how these data models can help and enable policymakers to plan the regional and national healthcare systems required and design monitored active plans. Keywords Novel coronavirus · COVID-19 · SARS-CoV2 · Predictive systems · Data models

F. N. Khan (B) · A. A. Khanam · S. Ahmad Department of Computer Science, Jamia Millia Islamia, New Delhi, India e-mail: [email protected] A. A. Khanam e-mail: [email protected] S. Ahmad e-mail: [email protected] A. Ramlal Department of Botany, University of Delhi, Delhi 110007, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 K. Raza (ed.), Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, Studies in Computational Intelligence 923, https://doi.org/10.1007/978-981-15-8534-0_7

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1 Introduction Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a novel human coronavirus, was originated from the animal and seafood market in the Wuhan city of Hubei province in mainland China during December 2019 and accounted as a progression of unknown cases of pneumonia throughout the city [1, 2]. As per the classification system, it belongs to the Nidovirales (order) and Coronaviridae (family), with a genome size of ~30 kb, enveloped in single-stranded positive-strand RNA [1, 2]. On January 12th, 2020, the World Health Organization (WHO) gave the name “2019-nCoV” for 2019 novel Coronavirus. It was declared as a national emergency of global concern by the WHO on January 30, 2020, and announced it as pandemic [3]. And further, the WHO on 11th February 2020, authoritatively named the infection brought about by the 2019-nCoV as Coronavirus disease 2019 (COVID19). According to the report of Chinese Center for Disease Control and Prevention (CCDCP), it has been argued that the number of cases of infections reaches to millions only not in China but also throughout the globe, i.e. they are exponentially increasing from March 16th 2020. As of now (June 2020), the number of diseased individuals, and also the death count, is growing rapidly throughout the world, and the topmost countries are USA, Russia, India, Brazil, and the UK. Centres for Disease Control and Prevention (CDC, USA) also announced that the virus might be spread through the contact of infected individuals, by touching the infected surfaces, or the contamination with faeces, and the isolation period is 14 days [2]. The common symptoms those seen in patients fever, cold, cough, and difficulty in breathing and the older individuals which have previously known medical problems, such as lung-related symptoms and low immunity are more susceptible to COVID-19 [2]. We can prevent ourselves by frequently washing the hands, keeping social distancing, as well as keep away our hands to touch with face, nose, and mouth [4]. Most of the studies considered that the reservoirs and carriers of the coronavirus are snakes and bats but till yet there is no confirmation regarding the source of COVID-19 (as shown in Fig. 1). It is important to estimate and understand the transmission dynamics of disease in the initial stages of the outburst. One can easily look into the epidemiological situations of disease with the estimation of the dynamic pattern of transmission with due time and can understand the quantifiable effects of the methods used in outbreak controlling [5–7]. With the help of these analyses, the potential transmission and spread of the disease in future outbreaks can be predicted, and help other underdeveloped countries in the estimation of risk to disease by guiding them to develop new effective interventions [8–10]. But sometimes these analyses have many challenges, especially in the real-time system, for example, delay in the appearance of symptom or delay and uncertainty in the detection of cases [11]. So, the modelling technologies such as predictive systems and data models (mathematical, statistical and computational models) can help in the delays process of infection. By analysing the spread of this virus, it seems not to controllable for now, anyhow it looks like to become a seasonal threat like swine flue etc. Many studies used the criteria of isolation and tracing of contacts of cases which are confirmed for the detection

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Fig. 1 Transmission of COVID-19 infection throughout the world (Figure courtesy: Blocks of figures retrieved from Google images (using “labeled for reuse”))

of COVID-19, but these methods are not so effective if there is transmission before the onset of the symptoms [7, 8]. These methods may also be helpful and show importance where the transmission and onset of symptoms are simultaneous such as in the Middle East Respiratory Syndrome (MERS) [12, 13], Ebola virus disease [10, 11, 14] and so on. The transmission even before the symptom’s onset can be stopped by using the strategy of tracing of contacts and testing of confirmed cases. Throughout the world most of the researchers, clinicians as well as policymakers continuously finding the methods of how to monitored and control the spread of disease. These issues require various methods of prediction analysis as well as data models, which are useful for the government and policymakers to take a relevant decision for the future, to fight against any of the outbreak. So, the prediction models play an important and crucial role in identifying the features for the estimation of the risk of individuals being infected. This chapter aimed to systematically deliver the review of the currently available predictive systems and data models that helped in predicting the cases of infections and also the deaths qualities of COVID-19 from the initial stages of the pandemic. It mainly focuses on mathematical, statistical and computation (AI) based models concerning COVID-19 which desire for giving a reference to future research and help for the preventing and controlling of the COVID-19 pandemic. Furthermore, it will highlight some of the challenges of using these prediction models and suggest how these data models can help and enable

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policymakers to plan the regional and national healthcare systems required (like developing new intensive care units [ICUs]) and design monitored active plans.

2 Overview of Survey The chapter deals with the background of some proposed predictive models for COVID-19 in Sect. 3. Section 4 of the chapter focuses on how artificial intelligence can be used to fight against the COVID-19 wherein the AI can be used in the areas from giving warning from the initial stage to tracking and prediction to controlling measures. In Sect. 5, predictive systems have been discussed which is followed by the description of some data models in Sect. 6. While in Sect. 7, how these (mentioned earlier) models will help in the prediction and diagnostic have been discussed. Sections 8 and 9 consists of challenges and future prospective. Figure 2 represents the complete study in a flowchart for better understanding. The figure gives enough information and correlation of the information about the study.

Fig. 2 Flowchart giving an overview of the chapter

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3 Background of Predictive Systems and Proposed Models According to the general as well as statistical information available on the official site such as WHO, CDC, Worldometer, etc., millions of positive cases and deaths of COVID-19 have been seen around the world. With the rapid increase and spread of COVID-19 cases, many studies have been accomplished in a short period for the prediction, trending pattern and effect of the disease. This section briefly gives the idea of recently published studies related to prediction systems and data models of COVID-19, as shown in Table 1.

4 Artificial Intelligence to Fight Against COVID-19 John McCarthy introduced the phrase “artificial intelligence (AI)” in 1956. While it seems that Alan Turing’s Turing test designed for distinguishing behaviour of human and machine have given rise to the idea of human behaviour simulation by machines. Artificial intelligence is the mimicking the human behaviour to increase the potential of machines as well as to make it more capable to work smarter (i.e. faster in a shorter time) [15]. After that computational capability has increased for immediate computation, for apprising new data likewise earlier evaluated data in real data. Artificial intelligence is our talkfest in different application like Google assistants, Siri, Alexa (as the personal assistant), air transportation, computational games, automated mass transportation etc. Now a day’s artificial intelligence is also integrated with medicine which enhances processes and acquires better efficiency for better patient care and gives rise to improved healthcare. In the medical field, the integration of artificial intelligence enhances the efficiency of treatment and diagnosis process. Machine learning helps in analysing electronic medical records, patient’s pathological slides, radiological images etc. Therefore, artificial intelligence has propitious application in the medical field [16, 17]. AI can play a key role in six ways to fight against COVID-19: (1) alerts and early warnings, (2) tracking and prediction, (3) data dashboard (4) diagnosis and prognosis (5) treatments and cures and (6) social control, as shown in Fig. 3

4.1 Alerts and Early Warnings Before an uncertain frenzied an early alarm is required that can communicate information among endangered people about their imminent danger so that possible actions can be taken before a hazardous situation to reduce the problem. At the time of natural disasters like volcano eruptions and hurricanes, early warning alerts are used. Similarly, early warning alarms are used for infectious disease epidemics. As the COVID-19 is a global epidemic and becoming health crisis as well, to save mankind

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Table 1 Predictive systems and proposed data models of COVID-19 Predictive models Description

Outcomes

AI and machine learning method

Treatment and production of vaccine and drug against COVID-19

Drug [39–44] ‘atazanavir’ is repurposed in 2020 for the treatment of COVID-19. Baricitinib for the treatment of myelofibrosis and rheumatoid arthritis in 2020

References

AI model and technologies of radiology integrated with AI

Diagnosis of COVID-19 infection by Diagnosed researchers from Dutch University, March COVID-19 2020 by utilizing the images of X-rays infection

[36]

Group Method of AI model proposed by Ivakhnenko for the Data Handling prediction of COVID-19 (GMDH)

Used for [109] many different purposes like in recognising patterns, optimization of complex systems, predictions, etc. Referred to as polynomial of the Ivakhnenko equation

AI-based model

BlueDot, launched in Canada on December 31st 2019

Helped in the [18] prediction of the outbreak of COVID-19

HealthMap, launched in the USA at Boston Children’s Hospital, even before the BlueDot4 (i.e. December 30th, 2019)

Helped in the [18] prediction of the outbreak of COVID-19

Applied AI applications against COVID-19 in 2020, for the diagnosis (Computed Tomography (CT) scans and X-rays) of COVID-19

Predicted that [31] it is a cheapest and fastest technique that saves the time of radiologist (continued)

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Table 1 (continued) Predictive models Description Prediction of COVID-19, by used only 53 victims from two hospitals in China

Outcomes

References

COVID-19 predicted

[38]

Adaptive Combined both the features of artificial Neuro-Fuzzy neural networks (ANN) and fuzzy logic Inference System systems (ANFIS)

Utilized for [110–118] the estimation of time series and forecasting difficulties by combining both the features of ANN and fuzzy logic systems, in forecasting model of stock prices, product development performance, electricity prices and loads, return products, building energy consumption

ANFIS model (FPASSA)

Found the best solution of many confirmed cases of COVID-19

Presented the ANFIS model by optimizing and improving with the help of FPA and SSA, which initiate the process of taking out the historical COVID-19 dataset

ANFIS model Authors proposed the model for the with SI improvement of the ANFIS model algorithms - GA (genetic algorithm) and Salp Swarm Algorithm (SSA)

[119]

Used for the [120] forecasting of time series data. Currently, numbers of studies are proposed for forecasting the number of confirmed cases of COVID-19 (continued)

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Table 1 (continued) Predictive models Description

Outcomes

References

Compartmental model

Forecasted spillover risk of transmission of disease and also the cases of human West Nile Virus (WNV) by utilizing the old age data from New York (Long Island) of WNY outburst of 2001–2014.

Forecasted spillover risk of transmission of disease

[75]

COVID-Net (deep convolutional neural network)

Diagnosis and treatment of COVID-19 by using the radiography images from the chest

Diagnosed COVID-19 infection

[34]

Deep learning model

Utilized for the COVID-19 diagnosis with Diagnosed the help of images produced by X-ray. patients with COVID-19

[32]

Diagnosis of COVID-19 infection by utilizing the images of CT scans

[35]

Diagnosed COVID-19 infection

Dynamic mathematical model

A model to investigate the virus’s Proposed for behaviours by utilizing the suitable dataset the of COVID-19 infection investigation of the virus’s behaviours.

[121]

Dynamic model along with the Bayesian inference

Studies for forecasting the Ebola virus outbursts in African countries (such as Liberia, Sierra Leone, and Guinea)

Forecasted the [106] outbursts by giving the dynamic model

Epidemiological model (SIR)

Used for the forecasting of the spread of COVID-19

Helped [28] people to take the measures by the government such as quarantines, lockdowns, prescriptions of social distancing and so on

Forecasting model

Estimating the seasonal outburst of influenza for 6 years data (2003–2008) of New York City-based on the integrating and adjusting the Kalman filter

Estimating the seasonal outburst of influenza

[104]

Estimating the weekly seasonal outburst of Noticed the influenza based on the integrating and illness like adjusting the Kalman filter, influenza susceptible-infected-recovered-susceptible

[105]

(continued)

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Table 1 (continued) Predictive models Description

Forecasting model: ANFIS model with other algorithms

Outcomes

References

Estimating the rate of influenza A (H1N1-2009)

Estimated the rate of influenza A

[108]

Time series for forecasting (TAIEX), using ANFIS in combination with OWA (ordered weighted averaging), To improve the ANFIS parameters former studies also utilized swarm intelligence (SI) approaches such as social-spider optimization, PSO (particle swarm optimization), and MVO (multi-verse optimizer), SCA (sine-cosine algorithm), to increase the outcomes of time series forecasting models.

Increased the outcomes of time series forecasting models

[112, 122–128]

Forecasting Comparative analysis to predict the models infected amount of hepatitis A virus by (Auto-Regressive utilizing the data of Turkey (13 years data) Integrated Moving Average (ARIMA), multi-layer perceptron (MLP), radial basis function (RBF), and time-delay neural networks)

The best [103] outcome was recorded from MLP

Machine Learning-based algorithm

Prediction of risk of mortality in Predicts individuals, by taking dataset of 29 victims prognosis of from Tongji Hospital of China (Wuhan) any person

[37]

Mathematical model

Estimated the actual number of Up to January [100] coronavirus infected cases in January 2020 15th 2020, there are 469 infected cases which results from the unreported cases. Additionally, later in their estimation after January 17th 2020, the infected cases enhanced to 21 times of the previous one (continued)

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Table 1 (continued) Predictive models Description

Outcomes

References

Predicted the actual risk of transmission of Estimated that [72] COVID-19 6.47 is estimated reproduction number (R0 ) and also there are confirmed cases estimated from 23-29 January 2020, Also expected for a high number of cases, which are confirmed after January 23rd 2020 Analyzed the rate of infection of the SARS Consequently, [107] outbreak in Toronto and Hong Kong the standard breeding number was 1.2 and 1.32 A model to investigate and predict the number of confirmed COVID-19 cases, by evaluating the transmission pattern and rate of recovery according to time

Predicted the number of confirmed COVID-19 cases

[129]

Mathematical The model used the approach of data model or analytics to see the effect of the spread of quarantine model COVID-19 infection

Predicted the spread of COVID-19 infection

[130]

Method of mobile Presented a method of mobile technology technology for the scanning of images of Computed Tomography (CT)

CT scanned images of COVID-19 patients

[33]

Model of differential equation

Helped in the identification of many cumulative cases of infection in China outburst by utilizing the dataset of early phase pandemic

[131]

Proposed their model of differential equation

(continued)

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Table 1 (continued) Predictive models Description

Outcomes

References

Model of time series

The model which gave the idea of methods Estimated the [132] to control the outbreak around the world count of individuals who are infected and also found the peak of the spread of infection during the pandemic

Neural-network model

Proposed an effective neural network for estimation of COVID-19 spread

Used for [22] tracking and predicting the COVID-19 disease spread with time and space

Prediction model

Data of 47 victims were utilized for the prediction of COVID-19 transmission from human to human

The transmission rate is 0.4

Prediction model

Estimated the death risk and standard breeding number of COVID-19 in 2 different studies

The death’s [102] risk was 5.1% and 8.4%, respectively and also the standard breeding number was 2.1 and 3.2, respectively

Estimated the rate of infection of COVID-19, in China (Wuhan), by utilizing the data of 565 citizens of Japan (move out from Wuhan from January 29-31, 2020)

The infection [101] rate comes out to be 9.5% and the death rate is in between 0.3%-0.6%

Proposed model of COVID-19 to analyze the effect of isolating and quarantine of infected individuals in the COVID-19 transmission cases

Found that [60] quarantine of infected individual and contact’s trace is not enough for minimizing the spread of COVID-19

Probabilistic model

[74]

(continued)

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Table 1 (continued) Predictive models Description

Outcomes

Probability-based Estimation of MERS transmission rate model

MERS [133] transmission rate estimated

References

Proposed method Proposed method predicts the rate of infections from the very first day of COVID-19 in Italy

Predicted the real number of exposed cases of coronavirus

[134]

Proposed model

Proposed a model to find the correlation Argued that [135] between temperature and COVID-19 cases the effect of in Europe temperature i.e. the seasonal variation is responsible for the active or inactive cases of COVID-19. They said that the higher temperature, the lesser will be the spread of COVID-19

SIDARTHE model

Outbreak prediction model for the comparison of the density of infected individuals and the symptoms persists

Helped in the [136] simulation of outcomes and defining the reproduction number. This model also provides the comparison outcomes from the actual dataset of COVID-19 outburst of Italy.

SIR model

By using the dataset from Johns Hopkins University website, they proposed the trending pattern of COVID-19 outbreak in China with the help of model

Predicted that [137] the outbreak remained in China

Statistical model

A model for knowing the reproductive number and how this number progress the cases of COVID-19 pandemic

Proposed model for statistical information

[138]

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Fig. 3 Role of AI to fight against COVID-19 (Figure courtesy: Blocks of figures retrieved from Google images (using “labeled for reuse”))

from this global epidemic different artificial intelligence based early warning alarms are being developed. BluDot is an AI-based model developed on Canadian case has become a legendary model of early warning alarm, which can predict out people in the epidemic comparably at a low cost. WHO declares about infection outbreak on 9th January 2020 while this BlueDot has predicted it by the completion of 2019 and provided the notice and warning to its user on December 31st 2019 [18]. Using BlueDot researchers developed a list of passengers who travelled from Wuhan to top 20 cities during the outbreak of epidemic [19]. Because of potentially efficient performance, BlueDot has become so popular even it is farfetched as the role of a human scientist. There are various AI-based models which are being developed and they are also providing efficient result like HealthMap at Boston children’s hospital of USA, warned alarm on December 30th 2019 which was 1 day earlier to 31st December 2019 on which BlueDot has warned about the epidemic. But by the view of Associated Press released that scientist at PMED (Program for monitoring emerging diseases) warned the alarm only after 30 min of it. It appeared that implication towards the epidemic of AI-based model which alarmed before 30 min is of lowlevel. To signify the warning, human observation is needed. Thus, AI can be ideally used with interoperation of human [20, 21].

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4.2 Tracking and Prediction Tracking and prediction of a deadly infectious disease like COVID-19 can help in preparing plan and managing its deadly outbreak. The pattern of the outbreak of COVID-19 disease, like how it is being spread over space and over time, can be tracked and predicted with the help of artificial intelligence. In [22] designed a neural network-based prediction model for 2015 Zika virus outbreak. This model can be also used for prediction of COVID-19 outbreak by retraining data of COVID-19. These prediction tools are being used in these days as well. Carnegie Mellon University was using the algorithm for prediction of seasonal flu is in use for today’s pandemic by retraining the data [23]. Many researchers are collecting and assembling data of COVID-19 to retrain it on different AI-based model prediction model. There are many issues which can bother prediction accuracy [23– 25]. Since the features of COVID-19 is unlike to different infectious epidemic disease many issues arise like biased data and insufficient historical data can bother the training of artificial intelligence and which can cause a buzz on social media. Along with insufficient historical data, the big data can also cause a problem, because the data of COVID-19 has been collected from different social media. According to [25] Google flu trend a popular prediction web service got failure because of big data and algorithm efficiency. Thus because of big data, insufficient historical data, noisy data and inefficient algorithm AI-based models for tracking and prediction of COVID-19 are showing imperfection as well. To make prediction accurate social media tried to modify content (for example checking fake news) using artificial intelligence [26]. Meanwhile, because of lockdown social media content was affected as human staff decreased. According to [27] data modification using artificial intelligence was also inaccurate. After that various researchers have started to work on epidemiological models rather than prediction model using artificial intelligence [28, 29].

4.3 Data Dashboards Data dashboard is being developed for analysis and making the perception of COVID19 data to make a prediction and tracking the outbreak. Ranking for such dashboards of prediction and tracking has been developed by MIT Technology Review. According to them, the dashboards developed by UpCode, NextStrain, the Johns Hopkins’ CSSE, The New York Times, The BBC, the base lab, and the HealthMap are at the top of the ranking of dashboards. Microsoft Bing’s COVID-19 Tracker is also an efficient dashboard. These dashboards have been developed for worldwide however countries have also developed their data dashboard as well. At the University of Pretoria (Social Impact Research Group), data scientists observe and adhere to the COVID-19 ZA South Africa Dashboard. With COVID-19 Starter Workbook, a COVID-19 Data Hub has been developed by American data visualization software

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Tableau, to provide data dashboard of COVID-19 pandemic. Sarkar 2020 is an efficient platform of python script using that COVID-19 data could be obtained from New York Time’s COVID-19 dataset [30].

4.4 Diagnosis and Prognosis As the COVID-19 is the deadly infectious disease, requires rapid and absolute diagnosis to save mankind, to break transmission and the accurate data can also be used further for prediction by training on AI models. Diagnosis with the help of AI can accomplish with rapid and cheaper, without radiologist as well and accurate than the usual test of COVID-19 [31]. AI can use both CT scan and X-ray images for diagnosis as discussed in [32]. CT scan images can be scanned on a mobile phone using a method given by Maghdid et al. 2020 [33]. Convolution neural networkbased COVID-Net was developed by Wang and Wong (2020), utilized for COVID-19 diagnosis using chest x-ray [34]. Around 13000 patient’s data which contains data of patients with a different lung problem and patients having COVID-19 have been used to train it. While it needs more improvement to start as a standard test. Deep learning-based model for diagnosis is more potentially efficient than an expert radiologist [35]. CAD4COVID developed by Delft’s Dutch university, is of artificial intelligence-based software for COVID-19 diagnosis using chest radiography images. This model is inspired by an earlier developed artificial intelligencebased model, through the University of Diagnosis and Tuberculosis. However, it has not been used in practice while various Chinese hospitals had used such artificial intelligence-based radiology technology [36]. To forecast the possibility of death among diseased people, data of 29 patients of Tongji Hospital of Wuhan, China, has been trained with machine learning and a prognostic forecasting algorithm has been developed [37]. Artificial intelligence-based prediction algorithm having an efficiency of 80 per cent reliability has developed to predict the risk of developing Acute Respiratory Distress Syndrome (ARDS) among COVID-19 infected [38]. While this model was limited to two hospitals of China as this model has been trained with little data. Diagnosis of COVID-19 involving artificial intelligence has motivated to apply artificial intelligence in prognosis, but still, it needs much effort to be used.

4.5 Treatments and Cures Artificial intelligence is considered as very much helpful and useful for drug discovery, since before COVID-19 pandemic [39–42]. Today in the global pandemic of COVID-19 researchers use the artificial intelligence-based method for the development of vaccine and treatment as well. Artificial intelligence could be used for recycling of existing drug and can also improve or speed up the drug discovery

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process. Structure of protein of virus is being predicted using Google’s DeepMind algorithm, an AI algorithm that has been used in game AlphaGO, helpful in the development of a drug. While the statement of invalidation i.e. no reliability of predicted structure has been mentioned by DeepMind on its website as the practical evaluation has not been made for predicted structure. Atazanavir a currently available drug has been reported as potentially efficient to be used in the treatment of COVID-19 by recycling it with the help of machine learning [43]. Baricitinib a drug used in the treatment of Myelofibrosis and Rheumatoid arthritis has found to be efficient to treat COVID-19 by benevolent artificial intelligence at UK [44]. These drugs could be in use after experimental validation, clinical trial of 18 months and scientific approval etc. [45]. A drug has found to be potentially efficient to fight against COVID-19 would have to go through different stages before it comes in use [46].

4.6 Social Control Social control has appeared as an efficient way to control the pandemic. To control a pandemic, lockdown and social distancing is implemented and public places are being scanned through thermal imaging involving artificial intelligence to eliminate out affected one [47]. In China, Railway station and Airports were scanned using an infrared camera to eliminate individual having high temperature [48]. Faces wearing a mask or not and having high temperature or not, are also being recognized. Public place used to scan through computer vision involving infrared camera is being developed by Chinese firm Baidu. These cameras having scanning efficiency of 200 individual per minute as well as can detect the individual having body temperature higher than 37.3 degrees [49]. While in some cases there may be difficulty in making surety that high body temperature is because of which reason is it COVID-19 or not, such as in case people wearing glasses thermal imaging unable to scan the most accurate data that is inner tear duct due to which the temperature predicted by thermal imaging is unreliable [50]. This camera is also used for enforcing and monitoring the order of self-quarantine. Using facial recognition, the suspected one who ignores the quarantine rule goes out of a home could be tracked by authorities. However, China is not the only country that uses this camera-based system for social control. In the Oxford city, the UK for implementing government’s rule of social distancing public places has been visualized and scanned using camera involving AI-based computer vision. Social distancing in the USA is also being monitored and notifying the warning to rule-breaker using imaging camera involving computer vision [48, 51]. The government security system of Israel has applauded the reliability of cyber-based monitoring system as for a very uttermost case they have taken observation on quarantine centre using a computer-based observing system that might be infection occurred in quarantined person could be detected. However, these AI-based computer vision and robots do not get obstruction in their process unlike AI-based prediction and diagnosis having issues of insufficient

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historical data for training. Thus, along with these purposes, AI is being used for social control as well. AI-based apps of mobile phones for collection of health data and location of that mobile phone user are considered as very certain to be applied, and can be utilized for providing the update of user-health to medical advisor proceeding the user will get back advice from medical advisor according to their health, as well as user can also get the information regarding hotspot region that must be avoided for safety purpose [52]. However, there is an issue of privacy as it might be possible after the pandemic ended, these data can be used by the government for population survey etc. Since in Hungry, it was considered that the government could ask for individual data, they could say that there is a requirement of biometric data as if COVID-19 epidemic took again then these biometric data can be used further as in case of Ebola, Ebola wave taking place again in central Africa [53].

5 Predictive Analytics/Predictive Systems for COVID-19 To set the condition of pandemic controlled, countries across the whole world jointly applying ICT [information, communication, and technology]. ICT system, application and tools can develop predictive analytics and can effectively enable the condition under control. ICT can analyze an area suspected COVID-19 to identify the stimuli, transmission and development of COVID-19. A large number of patients might have SARS-CoV2 do not show any symptom. ICT leads to predictive analysis is a crucial work as after a fixed duration of time the person has no symptoms being started to show symptoms. Artificial intelligence could play a pivotal role in fighting against a deadly outbreak of COVID-19. The transmission rate of COVID-19 can be determined using an artificial intelligence-based model as AI worked in 2015 in case of Zika virus outbreak. Rapid and reliable prediction helps in taking possible actions to reduce probable hazard before the danger condition occurs [54]. To control and observe the transmission of infection various steps has been taken across the world. Symptoms, asymptoms, pathogenesis of virus, and its evolution are the number of queries and confusions related to COVID-19 and process of its spread is obvious to arise. These questions require the development of a mathematical model incorporating predictive analytics simultaneously along with biological science so that suitable steps, as well as alert, can be taken by the government to fight against the pandemic. However, after many fast improvements in research studies, still, numerous studies were disappointed in proposing appropriate effort to control the epidemic. But still, development of a mathematical model incorporating predictive analytics is in requirement in today and alike in future to control pandemic and save mankind as well. For an improved prediction, there is data analytics, possess a special section of predictive analytics which uses historical data and analysis method involving learning and statistical method. Predictive analytics mainly wants to forecast the prospective and develop the pattern of given data. Predictive analytics have three basic supports machine learning, predictive modelling and statistical analysis. The key strengths of

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predictive analytics are predictive modelling, statistical analysis, logistic model and linear regression. The potential of prediction depends on the quality of the algorithm and predictive model as the more efficient algorithm and model leads to a better result. This predictive model can play a key role in hospital in supplying medical equipment, treatment and managing hospital like the prediction of how many patients would need ICU in coming seven days will help to manage ICU fulfilment as the effectiveness of disease is not same for all the patient. A predictive model is needed to be chosen according to queries for best and reliable result using an appropriate predictive algorithm. The basic constituents of predictive analytics are: 1. Predictive analytics model 2. Predictive analytics algorithm.

5.1 Predictive Analytics Model • Classification model an appropriate model for the problems which requires decision yes or no. This model uses historical data to classify data into different groups. In online banking, fraud transaction can be identified using this model. According to the feature of data clustering, model distributes data into different logical groups. This model could help upgrade the performance of students by classifying students into a different group according to the marks and distance between the school and their home so that students could decide to do labor for the amount of study accordingly. • One more prominent predictive model is the Forecast model, a numerical based model which uses historical data for learning and leads to prediction. This forecast model would be an appropriate model for prediction of the number of COVID-19 affected in the coming 7 days. • Outlier model is an appropriate model for predicting irregular entries from a dataset containing irregular data. This outlier model could be applicable in searching archives of strange for an insurance claim. • Time series model is the time-based model uses time as an input parameter. It needs a historical time domain from where data points are obtained and leads to short term prediction. In India, time series model can help in the prediction of transmission, for which short term data could be gathered from china pandemic.

5.2 Predictive Analytics Algorithm • Deep learning or machine learning could be used as predictive analytics algorithm for prediction and identification. Machine learning-based predictive analytics algorithm is applied for grouping and classifying the data leading to decision and prediction. In the case of structured data that would be linear or non-linear, these algorithms could be appropriate for use. While machine learning algorithm

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possessing inefficient for large size data such as images, audio, video and hence the deep learning algorithm is more applicable for these. Predictive analytics can widely be used for prediction of disease, possess learning techniques as key functioning [55, 56]. • Classification and regression of data are made by decision tree-based Random forest algorithm, which prevents inaccuracy by applying bagging, a suitable algorithm for handling big data. This algorithm is an efficient algorithm to deal with overfitting. Classification of data can be addressed by a gradient boosted model possessing an ensemble model of decision trees. Incremental model is obsessed by Gradient model, during the training of data at each time it constructs a tree and the inaccuracy occurred during previous training a tree is improved. • While in the case of Random forest the trees having no relation. K-means algorithm is an algorithm of clustering data, according to the common characteristics in the data. In the pandemic of COVID-19, K-means algorithm could be very helpful. As the count of infected patients is increasing day by day, the K-means algorithm can form a different group of patients according to the effectiveness of infection and a new case of infected would be put accordingly. The pandemic of COVID-19 requires forecasting of transmission and death rate so that community, public health service and government can step out precaution. • For the next three months, the number of cases can be predicted by the training model using FBProphet algorithm. Time series data using for the prediction can be addressed very efficiently by FBProphet algorithm which has the crucial characteristic of handling time series data. Kaggle provides time-series dataset for prediction of COVID-19 in which longitude, latitude, country, state, confirmed infected recovered patients, and deaths are termed as attributes. Except for these attributes, the Prophet has a specific property of taking complete dataset for fitting, without leaving any data to provide reliable result [54].

6 Data Models for COVID-19 Prediction Infectious diseases can be studied effectively using mathematical modelling. These models are found to be a valuable tool in understanding the dynamics of diseases like vector-borne diseases, HIV, influenza including COVID-19 [57]. The mathematical models can be used to study epidemiology as they are based on population biology studies [58]. Uniqueness, local and global stability, positivity, bifurcation analysis, disease control and presence of solutions are some of the characteristic features of the models [57]. A model mimics reality based on the observed and experienced data [58]. Smallpox was the first epidemic which was studied using mathematical modelling by Daniel Bernoulli in 1766 [59]. After many criticisms and revisions, a classical paper came in 1911 by Ross who systematically developed the ‘modern

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mathematical epidemiology’ [59]. The two key parameters which govern the transmission of epidemics are contact-tracing and isolation methods (include both propagations of disease from an infected person to healthier one and asymptomatically persons) [60]. Some of the general terms used in models are as follows: • The changing variable which defines the system’s state is known as the state variable. For example, in a pandemic like the COVID-19, the susceptibles and infectives are treated as state variables for that particular system [58]. • As per the requirement of the study, the user defines the parameter which generally controls the nature or value of the state variables like the minimum number of days an individual remains infectious [58]. • The greater number of variables and parameters used, the model becomes more accurate and fit. This shows the intricacies related to the model. Therefore, complex models are preferred over simple models. Also, the relationship among different parameters should be known exactly [58]. Modelling and simulation both are used in the analysis of epidemics where the data collection is easy and cost-ineffective and testing conditions are more in number. There are 3 types of mathematical modelling namely (a) state-space models (mathematical models which rely upon the system dynamics) which is used to evaluate the epidemic (may be hypothetical also), (b) statistical models which monitor the outbreak and identification of components at the time of epidemic and (c) machine learning ways which are highly efficient in determining the evolution of the epidemic [59]. Some general models used to study any epidemic along with models used for COVID-19 are described below. And other publicly available COVID-19 predictive tools developed by various groups are shown in Table 2. And also Table 3 shows the comparative account of different models of COVID-19.

6.1 Susceptible-Infectious-Reduced (SIR): A Simple Model SIR model was initially developed by Guldberg and Waage in 1864, as shown in Fig. 4 [59]. This model is based on the classification of the population according to their statuses like susceptible to the disease (S), infectious individuals (I) and persons recovered from the infection (disease) (R). Then the total number (N) of individuals will be the summation of all the classified individuals (N = R + I + S) [58]. The process of transmission is very important and crucial in understanding the logistics of any epidemic. The force of infection (λ) in terms of epidemiology is defined as “the per capita rate of acquisition of the infection” [58]. The probability that a susceptible person will be infected in a given particular time interval (t) is given by the expression, λ(t)t [58]. β is defined as the mean of the number of contacts made by the person per unit time is considered as constant after considering factors that influence the rate of transmission like status of epidemic, environmental and social factors. The number of contacts with infectives is given by the expression

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Table 2 Other publicly available COVID-19 predictive tools developed by various groups Model for prediction

Developed by

Links

Mechanistic transmission models

Imperial College London

https://www.imperial.ac.uk/ [139] mrc-global-infectious-dis ease-analysis/covid-19/

Statistical models

University of Geneva

https://renkulab.shinyapps. [139] io/COVID-19-Epidemic-For ecasting/

Modified SEIR model

MIT

https://www.covidanalytics. [139] io/projections

Statistical dynamical growth model

Los Alamos National Laboratories

https://covid-19.bsvgat eway.org/

[139]

Statistical model

The University of Washington

https://covid19.healthdata. org/projections

[139]

Statistical model

The University of Texas

https://covid-19.tacc.utexas. [139] edu/projections/

Spatial epidemic model

Northeastern University

https://covid19.gleamproj ect.org/

[139]

Modified SEIR model

University of California

https://covid19.uclaml.org/

[139]

https://ddi.sutd.edu.sg/ when-will-covid-19-end/

[76]

Data-driven prediction Singapore University of Technology and Design

References

βI N

. s × βNI , this expression gives the number of new cases per unit time with an assumption that the number of infected individuals is adequate to spread the infection. Thus, it implies that, Force of infection (λ) =

βI N

6.2 Mathematical Model: Stochastic Transmission Dynamic Model Stochastic Transmission Dynamic Model was developed in the early stage of COVID19 to study and analyse its transmission dynamics. The modelling methods allows to deal with the delays occurred due to uncertainty of the infection and number of cases registered [61]. This model studied the transmission pattern of the SARS— CoV—2 by considering 4 sets of data from and outside Wuhan from December 2019 to February 2020 [61]. The dynamic mathematical model was also used by Huaiyu Tian and Christopher dye where they found that by the action of national emergency of China, the number of incidences of disease decreased due to the implementation of intervention measures [62]. The population was divided into 4 categories based on

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Table 3 Comparative account of different models of COVID-19 Models

Reproduction number

Results

Stochastic Transmission Dynamic Model

Initial Reproduction number was 2.35 and reduced to 1.05 after imposing restrictions

Due to control measures, [61] the incidences were reduced in Wuhan

References

Branching process model

1. R0 1.5 and 0% transmission (no symptoms) 2. R0 2.5–3.5 and with the transmission (no symptoms)

Contact tracing and isolation can prevent the transmission of the disease. The model can be updated to give more precise results

[60]

Mathematical model

–

Social distancing and lockdown are the best ways to control the disease

[63]

IHME model

–

Needs to be validated using [64] other available models and they observed the deaths lie outside the 95% of predictive intervals.

SEIR model

Used from various references

There are certain limitations [68] with R0 and period of infection and restrictions reduced the infection in Wuhan

SIRD model

The average R0 = 2.6

Isolation and treatment led to the reduction in the rate

[69]

Conceptual model

R0 = 2.8, estimated one

The restriction imposed improved the condition

[70]

Time-Dependent Dynamic model

Defined R0 as time-dependent

Transmission is caused due to contacts

[73]

Fig. 4 It represents the simple SIR model; where λS is the rate at which susceptible individuals get converted into infected and γ is the rate of recovery. The arrows show the interconversion from one phase to another phase [58]

infection pattern namely susceptible, exposed (non-infected), infectious and removed (isolated or recovered) [61].

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Fig. 5 It represents a simple representation where an individual (say A) (infected) can potentially able to infect other individuals (say B, C and D) in chain-like manner. ‘0’ represent contacted while ‘x’ means not yet transmitted due to individual A was isolated

6.3 Branching Process Model Joel Hellewell et al. studied the spread and control of the COVID-19 using the parameters – contact tracing and isolation using a mathematical model, as shown in Fig. 5 [60]. In this model, they selected secondarily infected individuals from the negative binomial distribution (a probability method where independent and similar Bernoulli trails in which the failures are counted) where the mean and reproduction number is equal and it was observed that there are differences among the individuals. Now all the potential infection-causing individuals are assigned a time interval as it was taken (drawn) from the serial distribution [60]. For instance, one infected person produces 3 secondary infections (due to retrieved from negative binomial distribution). But due to the isolation, the individual might have or may cause 2 transmissions only. Hence, this model may lead to reduce the number of transmission cases due to isolation [60].

6.4 Proposed Mathematical Model in Indian Scenario COVID-19 is a communicable disease which spreads from one person to another due to contact. This model assumes that an infected individual propagates the infection to‘t’ number of individuals. Now, as lockdown, testing and quarantine came into play, the number of diseased individuals reduced to ‘q’. But still due to not following the procedures some people say d = t −q will behave as active nodes in the transmission of the infection [63]. Again, the cycle continues, the infected active node can transmit this disease to ‘t’ persons in unit time while the number of individuals ‘q’ who skipped the quarantine and act like hidden active nodes will again transmit this infection to others and it will increase in a constant number say ‘C’. therefore, the total cases or hidden nodes become d + C which soon will turn into d + 2C so on and so forth [63]. This can be shown using a simple equation as follows:

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Tcases = 1 + t + (dt + (d + C)t + (d + 2C)t + (d + 3C) + . . . + (d + nC)t where ‘1’ represents the first individual get affected and ‘t’ number of individuals affected by coming in contact with ‘1’. In the above case, the terms except 1 and t rest form an Arithmetic Progression (AP) having the first term as dt, common difference as Ct (Ct is also known as the rate of infection or r) and total (N−2) terms [63]. Tcases = 1 + t +

(N − 2)[2dt − (N − 3)Ct] 2

6.5 Institute for Health Metrics and Evaluation (IHME) Model This model is very modern which provides information on the requirement of beds and ventilators to the hospitals for the COVID patients. This model used collected data of many deaths of individuals throughout certain intervals. The model evaluated using a statistical measure; posterior interval (PI) for the deaths causing due to COVID-19 [64]. The accuracy of the data was confirmed using the logistic measure; the absolute 1 [64]. value of the percentage error (APE) or LAPE by using the formula 1+exp(−|x|)

6.6 Simple Iterative Method The model uses a number of confirmed reports of the infection to evaluate the accuracy of the prediction. The number of days is denoted by ‘x’ (xi is the days’ index), xi ∈ [0, n); where n denotes the total values. Let ‘m’ be the last value in the series of ‘n’ values [65]. Then the average growth rate is determined by the equation: G =

 n−1  xi 1  −1 m i=n−m xi − 1

The iteration can be represented as: xi+1 = xi (1 + G  ) By the above expression, the minimum and maximum growth rate during ‘m’ days can be obtained [65]. The obtained Eq. (1) can be rewritten in terms of days required for recovering (h ≈ 14days) and deaths (d ≈ 21days) where the death is 0.04 ( p ≈ 0.04).

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∗ xi+1 = xi+1 − pxi−d − (1 − p)xi−h ∗ xi+1 denotes non-iterative number and it implies either that the rate of recovering is more due to the development of immunity against the COVID or infected individuals are approaching population size [65].

6.7 Simple Time-Series Approach of Forecasting In this model, the parameters considered by the group (Fotios Petropoulos et al.) include a number of confirmed cases, recovered individuals and deaths occurred along with this they considered both laboratory and clinically diagnosed confirmed cases for the development of this model [66]. They also incorporated the exponential smoothing methods in their model. Exponential smoothing is a simple and robust method which is used to forecast the time series (also considered trended and non-seasonal exponential smoothing) [66, 67]. In this model, they assumed that the epidemic lasts longer and go indefinitely as compared to other methods. Here, multiplicative trend components and error were incorporated [66].

6.8 SEIR Model The COVID-19 has caused mass transmission wherein many are getting infected due to coming in contact with other persons (maybe infected or carrier). Due to the avoidance of social gathering and crowding various steps have taken to decrease the number of incidences of this pandemic disease. The group led by Kiesha Prem et al., studied the pattern of progression of this infection using the model called agestructured susceptible-exposed-infected-removed (SEIR). In this, they considered the parameters like social distancing, etc. [68]. They have divided the population as infected(I), susceptible (S), exposed (E), and removed (R) but the status of individuals which include ageing, deaths and births are considered in the analysis process [68]. Along with this, the population is also divided using age parameter like category 1 - > 5 years, category 2–5—10 years and thereon while the last category 16 consists of individuals with 75 years and more [68].

6.9 SIRD Model Susceptible-Infectious-Recovered-Dead or referred to as SIRD was developed by Cleo Anastassopoulou et al. They estimated the parameters namely the basic reproduction number R0 , recovery from the infection and fatality cases [69]. Here, the

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basic reproduction number is important and it helps in the prediction of whether the infection remains in the population or not [69].

6.10 Conceptual Model This model is also based upon the SEIR Model. The group led by Qianying Lin et al. adopted this model along with other parameters like ‘D’ it represents the risk of the public by considering severe, critical and death cases while the other component is ‘C’ represents the total cases. They used the formulae used by He et al. 2013 and modified accordingly [70, 71]. They considered that this infectious disease shows transmission through animals as well and denoted ‘F’ [70]. The formulae included in the model are as follows [70]: β0 S F β(t)S I − − μS N N β(t)S I β0 S F  + − (σ + μ)E E = N N  I = σ E − (γ + μ)I 

S =−



R = γ I − μR 

N = −μN 

D = dγ I − λD C  = σ E β(t)S I β0 S F + − (σ + μ)E N N   Also, β(t) = β0 (1 − α) 1 − ND and S—Susceptible, E—Exposed, I—Infectious and R—Removed β(t) is known as the rate of transmission [70]. 

E =

6.11 Time-Dependent Dynamic Model It is the latest model, an updated form of the SEIR model developed by Tang et al. and it is referred to as Time-dependent dynamic model (TDDM). In this model, the rate of contact is considered as the variable which is time-dependent [72, 73]. c(t) = (c0 − ce )e−r1 t + cb

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c0 is defined as the initial rate of contact while cb is that the minimum rate of contact which is computed after considering control measures and r1 represents the exponential reduction in the contact rate [2]. δi (t) represents the transition rate of the symptomatic individual to quarantine individual can be depicted using the following expression: 1 = δi (t)



 1 1 1 − er 2 t + δi (0) δi ( f ) δi ( f )

The reproduction number can be expressed in the following way [74, 75]:  Rc =

 βc(t)ρ(1 − q) βc(t)θ (1 − p)(1 − q) S0 + δi (t) + α + γi γα

Advantages There are many merits associated with the methods of prediction models which include: (1) it is used to model a dynamic process as it can incorporate minute fluctuations occurred in the population over time. (2) it uses simple information which is available easily. (3) the codes used in this model can be accessed easily from the available public tools [76]. The models help to predict the (1) future growth of the infection (COVID-19), (2) determine the countries at the risk and (3) provide ways for further improvisations and investigations [61]. Mathematical and computational models are very useful in predicting the nature of the pandemics like COVID-19 [62]. The models can provide awareness about the situation when where the data is a dearth and not enough to provide the infection. They provide the scope of revision of data, as in the case of COVID-19, new data is being added daily [62]. Similarly, precise investigations can be done using these statistical and mathematical models where the laboratory-based experiments fail to answer some of the questions like the rate of transmission and intervention-related issues [62]. Limitations There could be many demerits of this model like (1) due to delayed appearance of symptoms either due to late detection of infection or remained in isolation or incubation and (2) lack of efficient testing kits and methodologies. These issues have circumscribed the boundaries of the model [61].

7 Diagnostic and Prognostic Prediction Models for COVID-19 Infection Most of the models for the prediction of COVID-19 are based on the diagnostics and prognostic of disease, including the validation of the scoring system of the multilevel model and according to the level of personal participant data. These models also

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include information on the risk of spreading the infection in the population. Prediction horizon included predictors or outcomes such as C-reactive proteins, lymphocyte count, lactic dehydrogenase, age, and sex, which usually scanned with the help of computed tomography (CT), scans [77]. Prognostic models discussed in six studies have provided sufficient information about the death of 8–59% between the age ranges of 24-65 years [78]. These variations in death and age resulted because of severe samples bias made when we remove participants having disease till the end of studies, means they have not recovered nor died. Also, there is a temporal difference in diagnosis of the patients which also varied in the follow-up length and the admitted hospitals [37, 79, 80]. A previously diagnostic model which were discussed in 18 studies, a single study reported on the occurrence of the infection of coronavirus among people with suspected COVID-19 and it was ranging from 19% to 24% in which termed as development and validations datasets respectively. Among all, an only single study reported the extensive disease among the pediatric victims which are confirmed with the coronavirus infection. Other 16 studies were utilized as either case-controlling or an unclear method for collection of the data. In all identified models three are predicting the individuals admitted to hospitals with a risk of COVID-19 infection in the general public, having upper respiratory tract infections, influenza, nontuberculosis pneumonia, acute bronchitis, and also the patients without COVID-19 infection. The dataset of comorbidity, age, treatment in the previous hospital, sex, and social factors of health was included among the studies. One study developed a model to detect COVID-19 pneumonia in fever clinic patients (C index = 0.94), one model to treat COVID-19 with the suspect (C index = 0.97), one model to diagnose but with asymptomatic characters (C in = 0.870) and one study to diagnose COVID-19 by applying the algorithms of the deep learning (DL) on the sequence of the virus (C in = 0.98) [81, 82]. One study has included the assessing calibration was quite confusing how it has performed, prediction used in almost all models were the temperature of the body (fever, n = 2), age (n = 3), and the general symptoms. 13 prediction models were proposed in supporting the monitoring and diagnosis of COVID-19 pneumonia based on CT images. C index of predictive performance varied widely with the value ranging from 0.81 to nearly 1. In all 10 identified models, six studies have discussed the risk of estimated mortality in patients with the suspect of confirmed infection, two models predicted to stay in the hospital for over 10 days (C indices = 0.92, 0.96), two models aimed to predict severe progression to the critical state of the patients (C indices = 0.95 and 0.85.9). Two studies have discussed widely the report of mortality with the estimated C index of 0.90 and 0.98 [83, 84]. The models were assessed with PROBAST, it is estimated that each model is at the higher risk of biasing and it suggests their predictive performance is lowest than reported. Therefore, it has prediction concerns and unreliability. Out of 27 studies, 11 had a higher bias’s risk for the participant’s domain which indicated that the targeted population, not a proper representation of in the model, four studies had higher bias’s risk which indicated that predictor might have not present at the intended time model used. Moreover, CT images can be transformed into predictors using the complex Machine learning algorithms and the untransparent

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way which makes it to completely apply the PROBAST predictor’s part for such imaging techniques. Most studies include the outcomes which are easy to access e.g. incidence of COVID-19 by laboratory confirmations, death of the patients. Many studies also had insignificant sizes of the sample which is leading to enhance the risk of overfitting when complex modelling strategies were used [85]. One model was arrayed in 16 hospitals to check the mortality prognosis, a monogram, a tree for the decision, and the scoring based on the CT rule were used. Moreover, monogram used to estimate the prognosis of COVID-19 also the source code is available on GitHub. In 10 studies which have taken the data from Medicare claim data for predicting the hospitalization of the patients, patients with other disease were not taken into the studies. But control was having the viral pneumonia and it cannot be taken as the sample data which represent the complete populations. An analysis which has been carried out was for the determination of the varied outcome between the participants. Dichotomized predictors were used which lead to misleading the analysis. Usually, studies do not clear how regions of interest were taken and how they have screened the test through which patients went, which patient was taken for the CT scan and whether the selection of the control was from the sample population or form outside. Sometimes images are annotated by single scoring functionality without quality checks, the annotation was influenced by the model output, or the “ground truth” which used for building the model was an outcome of compositions based on the CT images which help in prediction between other factors. Models were lacking behind the specifications, subsequent estimation, transparency and challenge and reproducibility [86]. Studies are using the different architecture of deep learning; few were recognized and other designed with the specificity of no benchmarking used architecture against others. Only a single study was accounted for including the Cox regression in their analysis. One study contains the hypothesized model which uses the cross-sectional data to predict future severity which infers that the predictors timing was not appropriate, and the predictors’ value might have influenced by the outcome. Other studies highly contain the subjective predictors or the measurements which were previously utilized measurements of predictors from the databases.

8 Challenges Although the predictive systems and data models of Artificial Intelligence (AI) related to the COVID-19 pandemic are helpful in diagnosis and prognosis of infected individuals of COVID-19. Still there remain many challenges for the investigators, researchers, policymakers and clinicians for detecting risk in people of the general population who are admitted to hospitals for COVID-19 pneumonia, and the diagnosis and prognosis of victims with COVID-19. So the questions arise “Could transmission of COVID-19 be forecasted and tracked with the help of artificial intelligence?”, “Can AI contribute its help in diagnosis and prognosis for COVID-19?”, “Could artificial intelligence be applied in drug or vaccine discovery and treatment?”, “Could AI contribute its help in social control?”

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In most of the studies, these data models reported excellent predictive performance but it was estimated that the predictive systems and data models have a big possibility of partiality since irrelevant methods used for selection of participant from the population as patients are mixed with and without any disease, lack of actual description outcomes of any predictors (such age, sex, the temperature of the body, respiratory signs and symptoms, C reactive protein, direct bilirubin, lymphocyte count, lactic dehydrogenase, albumin, distribution of red blood cells and features derived from CT scoring) of the population under study, and utilization of a poor combination of methodology and various mathematical and statistical models. It has also been notified from most of the researches that in the prediction model studies related to COVID-19, patient’s clinical data and relevant information is still limited for the actual analysis of patients infected with COVID-19. Due to the limited amount of data sample and scarcity in reported events of interest, prediction models when build and validated enhance the risk of over-fitting the model [87], and leads to poor performance in practical applications. One of the challenges is when the predicted outcomes show the variations of relative frequencies in any model which leads to the production of un-calibrated predictions 45. Most of the predictive models were limited use for the COVID-19 pandemic, as they eventually cause harm to individuals [88, 89]. Instead of the biased data of the individual, there might be incomplete data i.e. it can take only a few aspects of the dynamics of COVID-19 pandemic. Currently, several other features such as spreading and transmission of COVID-19 are unspecified, for instance, the transmission pattern can occur before the onset of any symptom and the analysis by predictive systems might give the worst result of the outbreak [90–93]. In various predictive models, there is the limitation of reproduction number (R0) estimation, as it creates a large number of uncertainties in the model and also the period of infectiousness of the disease [94]. It is also considered as the difficult parameter for the estimation of disease outbreak, as during the transmission of the infection the actual numbers of cases at a particular period are not known and it will probably change with time as because of changing the behavior of the population in response to outbreak [61]. Some mathematical models do not provide details of heterogeneity level in individuals, which might be useful for the detection of spreading of disease especially in the early stages of the epidemic. The parameters used in ANFIS (Adaptive Neuro-Fuzzy Inference System) might be challenging for the researchers that required improvement for the better performance of the prediction model [95]. Many studies also show the challenge of COVID-19 in the process of sustainable development, which plays a major role in human society’s development [96–98]. There is one of the major challenges in predictive systems and data models, i.e. the lagging time in the analysis because the training data is not available for the calibration of analyzing novel disease if the available data is from internet sources as well as from social media network. In one of the studies, SEIR models also face challenges for COVID-19. For instance, the actual number of reported cases seen on the worldometer is not actual contaminated individuals i.e. E of the SEIR model. And also the count of infected individuals i.e. ‘I’ from the SEIR model is

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challenging because most of the infected individuals not go for testing if the signs and symptoms were shown to be less. And in many cases, if there is good data available for individuals those who were died and those who were in the hospitals. There is a huge amount of data available throughout the world related to COVID-19, but the challenge is how to handle issues such as what are the variety, velocity and volume of data, and how to handle the complex data. Due to the complex behavior of the population in different geographical areas, several variables involved in the transmission of disease, and variation in the methods of containment leads to enhancement of uncertainty of any model [99].

9 Future Trends of the Predictive Systems and Data Models Prediction models are designed to be kept in mind to help and support medical staffs and ultimately the benefits the public after the governmental approval. With the consideration of importance for the identification of a sample (target) population in which predictions should serve as the clinical benefits which should again be a representing dataset to present and make the governmental body to identify the loopholes and benefits for further approval. These datasets are generated from the patients and the model-based prediction which further compared to get the deviation statistics for further stepping ahead. The targeted sampled population should also be carefully identified to get the better performance of the developed model and further validate on other samples as well with the consent of the public by keeping the human rights in mind. The setting of the applied model for the prediction with the relative frequencies of extensive range to get the different outcomes and then to calibrate them before it can safely be applied to next targeted samples with updated settings. These calibrations and settings must have to be extensively analyzed before applying the same model in different disease and healthcare systems with a well—described population. It helps us to understand and observe the differences, report them and after all in making strategies, a better shining example could also be taken as the COVID-19 mortality cases [100]. For prediction modeller relative frequencies with variability is the bigger challenge. Coronavirus prediction problems are not just a simple binary classification task, it is all just because a wide number of statistics coming from different countries and been updated with different stats of mortality and widespread. It must be carefully handled before applying on other pandemics. A prediction vista must be specified for prognostic outcomes, e.g. mortality in 30-days. Most studies focusing on the model-based prediction are excluding from the sample if these participants are in the same condition with no update in death or recovery, it should not be removed from the sample population. Expurgating for other reasons, to necessitate analysis in the complete risk of the framework, it is important to analyze the follow up of the patients who are not on a high risk of death from coronavirus disease. In its place of just manipulating the local setting with a wide no of a sample in the same area, data should be taken from different countries to get a wide variety and also a

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relative frequency to validate the model and it provides generalizability and implementation of the model obliquely different sample with epigenetic differences. These data-driven approaches could improve the applicability and robustness of the model in prediction generally in routine health care [101]. There are various models come in the research area which is implicated on COVID-19 crisis and these models will be applied when this pandemic is over, which seems not to end sooner. Data registry has been started because of various testing and to make these data to be utilized in public for use in the research related to COVID-19. To influence the associative incorporation, international and potential evolution to gain data and model construction is significant. However, the chapter described the studies in very systematic view to understand and apply the same on other disease or any future outbreaks of an epidemic to control on the local level. There are huge benefits and suggestion of the predictive models on the society, alongside it also has a huge calibration issue due to very less time on this pandemic and the statically varied data in the death rate. With new publications on SARS coronavirus-2 dealing with the prediction models which are rapidly entering the literature related to the biomedical health, this chapter includes only till yet published data but in few months there will be updated models with new approaches will come in research that can be updated after peer-review only. There are also many preprints available on the various platforms which cannot be considered as a reliable source to write and predict something about that because these studies might be updated while reviewers will review it. In a recent publication, a wide no of publication has been listed with their updated details. They also have reported prediction models which are presently being utilized but without scientific publication and calculation of its web risk it is not recommendable, and the manuscript is still under review [72, 74]. SARS CoV-2 sets a global alarm, a Harvard Professor stated that 40–70% of the population around the world might be infected in the coming year and damage a lot before the vaccine or medicine will discover and will commercially available for public use, the same statement of Chancellor Angela Merkel’s cautionary statement regarding the effects of the SARS CoV-2 in Germany. The perseverance to diagnose and prognosis models for assisting in very less time with the triage efficiency of the patients during the ongoing pandemic, it might also give inspiration to the scientists to contrivance the model prediction with the peer reviews [75, 102]. Studies carried out initially shown that model was used during the pandemic and sometimes models cause more harms than something good. Since it has not been peer—reviewed, it is not recommendable to any model to use. It is expected that more data related to this pandemic will be available sooner publicly. These data can also be utilized for validating the models described in previous and recent publications e.g. a predictive model that forecasted progression to severe SARS CoV2 disease within two weeks to hospitalize showed capable bias at the time of validating it externally on two tiny but non-selection allies. Reported details in this study were not enough and the validation not performed on a large dataset, it was just validated on the Chinese dataset but for extensive reporting, the international dataset should have to take as per the suggestion of experts [103].

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There is a huge gap between Asian and European healthcare systems when patients are to be hospitalized and terminated their curation period after treatment from the hospital. Testing criteria for the patients with COVID-19, here also is a huge gap and anticipates a better model needed with the updated settings. The expert suggestion is required when building any model which are de novo to know the approaches and data utilized, we are selecting with the specificity of settings are important. These setting could also mislead the project so the analysis part is mandatory which might be checked by the computational statisticians. Data size is important assets while validating or even testing any model for Denovo because it gives an effective effect on views on the population. We encourage readers of this chapter to consider incorporating numerous candidate models e.g. models for the diagnosis; diagnosis models consist of the body temperature of the patients, signs related to the respirations, age, and the general symptoms and for the models of prognosis; sex, age, lactic dehydrogenase, lymphocyte count, C-reactive protein and efficient characteristics obtained by CT stats [104]. Both predictors were included the direct bilirubin, albumin, and RBCs radius. After analysis of the challenged faced in the methodological parts mostly available models gives a sight preview to develop new models with the proper extensive from the scratch. Bell-shaped curve provides straightforward detection of the inflexion point on the highest point of the graph which helps to distinguish different countries separately. Such monitoring should analyze together with the real-world data with the update could help to suggest governments to change its policies and further to support the public and health workers [105]. Countries structuring and lowering the strictness with the cases in their respective countries. COVID-19 is like fire but having a difference with it in monitoring systems. Usually, fires have a pattern, but pandemics have no pattern to spread and kill people. Due to increased threat with bioterrorism, governments concern to public health is very critical and most governments started working to ensure the public health. The financial threat is a major issue in this area, to make people aware of to have the stability and maintain their immune responses. While making the health policies government should always sanction extra fund and use in the development of vaccine or medicines. To further ensure the health of the public govt should have an extra committee for suggestion and to promote the researcher. Public health cannot be ensured without having good infrastructure and the facilities and their technicians, making it affordable to the public is the biggest task. Union government should also act on it for ensuring the availability of all equipment with proper research and modification. The committee should ensure that it should not tolerate religion or race-based discrimination and aimed to eliminate ethical issues should not exist in health care. Below are a few points which give a basic suggestion to consider [106]. • Union and state government’s policymakers should be aware of all fields and must have enough knowledge of public beliefs to ensure their health and should explicitly tackle the cons of the systematic and jump out of this issue after taking it into account.

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• Union government should ensure the public data which should not be stolen or not used for business purposes, although it can contribute to the research purpose with the consent of the patients without there name of identity. • The committee should take action to develop the health record in a manner to manage more human populations with a cheaper system which will improve the public participants in the healthcare research. • Union and government of each state should provide enough insurance to the public by which they can manage their health expenses, and a committee should also monitor it to not let any corruption take place. The insurance coverage should also increase within a decade to make it affordable. COVID-19, a contact-transmissible infectious disease, is thought to spread through a population via direct contact between individuals. Outbreak control should aim at mixing the populations to delay the risk of peak and minimize the immensity of the pandemic which also provides enough times for medical staffs to get prepared. Travelling restriction and physical distancing by complete lockdown thought the nations (or specific areas of infection) have aided in lowering of COVID-19 spreading throughout ongoing pandemics. To assess the result of location-based physical distancing rule like intervention in the workplace, parks closer to schools and universities on the timing of the peak of this pandemic. The government should ensure the public health and maintain the records for another disease (or epidemic to pandemic) like it is doing for the COVID-19 cases and should not under-report the cases or outbreaks [107]. There should also be an intergovernmental treaty to share all data about the diseases and exchange of medical staffs. Apart from the significant public health concerns, the dangers imposed on global supply chains and the economy are also considerable. People having week immunity (children and senior citizens) should prepare for the worst-case scenario. We must have to act conservatively after discarding any formal statistical forecasting until the process is not formalized after deep analysis. There are huge benefits of having physical distancing and also cannot be ignored the behavior changes of the public. Heterogenous data where the physical distancing followed properly and having governmental restriction can be utilized as a model sample data for primary prediction of the effect. It is problematic to quantify whether physical distancing alone could be responsible for the dropping in cases, especially during the ongoing pandemic. Therefore, scientists are taking several simple mathematical models (i.e., linear, logistic, logarithmic, compound, quadratic, cubic, power and exponential) were fitted using GA, PSO, and GWO. The logistic model outperformed other methods and showed promising results in training for 30 days. Extrapolation of the prediction beyond the original observation range of 30-days should not be expected to be realistic considering the new statistics [108]. The prediction system from each country should be read, analyzed, and interpreted together for cumulative improvements in the view of what is happening around the globe to make changes in the government’s policy. For example, Govt of India has announced complete lockdown in March-end but till that time many people within the nation were infected, govt should seal the airport and quarantined the people have travel history from abroad especially from China.

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Although initially, this model was successful in controlling the disease spread till that time it was up to nearby to community spread and when govt started giving relaxation, it was worst to spread at. For Singapore’s govt, they have started restriction in April-May have bent the curve earlier. Theoretically, it was predicted that New Zealand, Australia, China, a few other countries are infection-free. Therefore, New Zealand, South Korea, China and Australian governments may not want to open their international airports so soon and lift the domestic restrictions so quickly, until the pandemic nears its end in the world as a whole. Although, it is our responsibility to keep the distance from each other and shared the suitable information and data for the coordination during pandemics. For those countries which are in their early phase of this pandemic life cycles (Brazil, Bangladesh) based on our predictive monitoring, forecasting remaining curve, epidemic region estimation and period (i.e. how long) of a pandemic will be quite teasing, but additionally, for future safety it inherent very fewer data to the “real future” for the given data. The main data covers the early phase and remains in a smaller size with proportion to the total life cycle and the real-world scenarios, changes cannot be expected from the described models. Countries where the peak point of the infection already reached and are heading towards the ending of the nationwide infection, the prediction is quite less useful. When the cases of COVID-19 will be lower, that will be more likely to derive and approach a higher prediction to form the data. However, in these cases, the trained model is less about predicting the future and more about explaining the history. Countries where the chances of the second phase of infection are expected governments should carefully remove the restriction on the lockdown, especially when the pandemic is still prevalent in many countries [94, 100].

10 Conclusion With the rise of the global health problems of SARS-CoV-2 epidemics, health security concern becomes the foremost issue around the world. Progress in the accuracy of prediction models during a pandemic provides a way to investigate the disease spread and the consequences of the disease during the COVID-19 outbreak. The government took steps of lockdown and quarantine around the world, which is effective in most of the countries while in some others it is not a good approach and also there are many environmental factors such as temperature shows effective outcomes. Currently, the predictive systems and data models for COVID-19 are in the trends in an academic publication which helps in supporting the decision making in medical sciences. This review gives the idea of various prediction models for the prediction, diagnosis and prognosis of COVID-19 and it also gives the brief overview of available kinds of literature and published work of predictive systems related to COVID-19. Sometimes these models are not so accurate because the data which trained the model have an excess amount of uncertainty, the over-fitting of the model, and also sometimes the data is less for the prediction and analysis of any disease. Many studies also suggest that the presented models have shown huge biasness and hence they are not correctly

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described due to their poor performance. Therefore, quick response to accurate data of individuals related to COVID-19 is required to develop more sophisticated prediction models, also the validation of the previous models. Instead of many limitations in prediction models, various mathematical and statistical models along with the field of computational intelligence (CI) or the artificial intelligence (AI) have proved the potential tool in the prediction and diagnosis of COVD-19 pandemic. It is advised for the future research that integration of the prediction models increase the previously known epidemiological models, to be utilized accurately with a longer period in future outbreaks, and the government should also take the further steps to carry on surveillance systems for the citizens even after the pandemic. Lastly, to acknowledge the ongoing situation of COVID-19 outbreak, we require the flexibility and robustness of individuals, different organizations, and a governmental body, and also the data sharing and its coordination, to manage the unforeseeable and undesirable future outbreaks.

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A Comparative Study of the SIR Prediction Models and Disease Control Strategies: A Case Study of the State of Kerala, India K. Reji Kumar

Abstract The Novel Coronavirus (nCoV or COVID-19) that hit the City of Wuhan in the Hubei Province of China in December last year has become the greatest concern throughout the world. The countries in the world have shown a significant difference in the control of the spread of disease and the mortality rate. Kerala—a southern state in India—has shown notable performance in the field of disease control of COVID19. Various measures of disease control are proved effective in the containment of COVID-19. A study of the situation in Kerala after the outbreak of COVOD-19 is used to analyze the effect of the control strategies. In this chapter, the main focus is on a comparative study of the predictions of the SIR model and the actual performance made by the state in controlling the disease. Keywords Mathematical epidemiology · SIR models · Social network analysis · Mathematical modeling · Disease control strategies

1 Introduction COVID-19 (SARS-CoV-2 or 2019-nCoV is its old name) has become a global pandemic infecting more than 85 lakh people and claiming more than 45 thousand lives world over till the writing of this Chaper [1]. The outbreak of the disease was first reported in Wuhan, China on 31 December 2019 [2, 3]. The first case of COVID19 outside China was reported, by the Ministry of Public Health (MoPH), Thailand on 13 January 2020 [4]. On 15 January 2020, the Ministry of Health, Labour and Welfare, Japan (MHLW) reported that a person aged 30–39 who had returned from Wuhan was tested and conformed infected by the 2019-nCoV virus [5]. A 35-yearold Chinese national who has traveled from Wuhan to Korea was reported positive on 20 January 2020 by the National IHR Focal Point (NFP) for the Republic of Korea [6]. The case is a 35-year-old female, Chinese national, residing in Wuhan, K. Reji Kumar (B) Department of Mathematics, N. S. S. College, Cherthala, Kerala, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 K. Raza (ed.), Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, Studies in Computational Intelligence 923, https://doi.org/10.1007/978-981-15-8534-0_8

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Hubei province in China. This was the beginning of a long-standing fight of the world against a dangerously affecting disease which slashed the economy of almost all countries in the world. The time between the exposure to the virus and the appearance of the symptom (the incubation period for COVID-19) is on average 5–6 days. But in some cases, it can rise to 14 days. During this pre-symptomatic period, some infected persons can be contagious. The recovery time of the disease also varies significantly from person to person. For persons having normal immunity, it is about two weeks, while people with severe or critical diseases may take three to six weeks to recover [7]. The first case of COVID-19 in India was reported with the case of a student who had returned for vacation from Wuhan University in China. It was reported on 30th January 2020 in Thrissur District in Kerala [8, 9]. The second and third cases were reported on 2nd and 3rd February, respectively who were also students returned from Wuhan. These cases were in Alappuzha and Kasaragod districts in Kerala. Thus Kerala state has an important position in the history of the spread of COVID-19 in India. Kerala has rich experience in handling the spread of the virus effectively. After one month, on 8 March, five people of the same family were tested positive in Ranni, Pathanamthitta District, Kerala. This case was entirely different from the previously confirmed cases. Three of them came from Italy while others were in close contact with them. After they arrived in Kerala, avoiding the symptoms of fever and a great chance of becoming COVID-19 positive, they visited some houses of relatives, many shops, public places, and even a police station. After confirmation of viral infection, the district authorities quickly prepared a detailed route map of the infected. Their socially irresponsible behavior aroused strong public criticism. In all the previous cases the authorities were able to quarantine the diseased immediately after the cases were positively confirmed while the last case slipped a little from the control. Kerala is a southern state of India which is famous for its 100% literacy and has a well-developed network of the health care system. In recent years it caught the attention of the world due to the attack by the deadly virus such as NiV and CoV. The people of Kerala travel extensively in other countries to study, work, and visit. The first outbreak of NiV was reported in Kerala in the year 2018 in Kozhikode district [10, 11]. The diagnosis and control measures taken immediately after the detection of the virus infection helped the state successfully contain the disease within the limits of the district. Further details on the infected individuals and their contacts are given in [11]. The government agencies previously have similar kinds of experiences of the outbreak of disease and they have successfully controlled all of them without allowing it to transmit to other states and foreign countries. Identifying the affected cases in an early stage, isolating infected individuals, tracing the contacts of the infected, bringing all the contacts under strict observation, quarantining the most important cases, giving timely information to the public everything known about the disease, etc. are the main reason for the success achieved in these cases. Mathematical epidemiology is a branch of applied mathematics that deals with the study of the spread and control of various kinds of diseases using the methods of mathematics and mathematical modeling. For a systematic study of the topic, the reader is referred to [12]. The most important and widely applied

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mathematical models in epidemiology are the Kermack—McKendrick models or compartmental models. The model was first proposed by W. O. Kermack and A. G. McKendrick in a series of three papers [13–15] in the first half of the twentieth century. In the following section, we briefly discuss the SIR models and use it to predict the size of the disease spread in the districts of Kerala where the disease was reported in the initial stage. In the third section, we focus on the actual data related to the spread and control measures and make a comparison with the predictions of the SIR model.

2 Kermack—McKendrick Model and Epidemic Analysis The Kermack—McKendrick models divide the whole population into compartments according to the nature of the disease. For example, SIS models (Susceptible— Infected—Susceptible models) are used to model the diseases which do not provide immunity to the infected. So an individual once recovered can again be infected. Here the division is only into Susceptible and Infected group. Common cold and influenza are examples of the disease which can be modeled using the SIS model. On the other hand, some diseases provide permanent immunity to the recovered against the disease. So we provide one more compartment to represent the recovered people (R). Measles, mumps, and rubella are examples of the diseases that can be modeled by SIR models. An SEIR model is a modified version of the SIR model by incorporating the exposed compartment (E) which stands for the exposed portion of the population. A person is said to exposed to the infection if there is a significant incubation period during which individuals have been infected but are not yet infectious. Both SIR and SEIR models are being used to study the dynamics of the COVID-19 pandemic. Yang et al. [16] have studied the effect of the modified SEIR and Artificial Intelligence prediction method in the prediction of epidemics of COVID-19 in China under public health interventions. In this paper, they have used the SEIR model to predict the trends of disease spread. They found that the epidemic of China would peak by late February, gradually decline by the end of April. A delay in implementation for five-days would have increased three-fold the epidemic size in mainland China. In the study of Chen et al. [17] six important questions related to the dynamics and disease control of COVID-19 and tried to answer them in the context of the spread in mainland China using a time-dependent SIR model. They have extended the study to Japan, Singapore, South Korea, Italy, and Iran and determined the basic reproduction number for these countries. The time-dependent SIR model tracks two-time series: One is the transmission rate at time t and the other is the recovering rate at time t. This approach is more adaptive and robust than the traditional static SIR models. The study also addresses the effect of undetectable persons, social distancing, lockdown, etc. on the spread of disease. A variant of the SIR model is used by Calafiore et al. [18] to study the nature of the spread of COVID-19 specifically for Italy. The purpose of the work was to explain and predict the future evolution of the disease to verify the effectiveness of the

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containment and lockdown measures. Roda et al. [19] present a different approach to the problem. Using the Akaike Information Criterion (AIC) for model selection, they compared the performance of the SIR model with the SEIR model in representing the information obtained by the actual data and proved that SIR models perform better in predicting the disease than its complex variation. It provides us an insight into the fact that increasing the complexity of the model cannot always ensure more reliable information. Effectiveness of optimal lockdown by minimizing the output cost of the lockdown is studied by Alvarez et al. [20]. They have analyzed the effect of the lockdown at various stages of the disease spread and its impact on the economy. Maier et al. [21] have come up with an explanation for the sub-exponential growth of confirmed cases during the early stage of disease propagation in China. They have shown that this is due to the direct consequence of containment policies. In the model, the effect of quarantine of symptomatic cases and large scale isolation practice is taken into account. The effect of physical distancing and various control measure strategies are very effective in the control of the disease as evidenced by the studies of Prem et al. [22] and Fang et al. [23]. The applications of artificial intelligence and related technologies in the control of COVID-19 is the focus of the research of Raza [24]. The spread and dynamics of the Nipah outbreak in Bangladesh have been studied using SIR models and their extensions [25]. A critical analysis of the study in the context of the same disease in Kerala was done by Reji Kumar [26]. It is found that a network-based analysis along with contact tracing, isolation, quarantining, etc. have a very serious impact on the control of the disease. Modeling and predictions based on the compartmental models can give only a partial picture of the dynamics. It is because the population in a geographically vast area is divided into communities and sub-communities which have a very different pattern of interconnections. The main drawback of the compartmental model is that it doesn’t consider the community structure and variations of transmission within different communities. Another important factor that must get attention is the impact of personal variations on the spread of the disease. Different individuals respond to the knowledge of the spread of disease in different ways. The S I R compartment model divides the whole population into three compartments S(t), I (t), and R(t). These compartments stand for susceptible, infected, and recovered individuals in the whole population (denoted by N(t)) respectively. So we have N(t) = S(t) + I(t) + R(t). As the disease progresses the members are transferred from one compartment to another. Suppose the recovery rate (removal rate) of the disease is γ . So the change in the number of recovered people Rn = Rn+1 − Rn = γ In . Here γ represents the proportion of the infected individuals, who are removed each week. In is the number of infected in the n th week. The change in In is due to the newly infected cases which are caused by the interaction of susceptible and infected and the recovery from the disease. The number of newly infected persons in a week is the product of the total number of infected (In ), the number of susceptible (Sn ) in the previous week and a parameter α. The parameter α, which is called the transmission coefficient and represents the likelihood that the interaction between a susceptible and infected can result in a new infection. So, the change in the number

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of infected people In = In+1 − In = αSn In − γ In . Simultaneously the number of susceptible changes, which can be expressed by Sn = Sn+1 − Sn = −αSn In . Thus we get the following set of difference equations, Sn+1 = Sn − αSn In In+1 = In + αSn In − γ In Rn+1 = Rn + γ In

(1)

It satisfies the initial conditions, S(0) = S0 ≥ 0, I (0) = I0 ≥ 0, R(0) = R0 ≥ 0. The parameters α and γ are determined as follows. T he number o f new cases in week 1 (I (0))(N − I (0))

(2)

1 Average duration o f the in f ectious period

(3)

α= γ =

The average duration of the infectious period is the time during which an infected person remains infected. Next, we use this model to predict the disease in Kerala. The above difference equations are used in excel software to predict the number of infected, susceptible, and recovered in the weeks following 1st February 2020, because the five patients traveled and visited many places one week before the day they were declared positive (on 8th February). The first three confirmed cases of students, who came from Wuhan, are not taken as the starting point of the simulation because they were directly taken to quarantine form the point of their arrival in India. On the other hand, the couple and their son who came from Italy traveled in three districts hiding the fact that they were infected. This allowed them to have contact with the public. Two more family members were confirmed positive subsequently. They were kept under quarantine in Pathanamthitta General Hospital. Following the irresponsible behavior of the family, the government issued a’high alert’. As the patients traveled in some places in three districts (Pathanamthitta, Eranakulam, and Kollam), we consider the total population of the places they visited for completing the simulation. The total population of the places is estimated at approximately equal to 1,00,000. In the predictions using the model, we accept the average period of the infection as two weeks. So the removal rate is 0.5. The number of infections in the following weeks in the three districts is 3. So we estimate the transmission coefficient as 3/(3 × 100000) = 0.00001. The graph of the simulation is shown in Fig. 1, which shows the peak of infection after 25 weeks and then gradually comes down to zero. Next, we proceed to analyze the actual data of COVID-19 of the state and make a comparison with the data obtained using the model. In Table 1 the weekly details of the confirmed, recovered, and deaths are given [27]. It shows a significant difference from the estimated Fig. 1. According to the estimate, the number of confirmed cases due to contact must rise to 18,000 approximately before it starts to come down. By

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Fig. 1 SIR simulation of with α = 0.00001 and γ = 0.5 Table 1 Weekly number of infected, recovered and dead of COVID-19 in Kerala [27]

Date

Recovered

Confirmed

Dead

30th January

0

1

0

6th February

0

2

0

13th February

0

0

0

20th February

0

0

0

27th February

0

0

0

5th March

0

0

0

12th March

3

16

0

19th March

0

9

0

26th March

9

109

0

2nd April

16

149

2

9th April

69

70

0

16th April

148

38

0

23rd April

71

53

0

30th April

67

50

1

7th May

91

5

0

14th May

19

58

0

21st May

17

130

0

28th May

45

398

4

4th June

135

500

7

11th June

277

656

4

18th June

446

550

3

25th June

528

932

1

30th June

363

716

2

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the end of June, the number of active infected cases by direct contact per week must reach 4000. But the number reported as confirmed cases by contact is far below the estimated figure (only 716 as on 30th June). As per the data available at the end of June confirmed cases are increasing significantly. About 80% of these cases are those who came to Kerala from other states and other countries. The number of infected by contact has not increased as inferred from the simulation. This disagreement in the spread by contact is due to the strict measures adopted by the state government to control the disease. We will discuss these measures in the next section.

3 Effect of the Measures Taken by the State to Control the Disease From the first day, a COVID-19 case reported in Kerala, the government arranged the facilities to quarantine the patients. Two types of quarantine facilities were arranged. One is the hospital quarantine. It is for symptomatic cases and confirmed cases. The second is home quarantine, which is meant for the people who are asymptomatic and suspected to get an infection because of their contacts with confirmed cases. Besides, all those who have come from the places outside Kerala are also quarantined. The quarantine status of the state from March to July is given in Fig. 2. Social distancing, wearing masks outside the home, using mobile applications to track the patients, etc. are other strictly implemented measures [28]. Using the route map all probable cases were identified and brought under strict quarantine. A semi-lockdown was implemented in the first week of March. Public response to the government initiated control measures is very high in the state. “Break the chain” 300000 250000 200000 150000 100000 50000 0 01/03/2020

01/04/2020

Hospital Quarantine

01/05/2020

01/06/2020

Home Quarantine

Fig. 2 Quarantine type of primary and secondary contacts 30th June 2020 [27]

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Table 2 Nature of the disease as on 30th June 2020 Districts

Through Contact

Thiruvananthapuram

29

Kollam

Imported (From other states and countries) 185

Total 214

35

307

342

Pathanamthitta

8

281

289

Alappuzha

9

291

300

Kottayam

25

195

220

Idukky

22

87

109

Ernakulam

21

228

249

Thrisur

60

342

402

Malapuram

62

441

503

Palakadu

52

475

527

Kozhikodu

18

243

261

Wayanadu

22

73

95

Kannur

78

402

480

Kazargodu Total

83

368

451

524

3918

4442

campaign is an example of a safety measure initiated by the government which was widely accepted all over the state. Social distancing has also got equal acceptance. The state went to full lockdown when the central government announced it. Till the end of the lockdown, Kerala managed to decrease the number of infected cases. When the lockdown period was over there was a rapid increase in the movement of people in public places. Simultaneously the examinations of the state board and the Universities started. This might have increased the chance of forgetting social distancing and the basic precautions to defend the transmission. The arrival of the people stranded outside the state during the period of lockdown has geared up the number of confirmed cases. Along with this, reporting of confirmed cases without any information about the source of infection has increased the fear of community spread in the state. The reality behind the current situation is yet to be unearthed in the forthcoming weeks. Table 2 given below shows the details of the confirmed cases in all districts classified according to the source of origin. The quarantine status of the primary and secondary cases up to 30th June 2020 is shown in Fig. 2.

4 Conclusion In this paper, we analyze the effect of the great pandemic COVID-19 in Kerala, a southern state of India. The difference equations of the SIR model are used in excel to estimate the number of infected, susceptible, and recovered in a consecutive period of one week. It is then compared to the actual data available up to 30th June 2020.

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The actual spread is much less than what is predicted by the model. It is because of the effect of the safety and control measures taken by the government to contain the disease. The spread of the disease is on the go at the time of preparation of this article. So a more detailed analysis of the situation is a work for the future. Furthermore, analysis of the situation using other modified versions of SIR models remains to be done. Acknowledgements I express my gratitude to the anonymous reviewers for the valuable comments and suggestions that helped me to improve the quality of this chapter.

References 1. World Health Organization. (2020). Coronavirus disease 2019 (COVID-19) Situation Report— 152. WHO. Available from. https://www.who.int/docs/defaultsource/coronaviruse/situationreports/20200620-covid-19-sitrep152.pdf?sfvrsn=83aff8ee_4. Accessed on June 23 2020. 2. https://www.who.int/csr/don/05-January-2020-pneumonia-of-unkown-cause-china/en/. Accessed on 30 June 2020. 3. https://www.who.int/csr/don/12-January-2020-novel-coronavirus-china/en/. Accessed on 30 June 2020. 4. https://www.who.int/csr/don/14-January-2020-novel-coronavirus-thailand/en/. Accessed on 30 June 2020. 5. https://www.who.int/csr/don/17-January-2020-novel-coronavirus-japan-ex-china/en/. Accessed on 30 June 2020. 6. https://www.who.int/csr/don/21-January-2020-novel-coronavirus-republic-of-korea-ex-chi na/en/. Accessed on 30 June 2020. 7. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200402-sitrep-73COVID-19.pdf?sfvrsn=5ae25bc7_6. Accessed on 30 June 2020. 8. https://en.wikipedia.org/wiki/Timeline_of_the_COVID-19_pandemic_in_India. Accessed on 30 June 2020. 9. Mukesh, R. (2020). Coronavirus in India: Tracking country’s first 50 COVID-19 cases; what numbers tell. India Today. Retrieved 12 March 2020. 10. Ajith Kumar, A. K., & Anoop Kumar, A. S. (2018). Deadly Nipah outbreak in Kerala: Lessons learned for the future. Indian Journal of Critical Care Medicine, 22, 475–476. 11. Arunkumar, G., Chandni, R., Mourya, D. T., Singh, S. K., Sadanandan, R., Sudan, P., et al. (2018). Outbreak investigation of Nipah virus disease in Kerala, India, The Journal of Infectious Diseases, https://doi.org/10.1093/infdis/jiy612. 12. Brauer, F., Van den Driessche, P., & Wu, J. (Eds.). (2008). Mathematical epidemiology. Berlin, Heidelberg: Springer. 13. Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings Royal Society London B Biological Science, 115, 700–721. 14. Kermack, W. O., & McKendrick, A. G. (1932). Contributions to the mathematical theory of epidemics, part. II. Proceedings Royal Society London, 138, 55–83. 15. Kermack, W. O., & McKendrick, A. G. (1932). Contributions to the mathematical theory of epidemics, part. III. Proceedings Royal Society London B Biological Science, 141, 94–112. 16. Yang, Z., Zeng, Z., Wang, K., Wong, S. S., Liang, W., Zanin, M., et al. (2020). Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. Journal of Thoracic Disease, 12(3), 165. 17. Chen, Y. C., Lu, P. E., Chang, C. S., & Liu, T. H. (2020). A Time-dependent SIR model for COVID-19 with undetectable infected persons. arXiv preprint arXiv:2003.00122.

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18. Calafiore, G. C., Novara, C., & Possieri, C. (2020). A modified sir model for the COVID-19 contagion in Italy. arXiv preprint arXiv:2003.14391. 19. Roda, W. C., Varughese, M. B., Han, D., & Li, M. Y. (2020). Why is it difficult to accurately predict the COVID-19 epidemic? Infectious Disease Modelling, 5(2020), 271–281. 20. Alvarez, F. E., Argente, D., & Lippi, F. (2020). A simple planning problem for COVID-19 lockdown (No. w26981). National Bureau of Economic Research. 21. Maier, B. F., & Brockmann, D. (2020). Effective containment explains sub exponential growth in recent confirmed COVID-19 cases in China. Science, 368(6492), 742–746. 22. Prem, K., Liu, Y., Russell, T. W., Kucharski, A. J., Eggo, R. M., Davies, N., et al. (2020). The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modeling study. The Lancet Public Health. 23. Fang, Y., Nie, Y., & Penny, M. (2020). Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: A data-driven analysis. Journal of Medical Virology, 92(6), 645–659. 24. 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. Studies in Big Data, 78, 2020. Berlin: Springer (In Press). 25. Haider Ali Biswas. (2012). Model, and control strategy of the deadly Nipah virus (NiV) infections in Bangladesh. Research and Reviews in BioSciences, 6(12), 370–377. 26. Reji Kumar, K. (2020). Nipah outbreak in Kerala—A network-based study, to appear in the proceedings of the International conference, ICMMCMSE 2020. 27. http://dhs.kerala.gov.in/. Accessed on 30 June 2020. 28. http://dhs.kerala.gov.in/route-map/. Accessed on 30 June 2020.

Computational Intelligence Approach for Prediction of COVID-19 Using Particle Swarm Optimization R. S. M. Lakshmi Patibandla and V. Lakshman Narayana

Abstract Computational Intelligence (CI) is predicated on naturally motivated computational procedures. The strategic supports that comprise this arena are Genetic Algorithms, Neural Networks, and Fuzzy Systems. Neural Networks are procedures that will be recycled for function classification or estimation difficulties. They embrace Supervised, Unsupervised, and Reinforcement Learning. Genetic Algorithms, conversely, the pursuit of procedures motivated by gradual genetics. Crossover and Mutation are the two important trust operators. The populace of people signifying resolutions to the matter is twisted over numerous cohorts. The procedure customs an arbitrary steered methodology to optimize problems supported by a fitness function. Fuzzy Logic is predicated on Fuzzy Set theory so on embracing cognitive that’s fluid or imprecise slightly than secure and precise. Fuzzy Logic variables consume certainty standards extending in score between 0 and 1 which may also switch restricted certainty Computational Intelligence methods are effectively utilized in various real-life applications during a diversity of commercial and medical problems. Data processing is designated as mining of pertinent info as of copious capacities of knowledge to market the commercial buzz and deciding proficiency. The empirical analysis of data may be an analogous performance for succinct and classifying the designs within the data. This empirical data exploration may be statistical, a regression model, a discriminate model, or a clustering model, which is produced using the data and is used for forecast or prediction, classification, or hypothesis verification. We introduced a standard rule detection process reinforced swarm intelligence to advert the eminence creation of rules and smear the rule pruning appliance to condense the rule. The predictable technique usages COVID-19 and Mammographic Corpus Statistics. This procedure, while smeared on COVID-19, initiate to R. S. M. L. Patibandla (B) Department of IT, Vignan’s Foundation for Science, Technology, and Research, Guntur, AP, India e-mail: [email protected] V. L. Narayana Department of IT, Vignan’s Nirula Institute of Technology and Science for Women, Guntur, AP, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 K. Raza (ed.), Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, Studies in Computational Intelligence 923, https://doi.org/10.1007/978-981-15-8534-0_9

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be extra precise associated with the prevailing Classification procedures explicitly Classification using Decision Trees. This performance analysis has been achieved with slightly more balanced trees using entropy-based Information gain measures. Keywords Analysis · Data · Prediction · Fuzzy logic · COVID-19

1 Introduction Logical critical thinking comes from the securing of information from a particular domain, the control of such information, and the intercession in reality with the controlled information. The more thorough and better organized the information base, the more it imitates logical progressions, and in this way the simpler the arrangement is to investigate progressively logical issues with satisfactory understandings. As its history demonstrates, computational insight isn’t just about robots.

1.1 Computational Intelligence The IEEE Computational Intelligence Society included new zones of intrigue, for example, fuzzy frameworks and transformative calculation, which they identified with Computational Intelligence in 2011. Personal Computers (PCs) have been utilized for better understanding and translation of procedure conduct dependent on the accessible data to get input-yield mapping and dynamic. The usage of master (administrator) information, capacity to utilize loose, unsure data, incorporation of information over numerous orders, mechanized Artificial Intelligence (AI) Modelsfrom nature (neuroscience, hereditary qualities, conduct science), the advancement of models for improving the framework execution fulfilling the inalienable framework/process limitations. CI is something in which Intelligence is worked in PC programs. Computational Intelligence is a subset of Artificial Intelligence. There are two sorts of machine insight, one is the counterfeit one dependent on hard registering strategies and another is the computational one dependent on delicate registering techniques, which empower adjustment to numerous circumstances [1–3]. The principle utilization of Computational Intelligence incorporates software engineering, designing, information investigation, and biomedicine. This extensively covers Evolutionary processing, Fuzzy registering, neuro-figuring, and delicate registering. Computational Intelligence is in this manner a method of performing like people. The principal away from of Computational Intelligence was presented by Bezdek in 1994: a framework is called computationally savvy if it manages low-level information, for example, numerical information, has an example acknowledgment part and doesn’t utilize information in the AI sense, and when it starts to show computational adaptively, adaptation to internal failure, speed moving toward human-like

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turnaround and mistake rates that estimated human presentation [4]. Computational Intelligence is a subset of Artificial Intelligence. The counterfeit one is dependent on hard figuring procedures and the computational one dependent on delicate processing strategies, which empower adjustment to numerous circumstances. Computerized reasoning and Computational Intelligence look for a comparable long haul objective: arrive at general knowledge, which is the insight of a machine that could play out any savvy task that a person can; there’s an unmistakable distinction between them. CI additionally utilizes Artificial Neural Networks (ANN) comprising of multilayer Perception idea (MLP), Rounded Basis Function (RBF), and Probabilistic Neural Networks (PNN) [5]. Likewise, a fuzzy Logic and ANN procedure utilizes aAdaptive Network-based Fuzzy Inference System (ANFIS). The ANN structure comprises of an info layer, concealed Layer (s), and Output layer, number of hubs in each layer and works, and their boundaries. The means of the fuzzy logic approach are Fuzzification utilizing Member Functions (MFs)- input, age of the standard base, conglomeration, and De-fuzzification utilizing MFs – yield. The info yield of enrollment capacities needs a number, type, boundaries, and a standard base. The Neurofuzzy framework joins the methodology of Fuzzy Logic (FL) and ANNs. This begins with an underlying FL structure. Further, the Neuro-fuzzy framework additionally utilizes ANN for adjusting the FL boundaries and the standard base to the preparation information.

1.2 Particle Swarm Optimization (PSO) In 1995, Kennedy and Eberhart presented the particle swarm optimization as an unsure quest strategy aimed at advancement resolutions. The calculation is present motivated by the mass development of winged animals searching intended for sustenance. Every arrangement in particle swarm optimization is known as a particle that is comparable to a fowl in the sectionindividual figure development calculation. Every particle consumes resources that are determined by a fitness work that increments as the particle in the inquiry universe move toward the objective [6]. Every particle likewise has a speed that controls the movement of the particle. Every particle keeps on moving in the issue universe by following the ideal elements in the recent state. The PSO technique is established in Reynolds’ work that is an initial reproduction of the societal conduct of winged animals. The mass of particles in nature speaks to aggregate knowledge. Think about the aggregate development of fish in the water or aerial animals through relocation. Entirely individuals transfer in ideal amicability with one another, chase together on the off chance that there to be pursued, and break from the grasp of a raider by stirring extratarget on the off chance that they are to be gone after.

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1.3 COVID-19 Access to exact episode expectation models is fundamental to get experiences interested in the imaginable extent and results of irresistible maladies. Supervisions and former authoritative groups depend on taking place bits of knowledge as of expectation prototypes to propose innovative arrangements also to survey the capability of the upheld strategies. The epic COVID-19 has been accounted for to contaminate more than 2 million individuals, with over 132,000 affirmed deaths around the world. The ongoing worldwide COVID-19 epidemic has shown a non-linear and composite environment. What’s more, the incidence conveys contrasted using former late explosions that convey crazy about query the capacity of usual replicas to convey precise outcomes [7–9]. The demonstrating methodology stances conformed to the supposition of conveying the irresistible illness from side to side contacts, since three unique classes of very much blended populaces; defenseless to disease (class S), tainted (class I), and the expelled populace (class R is given towards the individuals who consume recuperated, created invulnerability, existed detached or else died). The situation is additionally accepted that the class I transfers the disease to class S wherever the quantity of likely diffusions is relative towards the all outnumber of interactions. The expanse of people in the class S advances as per a period arrangement regularly processed utilizing an essential differential condition as follows: dS = −αS I dt

(1)

where I is the contaminated populace, and dS is the helpless populace both as parts. α speaks to the day by day multiplication pace of the differential condition, directing the quantity of helpless irresistible interactions. The estimation of S in the timearrangement created via the discrepancy condition bit by bit decays. At first, it is expected that at the beginning time of the flare-up S1 while the quantity of people in class I is unimportant. Along these lines, the augmentation dI gets straight and the class I, in the long run, can be figured as follows: d = αS I −β I idn  t

(2)

where β I manages the day by day pace of new diseases by evaluating the number of contaminated people skilled in the transmission. Besides, the class dR, speaking to people rejected from the spread of disease is registered as follows: dR = βI dt

(3)

Further down the unconfined states of the barred gathering, Eq. (3), the explosion growth be able to figure as follows:

Computational Intelligence Approach for Prediction of COVID-19 …

I (t) ≈ I 0e{(α − β)}

179

(4)

The epidemics of extensive scope of irresistible disorders consume existed displayed utilizing Eq. (4). Be that as it may, for the COVID-19 episode forecast, because of the exacting estimates authorized by specialists, the vulnerability to disease has been controlled drastically. To assess the exposition of the models, the middle achievement of the flare-up expectation presents helpful data [10, 11]. The median expectation factor can be determined as follows: To evaluate the proximity of the models, the center accomplishment of the scene figure presents significant evidence. The median estimate concern can be resolved as follows: f =

Pr ediction T r uevalue

(5)

1.4 Need of CI and PSO in Detection of COVID-19 Particle Swarm Optimization calculation be present a populace centered stochastic streamlining empirical. Used, for the most part, particle swarm optimization can work continuously in some sort of pursuit space. A particle is a moving point in n-dimensional space [12–15]. Other than the position xi (p) and speed vi (p), at every cycle p, the particle I will recollect the nearby best position yi (p) in the pursuit space it has visited up until now and the worldwide best position y (p) worldwide different particles (counting itself) have visited up until now. Let x(p) speaks to the situation of particle x at emphasis p. The situation of this article in next emphasis is accomplished by including the speed vector v(p + 1) to the situation in the current cycle: x( p + 1) = x( p) + v( p + 1)

(6)

The velocity is calculated as follows: vi j( p)+ = ωvi j p + c1r 1 j pyi j p − xi j p + c2r 2 j pyglobal j p − xi j p

(7)

where vij(p) and xij(p) are the speed and position of particle i in measurement j, ω is the inactivity weight factor, c1 and c2 sensitivities to the neighborhood best position and worldwide best position separately and r1j and r2j are irregular qualities from 0 to 1. Motivation and Contribution: Due to the COVID-19 pandemic, the world is suffering severely and many number of deaths happen in very shortest time span. To predict the COVID pandemic, computational intelligence techniques are used for accurate prediction. The motivation of the proposed work is to perform prediction of

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COVID-19 using computational intelligence techniques. The particle optimization technique is used with AI techniques for prediction of COVID-19. Organization of the Chapter: The remaining chapter discuss about the PSO Technique for prediction of COVID-19 in Sect. 2, Sect. 3 discuss about use of Reinforced Swarm Intelligence in Prediction of COVID-19 and then in Sect. 4 performance analysis is included and then the chapter is concluded.

2 PSO in the Prediction of COVID-19 Developmental calculations are incredible assets for taking care of streamlining issues through insightful techniques. These calculations are frequently propelled by common procedures to scan for every conceivable answer as an enhancement issue. In the current examination, much of the time utilized calculations, i.e., Genetic Algorithm (GA), PSO is utilized to appraise the boundaries by tackling cost work. Particle properties in this calculation include: Each particle freely searches for the ideal point. Every particle moves at a similar speed at each progression. Every particle recollects that one finest situation in the universe. The elements cooperate just before educate to each former of the adverts they are searching for. Every particle is in connection with its adjoining atoms. Each particle knows about the particles that are in the area [16–19]. Accordingly, PSO is an irregular improvement calculation that can look for unknown and complex regions. This makes PSO more adaptable and tough than customary techniques. PSO manages no differential target capacities because the PSO utilizes the data result (execution record or target capacity to direct the hunt in the difficult zone). The nature of the proposed course reaction doesn’t rely upon the underlying populace. Beginning from anyplace in the inquiry space, the calculation at last merges on the ideal answer. PSO has extraordinary adaptability to control the harmony between the nearby and generally speaking pursuit space [20]. This one of a kind PSO property conquers the issue of inappropriate intermingling and expands the pursuit limit. These highlights make PSO not quite the same as the GA and other imaginative calculations. In the current investigation, PSO was utilized for estimation of the boundaries of Eq. 8 to 15. The populace number was chosen to be 1000 and the emphasis number was resolved to be 500 as indicated by various experimentation procedures to diminish the cost work esteem. The prediction mathematical models with equations are depicted in Table 1. A, B, C, μ, and L are boundaries that portray the previously stated capacities. These quantities should be evaluated to build up an exact assessment exemplary. At all of the objectives of this investigation existed to display time-arrangement information dependent on the calculated infectious development exemplary. The uncertain work was characterized as the Mean Square Error amongst the objective and assessed ethics as indicated by Eq. (16).

Computational Intelligence Approach for Prediction of COVID-19 … Table 1 Prediction mathematical models

Name of the model

Description

Logistic model [12] R = A/(1 + exp(((4*μ)*(L-x)/A) + 2))

181 Equation number (8)

Linear model [14]

R = Ax − B

Logarithmic model [3]

R = A + Blog(x)

(10)

Quadratic model [6]

R = A+Bx + Cx2

(11)

Cubic model [8]

R = A+Bx + Cx2 + Dx3

(12)

Compound model [4]

R = ABx

(13)

Power model [1]

R = AxB

(14)

Exponential model [5]

R = AEXP(Bx)

(15)

 MSE =

(Es − T )2 N

(9)

(16)

Wherever Es raise to predictable standards, T denotes to the target ethics and N denotes to the numeral of data (Fig. 1).

3 Reinforced Swarm Intelligence in Prediction of COVID-19 The discrete reinforced learning calculation is a viable technique to improve the effectiveness of reinforced learning [21]. In this proposal, we propose an appropriate design for profound support learning joined with PSO to tackle the deficient investigation issue for the current conveyed Reinforced Learning (RL) structure. This engineering can be applied to the profound RL calculations (for instance Deep Q Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO)) in which DDPG is an off-policy algorithm, PPO is an on-policy algorithm that is easy to implement andDQN requires a Target network for stability which is time consumingof which worth capacity or conduct strategy can be spoken to by a capacity estimated, for example, a neural system. Since our dispersed engineering acquires the PSO calculation to investigate the state and activity space by haphazardly producing numerous particles, specifically the voyager operators in this proposal, this design has better keyspace investigation capacity. The structure of Reinforce learning is depicted in Fig. 2.

182 Fig. 1 PSO Algorithm

Fig. 2 Structure of reinforce learning

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Every specialist tests and updates boundaries independently. To improve investigation, we consolidate the PSO during the time spent producing wayfarer specialists and refreshing the neural system boundaries. The places of these specialists are instated haphazardly and they select activities utilizing diverse worth capacity or conduct strategy. They additionally update their boundaries of the worth capacity or conduct strategy under the impact of other adventurer specialists. The pioneer specialists update the boundaries of the neural system, which is utilized to fit the worth or strategy work calculated as: θ new = θ old + α · θ newL(θ )

(17)

Here which θ old and θ new are the boundaries of the system when update;∇θL(θ) is the angle of loss work; θ ∗ is the ideal boundaries of the system; α is the learning rate; r is arbitrary qualities from 0 to 1; c is the heaviness of ideal boundaries which will likewise refresh during the preparation [22]. Watching the above capacity, we can find that the new update capacity will permit the wayfarer operator to have free learning capacity just as gaining from others, which is useful for learning the ideal procedure. The investigation brings about the fifth part additionally confirm this. Direct operators: In Eq. 15, the estimation of weight c and ideal boundaries of the system θ ∗ are basic to improve the effectiveness of conveyed learning. Be that as it may, the information on the situations utilized in the assignment is mind-boggling and obscure. The ideal boundaries of the system must be gotten by looking at the aftereffects of operators under various neural system boundaries. Since the preparation itself is irregular, the realness of boundaries is hard to ensure. On the off chance that the boundaries are not the best and we pick a huge weight c, it will make the operators update in a misguided course. Likewise, toward the start of preparing, voyager operators can just accomplish low scores, so gaining from others has minimal beneficial outcomes. On the off chance that the absolute return of the manage operators under boundaries is more noteworthy than the onset acquired by the specialists utilizing the current ideal neural system boundaries, the ideal boundaries will be refreshed, else they won’t be refreshed [23]. To additionally assess the boundaries, the direct specialists will continue testing the current ideal neural system boundaries before the voyager operators locate the following conceivable ideal neural system boundaries. At whatever point there is a bigger all-out a return, the impermanent variable x answerable for recording will include 1. At the point when x is bigger than the edge b, the weight c is given equation will be refreshed utilizing the accompanying capacity: c = ar ctan(x/200b)

(18)

Here the weight c extending from 0 to pi/2 is a limited capacity on x. The presentation of the direct specialist can adequately evade the visual impairment of the operators’ gaining from one another and stay away from the free-fall decline, which is demonstrated by our following trial. Additionally, ideal neural system boundaries will be refreshed rapidly toward the start of preparing since the operators can just

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accomplish low scores. This will keep the weight c exceptionally little, subsequently, guarantee that the operators chiefly learn without anyone else at this stage [24].

4 Performance Analysis In healthcare industries, a decision tree can tell whether a patient is suffering from a disease or not based on conditions such as age, weight, sex, and other factors. Other applications such as deciding the effect of medicine based on factors such as composition, a period of manufacture, etc. Also, in the diagnosis of medical reports, a decision tree can be very effective (Fig. 3).

Fig. 3 Decision tree in healthcare

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There is an operational definition of surveillance and response provided by the Indian government to implement prevention and control in dealing with COVID-19 outbreak cases with the following categories: 1. Patient under Supervision (PUS) or “PasienDalamPengawasan (PDP)”. 2. Person in Monitoring (PIM) or “Orang DalamPemantauan (ODP)”. 3. Person without Symptoms (PWS) or “Orang TanpaGejala (OTG)”. Some of the symptoms along with their respective codes from the COVID-19 surveillance categories include: 1. 2. 3. 4. 5.

Fever or history of fever (G01). Symptoms and signs of respiratory distress (cough, cold, sore throat, etc.) (G02). Severe Pneumonia or Acute Respiratory Infections (ARI) (G03). There are no other causes based on convincing clinical descriptions (G04). In the last 14 days before the symptoms have a history of travel or living abroad who reported local transmission (G05) 6. In the last 14 days before the symptoms have a history of travel or stay in the local transmission area in Indonesia (G06). 7. Contact with Coronavirus Disease 2019 (COVID19) confirmation cases in the last 14 days before symptoms (G07). C4.5 Algorithm The decision tree method is derived from the learning systems concept. One of the methods developed is the C4.5 algorithms which can deal with attributes continuous value. The C4.5 algorithms can be used to handle data classification problems. Several elements must be looked for in the decision tree modeling using the C4.5 algorithms, including 1. Entropy(S) is a parameter used to measure the diversity of each attribute value against the decision attribute. Entr opy(S) =

n 

K − pi ∗ log2( pi)

(19)

i=n

Information: S = Sum of case samples (sampling) n = Number of partitions for S pi = Proportion of Si to S K = constant 2. Gain(S, A) is the gain value that used as the basis for forming nodes or roots and branches of a decision tree.

Gain(S, A) = E(S) −

n  |Si| i=1

Information: E = Entropy

|S|

∗ E(Si)

(20)

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S = Sum of case samples (sampling) A = Attribute n = Number of partitions for S |Si| = Sum of cases on i-partition |S| = Sum of cases in S 3. The C4.5 algorithm uses different sizes in selecting attributes to be split. C4.5 algorithm uses the Gain Ratio in the calculation process instead of the Information Gain obtained. Split Information is first calculated with the following Formula:

Split I n f o.(S, A) = −

n  |Si| i=1

|S|

∗ log2

|Si| |S|

(21)

Therefore, the Gain Ratio value can be calculated with the following equation: Gain Ratio(S, A) =

Gain(S, A) Split I n f or mation(S.A)

(22)

Information: S = Sum of case samples (sampling) A = Attribute n = Number of partitions for S |Si| = Sum of cases on i-partition |S| = Sum of cases in S 4. The steps in the process of decision tree formation using the C4.5 algorithms as follows: a. Find the Entropy(S) value for total cases and each attribute value. b. Find the Gain (S, A), Split Information (S, A), and GainRatio (S, A) for each attribute value. c. Create root, node, and branches based on the highest Gain Ratio value. d. Repeat the process for each branch (Fig. 4). The confusion matrix calculation with three classes in this research is presented in Table 2. The accuracy testing process of the C4.5 algorithms for the COVID-19 surveillance categories in this research using Tensorflow with the confusion matrix presented in Table 3. The accuracy value of the C4.5 algorithm calculation for the COVID-19 surveillance categories shown in Table 3 is obtained from the accuracy value percentage in the performance vector that is 0.8750 × 100 = 87.50%. The PSO model is used for calculating Fitness function and computational Intelligence plays a key role in the identification of the COVID disease. The Computational

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Fig. 4 Node 1.2 of decision tree

Table 2 Confusion matrix with 3 classes Confusion matrix

Actual

Predicted

FP Class 1

Class 2

Class 3

Class1

A=6

B=1

C=2

B + C=3

Class2

D=2

E=6

F=1

D + F=3

Class3

G=1

H=2

I=6

G + H=3

D + G=3

B + H=3

C + F=3

FN

Table 3 Confusion matrix of C4.5 algorithm True PDP

True ODP

True OTG

Pred. PDP

6

1

1

Pred. ODP

1

6

2

Pred. OTG

2

2

6

100%

100%

recall

87.50%

Precision 100% 83.33% 100%

Intelligence model strongly analyzes the data, calculates fitness function using PSO, and then generates the rules for prediction of the disease. The principle utilization of Computational Intelligence incorporates software engineering, designing, information investigation, and biomedicine. Nowadays, computational intelligence models are vastly used in COVID prediction, as the prediction accuracy is high in this model which is used to identify the COVID cases and reduces risk to humans.

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5 Conclusion In this proposal, Computational Intelligence Techniques with Particle Swarm Optimization for support learning is utilized for the prediction of COVID-19. The conclusion procedure of the COVID-19 observation classifications utilizing the C4.5 calculations. The order aftereffects of the three classifications were effectively demonstrated into a decision tree. PSO model is used for calculation of fitness values and then computational intelligence models are applied for the accurate prediction of COVID. Besides, the subsequent depiction tree is as per a few finding explanations of the decision guidelines in the exploration case. The preparation information tests utilized for decision tree demonstrating in this exploration have been confirmed. In the test with a disarray framework of 3 (three) classes from the COVID-19 investigation classifications, its estimation brings about the exactness of 0.8750 (87.50%). For additional exploration, the C4.5 calculations can be contrasted and the ID3 calculation which just uses Entropy and Information Gain esteems in its computations (without Split Information and Gain Ratio). In a future examination, it can even be improved utilizing the J48 calculation which is a subsidiary of the C4.5 calculations in WEKA execution. What’s more, there is additionally the most recent strategy, to be specific the C5 calculation which is situated towards decision tree displaying. Acknowledgements The authors are thankful to the management of Vignan’s group of institutions for supporting throughout the completion of the chapter.

References 1. Patibandla, R. S. M. L., Kurra, S. S., & Mundukur, N. B. (2012). A study on scalability of services and privacy issues in cloud computing. In R. Ramanujam, & S. Ramaswamy, (Eds.), Distributed computing and internet technology. ICDCIT 2012. Lecture notes in computer science (Vol. 7154). Berlin, Heidelberg: Springer. 2. Patibandla, R. S. M. L., & Veeranjaneyulu, N. (2018). Survey on clustering algorithms for unstructured data. In V. Bhateja, C. CoelloCoello, S. Satapathy, P. Pattnaik (Eds.), Intelligent engineering informatics. advances in intelligent systems and computing (Vol. 695). Singapore: Springer. 3. Patibandla, R. S. M. L., & Veeranjaneyulu, N. (2018). Performance analysis of partition and evolutionary clustering methods on various cluster validation criteria. Arab J Science, 43, 4379–4390. 4. 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. Studies in Big Data, 78, 2020. Berlin: Springer (In Press). 5. Lakshmi Patibandla, R. S. M., Kurra, S. S., & Kim, H.-J. (2014). Electronic resource management using cloud computing for libraries. International Journal of Applied Engineering Research, 9, 18141–18147. 6. Lakshmi Patibandla, R. S. M., & VeeranJaneyulu, N. (2020). A simrank based ensemble method for resolving challenges of partition clustering methods. Journal of Scientific & Industrial Research, 79, 323–327.

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COVID-19 Insightful Data Visualization and Forecasting Using Elasticsearch Hemant Kumar Tewtia and Deepti Singh

Abstract The advent of big data analytics tools in recent years has sparked interest from different communities to take advantage of data and plan the right strategy for their organization. Data is meaningful only if it is processed correctly and transformed into some useful information. This information is very helpful in planning strategies and various decision-making tasks. COVID-19 is one such pandemic that has devastated the world on a large scale. Every country is trying to make the best of the strategies to fight against this pandemic. In this chapter, the analysis is performed on the global COVID-19 available data to get some meaningful insights about the disease. Elasticsearch’s Kibana dashboard has been used in this work which will be acting as a single viewpoint to get the insight of different statistics based on multiple key factors such as country-wise statistics, death ratio, gender distribution, infection rate, etc. This data will be used to forecast multiple statistics for different countries. It can be easily deployed over the web so that relevant authorities can utilize it as per their requirement. A machine learning approach is also used in this study to predict future trends of COVID-19 pandemic. This work will provide a good visualization over novel coronavirus and help us in planning better strategies to deal with this pandemic. Keywords COVID-19 · ELK stack · Elasticsearch · Kibana · Pandemic · Machine learning · BigData analytics

1 Introduction At present, the whole world is fighting with a novel Coronavirus disease 2019 (COVID-19). The disease has almost devastated the whole world because of its H. K. Tewtia (B) Birla Institute of Technology and Science, Pilani, India e-mail: [email protected] D. Singh Jaypee Institute of Information Technology, Noida, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 K. Raza (ed.), Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, Studies in Computational Intelligence 923, https://doi.org/10.1007/978-981-15-8534-0_10

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rapid speed of emergence, spread, and the fatality rate. It is believed to be first found in bat(s) and then somehow transmitted to humans in Wuhan, China in December 2019 [1]. The correct information in the context of original transmission in humans is still unclear and a lot of debate is happening on this in different communities. In January 2020, World Health Organization(WHO), has declared its first guidance on this novel virus, describing it as a global threat to human health and further declaring it a public health emergency to the whole world [2]. Such circumstances have created so many challenges for most of the countries in the world in various sectors such as—health, finance, education, shipping, employment, aviation, tourism to name a few. Countries can further counter these challenges by making appropriate planning for each of these sectors, creating support packages for the sufferers, planning lockdown strategies, manufacturing or exporting healthcare kits, etc. The most common question that everyone is looking up to is—when will the coronavirus pandemic end and people be back to normal life? Answer to this question is very uncertain, it depends upon the number of factors like—the rate of spread of the disease, recovery rate of the infected people, rate of research on vaccine production, rate of testing of the disease, and many more. So, there is a pressing need to do an in-depth analysis of the data available on COVID-19 and provide insightful information that will help in enhancing the planning system w.r.t coronavirus disease. The manual analysis and visualization of COVID-19 data is a very time consuming and complex task. One of the efficient and open source data analytics and visualization tools available in the market is ElasticStack. Here, in this study there are three components of ElasticStack—Logstash, Elasticsearch, and Kibana also called ELK stack [3] has been used. In this paper, the main goal is to try analyzing COVID-19 global data, to find some good insights using Logstash and Elasticsearch and further push these insights into an interactive user interface using Kibana. The machine learning feature of ELK stack is used to forecast coronavirus information using the COVID-19 dataset. The forecasting information is very useful in understanding the future trends of this disease. A user can forecast information about different parameters like—total cases for the next few months, total deaths expected in the coming months, total active case trends for future, and few more. This will give an idea to policymakers in making more appropriate strategies to cope up with the coronavirus outbreak in their respective regions. The content ahead in this paper is arranged in the following order: Sect. 2 provides a summary of ELK stack, presenting a brief overview of Elasticsearch, Logstash, and Kibana components. In Sect. 3, literature research is discussed with the inclusion of some most relevant and significant work on COVID-19 and data analysis. The next Sect. 4, describes the proposed methodology and framework. In Sect. 5a discussion onthe experimental setup and the results are done. Lastly, the conclusion of the work is discussed in Sect. 6.

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2 ELK (Elasticsearch-Logstash-Kibana) Stack A massive amount of data is being produced through various sources such as the internet, electronic devices, blogging, social networking, news feeds, etc. The Elastic Stack is an opensource software package comprising of three major components— Elasticsearch, Logstash, and Kibana. This makes a complete stack which provides an end to end solution for big data analytics, this includes the task like—storing the big data, performing queries over it, creating visualizations, forecasting future values using machine learning feature and many more [4]. Now to have a better understanding of the ELK stack, let’s have a brief look over each of these components.

2.1 Elasticsearch Elasticsearch is the central component of ELK stack which mainly focuses upon the data analytics part and full-text searching of data. It has basically, originated as a scalable version of Apache Lucene—an open-source framework for text searching written entirely in Java [5]. Elasticsearch stores data in document form, written in JSON format, which allows us to insert, delete, retrieve, analyze, and more importantly search data using query DSL(Domain Specific Language). Elasticsearch organizes the document data into a data structure called inverted index [6], which maps a text or any number to the exact document locations, this helps Elasticsearch in quick searching.

2.2 Logstash The next component is Logstash which is again open source and usedas a data processing engine. It can take data as input from various types of data sources and can process them as per the requirement of the user. It can handle data of varied complexities and formats using various feature-rich plugins. The plugins are divided mainly into three categories—receiving the input data, manipulating the data, and then sending the data to the target application. Logstash decouples the overall architecture and acts as a centralized data processing engine. Using Logstash can improve the efficiency of the overall application as any change in other applications connected to Logstash does not impact the data processing in Logstash [7].

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2.3 Kibana The last component of the ELK stack is Kibana, which serves as an interactive user interface to visualize complex data. It becomes very easy to analyze pictographically, Kibana provides the same by giving us the options of visualizing data in charts, maps, graphs, histograms, and so many others. The data which is stored in Elasticsearch can be visualized in an interactive manner using Kibana [8]. Users can create multiple dashboards in Kibana showing the data visualizations for a different audience. One can also perform a machine learning task in Kibana by just completing a few steps. Overall, it saves a lot of time in creating visualizations for the complex data and made the application more interactive [9]. Figure 1, describes a high-level architectural view of ELK stack. In this, one can observe that raw data is being sent to Logstash for preprocessing after data preprocessing Logstash sends the processed data to Elasticsearch, where it gets stored in document form. Now, data gets queried by Kibana in Elasticsearch. And then the query results were sent back to Kibana. Now whenever the user applies filters and requests to visualize the data, Kibana will output all visualization with all filters applied as per the user’s requirement.

3 Literature Search Many researchers have started working on coronavirus disease these days. The disease came into light in December 2019, so not much literature is available on it but still, there were studies available on similar kinds of diseases like SARS (severe acute respiratory syndrome) and MERS (Middle East respiratory syndrome) [10]. In this paper [11], the author has analyzed the data of coronavirus infected patients

Fig. 1 A high-level architecture of ELK stack

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of two hospitals in Wuhan, China. The analysis shows that the greatest number of infected persons mainly range from 51 to 55 years of age, however, most death cases were observed in patients from 66 to 70 years of age. Also, it was found that the patient is at more risk of death in case he/she is suffering from some underlying disease or any secondary infection. Wang et al. [12] proposed a weekly supervised deep learning model for classifying and localizing the lesion in COVID-19 patients using the chest CT images of 540 patients. The model claims to be good in classification performance compared to other methods in the paper except for the human expert and the lesion localization was measured in terms of hit rate as well as region discovered. Rustam et al. [13] proposed a machine learning-based forecasting framework to predict the risk of COVID-19 on global data. The model has taken account of the daily statistics of past data to predict the future risk of the COVID-19 infection. The models like —Linear regression, LASSO regression, Support Vector Machine, and Exponential smoothing have been used in the paper to predict the parameters like— recovery rate, confirmed infected cases, the death rate in the future. In this paper [14], author(s) have analyzed healthcare system data and identified defects in the system using ELK stack. Shah et al. [15] have also used Elasticsearch and Kibana to propose a framework to analyze social media data. Social media being one of the major sources of big real-time data related to health, finance, politics, education, and many more fields, provides lots of opportunities to data science researchers to mine knowledge out of this data. In [16], the authors have proposed a mathematical model predicting the quarantine period based on a global dataset on coronavirus available on Worldometer [17]. The study shows the importance of quarantine to decrease the spread of coronavirus. In [18], authors have studied the coronavirus spread in the region of the US and analyzed coronavirus spread from the geographical angle. The geographical differences among the different regions of the US include factors like—population density, the neighboring states, etc. Clerkin et al. [19] studied the impact of cardiovascular disease in COVID-19 patients and vice versa. It was found in this study that there remains a major challenge in COVID-19 patients treating health issues like—heart transplantation, immunosuppression, donor selection, etc. In another interesting study by Le et al. [20], the research on vaccine development for COVID-19 is discussed. Because of the novel nature of this disease more collaborative and global support is needed to come up with some vaccine on this. As per the research done in his study the time estimated for the vaccine development to be completed is by early 2021. In [21], the authors have proposed a CovidGAN model based on Auxiliary Classifier Generative Adversarial Network (ACGAN), which generates synthetic chest X-ray images to detect COVID-19 patient. Jamshidi et al. [22] have discussed various deep learning-based approaches in dealing with COVID-19. These approaches are mostly based on machine learning methods like— regression, classification, clustering, feature engineering, etc. A more specific study on future forecasting of COVID-19 using supervised machine learning is done by Rustam et al. [13]. This paper utilizes the machine learning methods namely—linear regression, least absolute shrinkage and selection operator, support vector machine, and exponential smoothing to predict the COVID-19 statistics for the next 10 days.

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Fig. 2 The workflow of the methodology used

4 Methodology The methodology that has been adopted during the experimentation of this work consists of the following three steps: Step 1: Pushing the data to Logstash for data pre-processing. At first, the dataset is collected and stored in the local system. Then the Logstash is been used to preprocess the data. Logstash supports different input plugins so that it can understand that data and further parse it to output plugin. In between, a developer can use filters for intermediate data processing like—formatting dates, location to geocode conversion, and many others. In thisstudy the output plugin used is Elasticsearch. Step 2: Storing data to Elasticsearch. In this step, the aim is to setup Elasticsearch and store the data which was received as an output in Step 1. All data in Elasticsearch is stored in the document form only. After this step, the application is in a position to execute a query and analyze the results. Users can perform text analysis, search across clusters, different types of aggregation—metrics, buckets, pipeline, and many others. Step 3: Visualizing data using Kibana. In this step, Kibana will be set up for data visualization purposes. Big data looks very complex to understand but if some visualizations were created out of it, it will become very easy to understand to end users even without any technical skills. Kibana serves this purpose in a very interactive manner. Figure 2 shows the complete workflow of the methodology being used for the analysis of COVID-19 data throughout this work. There are four components in the figure—the first one is the data gathering component. The next one is data preprocessing component—Logstash and then there is Elasticsearch component where the pre-processed data is stored and lastly Kibana component for data visualization. The interaction among these components represents the steps that are explained above.

5 Experimental Evaluation Before discussing the experimental setup and results, let discuss the dataset been used in this study. Afterward, a detailed discussion about the experimental setup and the results obtained were mentioned.

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5.1 Dataset Description In this work, the two datasets have been used for experimental studies. The first dataset is of Jhon Hopkins University’s COVID-19 dataset [23], which consists of world statistics on coronavirus disease. The second dataset that hasbeen used here is another opensource dataset available on COVID-19 from Our World in Data [24] website. Both datasets provide global details on coronavirus disease, but some of the attributes are different. The differences have been figured out in both the datasets and then dataset aggregation is performed to provide only relevant information to end-users.

5.2 Experimental Setup and Results The experimentation is done on a case basis, various use cases have been figured out to study the COVID-19 data. Both the datasets are now stored in Elastic search and ready to visualize in Kibana. Below are the various cases explained with their detailed analysis. Case 1: The basic details on the total count of COVID-19 cases, deaths, and recovery. This visualization can be created on Kibana using simple metricvisualization. Metric visualizations help in displaying a single number to end-user. In Fig. 3 one can see the total number of COVID-19 cases in the first place, in the middle one can see the total number of recovered patients of COVID-19 and in the rightmost place, one can see the total deaths occurred due to coronavirus so far in the world. Case 2: Pie chart showing the top ten countries in descending order of total cases of COVID-19 in the World. This visualization can be used in case one wants to visualize the total cases of COVID-19 for the top ten countries in descending order. The number of countries is set to ten just for simple presentation, the user can simply customize this as per the project requirement. Also, one can observe the percentage distribution for each country. Figure 4, shows this visualization, in which the major highlight is that the U.S has the highest cases in the World.

Fig. 3 The basic statistics on COVID-19

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Fig. 4 Pie-chart showing total cases of COVID-19 in the World

Case 3: Pie chart showing the top ten countries in descending order of total death cases of COVID-19 in the World This visualization can be used in case one wants to visualize the total death cases of COVID-19 for the top ten countries in descending order. Also, the percentage distribution for each country as per the descending order of death cases in the World is shown in this use case. Figure 5, shows this distribution of the top ten countries as per the death numbers. The U.S again tops the chart with 32.9% of the total share in death numbers. Case 4: Pie chart showing the top ten countries in descending order of total recovered cases of COVID-19 in the World In this pie-chart visualization, each country is displayed w.r.t the number of recovered COVID-19 cases. Again, the top ten countries in the chart are kept for display just for the sake of simplicity, however, customization is always there in case the user

Fig. 5 Pie-chart showing total death cases of COVID-19 in the World

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wants to display more countries in the chart. In Fig. 6, visualization illustrates that the country that has the highest number of recoveries in COVID-19 is Brazil with 22.77% of share in the total, followed by the US with 20.99%. Figure 6 represents this visualization in detail. Case 5: Pie chart showing the top ten countries in descending order of total testing done of COVID-19 in the World In this pie chart visualization, the total testing done by different countries for COVID-19 has been presented. Again, for simplicity, only the top ten countries have been chosen to be displayed in the chart. Figure 5, shows this distribution of the top ten countries as per the testing numbers. The U.S again tops the chart with 39.34% of the total share in testing numbers (Fig. 7). Case 6: Analyzing COVID-19 total cases, deaths, and recovery in a single visualization

Fig. 6 Pie-chart showing total recovered cases of COVID-19 in the World

Fig. 7 Pie-chart showing total testing done of COVID-19 in the World

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This visualization can be created on Kibana using lens visualization. Here the aim is to try visualizing total deaths, recoveries, and cases worldwide in a single visualization. In this one can see a single bar for a country with multiple colors indicating three metrics w.r.t each country. Figure 8 displays a clear picture of this visualization. Case 7: Line chart visualization for total cases of COVID-19 in the World This visualization can be created on Kibana using simple line visualization. Line visualizations are very easy to understand, also it provides a nice comparison with other countries as shown in Fig. 9. The line for the U.S is far more above than India for the date—01-06-2020. In this, one can see each country progresses in an increase in the number of cases with time. Case 8: Line chart visualization for total deaths due to COVID-19 in the World

Fig. 8 World COVID-19 total cases, deaths, recoveries in a single visualization

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Fig. 9 Line chart visualization for total cases of COVID-19 in the World

In this case, the total deaths were analyzed with time for each country in the World. This visualization can be created on Kibana using simple line visualization again. The line for the U.S is far more above than other countries in the World for the date—01-06-2020. However, for past date say—24-043-2020, the cases in Iran were higher than U.S. Fig. 10 shows the details of this visualization. Case 9: Creating Complete Dashboard for all visualizations

Fig. 10 Line chart visualization for total deaths due to COVID-19 in the World

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In this case, a Kibana dashboard has been created to include all the visualization in one place. The user can visualize all the data in a single dashboard, also it is very much interactive. Clicking on charts and lines will provide the exact statistics of that country. Figure 11 shows the Kibana visualization dashboard for different statistics on coronavirus disease. Case 10: Forecast of COVID-19 cases for future analysis In this visualization, the machine learning feature of Kibana has been used to forecast the total cases in the future. A user can pick any country say—Italy and then the forecasting of total cases of Italy will be displayed in the visualization window. A line chart is shown in Fig. 12 shows the spikes in Italy’s total cases, as well as the yellow region, shows the future trend for total cases. Case 11: Forecast of COVID-19 deaths cases for future analysis

Fig. 11 Complete Dashboard for all visualizations

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Fig. 12 Forecast of COVID-19 cases in Italy

Fig. 13 Forecast of COVID-19 death cases in Italy

In this visualization, the machine learning feature of Kibana has been used to forecast the total death cases in the future. A user can pick any country say—Italy and then the forecasting of total death cases of Italy will be displayed in the visualization window. A line chart shown in Fig. 13 shows the spikes in Italy’s total death cases, as well as the yellow region, shows the future trend for total death cases. Case 12: Forecast of COVID-19 recovered cases for future analysis In this visualization, the machine learning feature of Kibana has been used to forecast the total recovered cases in the future. A user can pick any country for example here Italy has been chosen and then the forecasting of total recovered cases with the time of Italy will be displayed in the visualization window. A line chart is shown in Fig. 14 shows the spikes in Italy’s recovered cases, as well as the yellow region, shows the future trend for recovery in Italy. Similarly, a user can choose another country and analyze the forecast.

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Fig. 14 Forecast of COVID-19 recovered cases in Italy

6 Conclusion In this chapter, various cases on the COVID-19 dataset using the ELK stack has been explored. One can utilize the visualizations and the dashboard for analysis purposes. It could help different policymakers, analysts in decision making with it’s simple to understand format. The forecasting feature helps in analyzing future trends related to total cases, deaths, and recovery in COVID-19 for all affected countries. Different visualization dashboards can be implemented to different audiences as per the requirement of the project. The work in this chapter is limited to the dataset available. If more robust data is available in terms of a country’s healthcare services, geography, etc., then more insightful analysis can be done on COVID-19. In the future, further improvement in the dataset can be done and visualize this data for more informative purposes.

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Computational Intelligence Methods for the Diagnosis of COVID-19 Sarra Akermi, Subrata Sinha, Surabhi Johari, Sunil Jayant, and Anshul Nigam

Abstract COVID-19 is a global catastrophe affecting over 200 nations worldwide. The RT-PCR and antibody based detection assay are considered as a standard diagnostic method and are expensive, time-consuming with low sensitivity (40–80%) making it imperative to look for alternatives. COVID-19 patients display severe lung damage which can be spotted in chest X-ray and CT scan which can be analyzed by computational methods for confirmation of the disease. Apart from above techniques, we have discussed battery of computational intelligence tools like docking, molecular dynamics and quantum mechanics (QM), the novel biosensor based diagnostic tool has been developed. The biosensor involves hybridization of SARS-CoV-2 RNA with cDNA to form RNA (viral)–cDNA (probe) hybrid, with intercalation of transition metal Osmium-Ruthenium (II) redox probe that release electrons. The electrons generated by redox metal measured by respective electrodes and detect even miniscule quantities of viral-RNA in the sample. The computational methods support efficient measurement of interaction of redox probe with viral genetic material and intercalation of the Osmium-Ruthenium (II) redox probe between RNA-cDNA hybrid. The computational docking offers proof of concept with better sensitivity, speed and S. Akermi · S. Jayant (B) Annotation Analytics Pvt. Ltd., 36, Ward no-14, Biswa, Amarpura, Gurgaon 122001, India e-mail: [email protected] S. Akermi e-mail: [email protected] S. Sinha Centre for Biotechnology and Bioinformatics, Dibrugarh University, Dibrugarh, Assam, India e-mail: [email protected] S. Johari Institute of Management Studies (IMSUC), Ghaziabad, Uttar Pradesh, India e-mail: [email protected] A. Nigam (B) Amity University Mumbai, Mumbai - Pune Expressway, Bhatan Post, Somathne, Panvel, Mumbai, Maharashtra 410206, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 K. Raza (ed.), Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, Studies in Computational Intelligence 923, https://doi.org/10.1007/978-981-15-8534-0_11

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accuracy, by presenting stronger interaction of RNA–DNA hybrid with Osmium (II) redox probe as compared DNA-DNA hybrid in RT-PCR. Keywords RT-PCR · COVID-19 · SARS-CoV-2 · RNA–DNA hybrid · Molecular docking and dynamics and quantum mechanics (QM)

1 Introduction Corona family of viruses are known to cause mild respiratory diseases in humans [1]. However, a few of them are able to cause epidemics for example, severe acute respiratory syndrome coronavirus, abbreviated as SARS-CoV and the Middle East respiratory syndrome coronavirus, abbreviated as MERS-CoV [2]. It’s noteworthy that both viruses were jumped from their animal host to effect humans [3, 4]. Since, no medication was available these epidemics were controlled by quarantine practices [5]. December 2019 saw the surge of respiratory syndrome virus from China and is known as SARS-CoV 2 which resulted in pandemic across the world and was termed as COVID-19 [6], where CO is abbreviation for Corona, VI is abbreviation for Virus, D is abbreviation for Disease while 19 represent year 2019. The major issues with causative agent of COVID-19 is that although it is highly contagious and but doesn’t relay symptoms of infection in many of its carriers resulting in its massive spread across the globe [7]. By the time we are writing this manuscript the detected cases of COVID-19 has cross the twenty million mark with nearly million deaths and has resulted lockdown of billions to contain its spread. Thus it is imperative to develop low cost diagnostic kit capable of scanning the massive population. The diagnosis of COVID-19 forms its basis on epidemiological background, clinical manifestations tests and some secondary examinations like CT scan, nucleic acid detection, immunological test, that is, Point-of-care Testing (POCT) of IgM/IgG [8] and ELISA. However, most of the COVID-19 detection kits developed by using RT-PCR as base. The problem arises when it is used without auxiliary examinations and present a troublesome accuracy range of 30–80% with false negatives [9–11]. The standalone RT-PCR, with or without auxiliary examination methods requires expensive instrumentation and highly skilled trained personnel to handle the process [12]. Here, we present the detailed information of computational intelligence tools for quick and efficient diagnostic of COVID-19. We also describe a case study of using computational approach to design an ultra-low cost portable (palm size device) electrochemical based method, which is instant and have high accuracy by using the Electrode DNA Chip and based on principle of electrical conductance of DNA/RNA hybrid.

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2 In-silico Methods for COVID-19 Diagnosis 2.1 Application of Artificial Intelligence (AI) for the Diagnostic of COVID-19 AI and application of machine learning are widely applied in the medical industry from diagnostic to treatment [13]. Therefore, it was thought that it can be used to track COVID-19 spread in the population which can be done by faster diagnosis. This will be helpful in controlling the diseases. Recently, it has been established that AI based prediction of COVID-19 is very precise and is able to differentiate the lung damage due to it and other pulmonary diseases [14–16]. Several reports have been published which show the impact of AI in reducing the time of detection and also making the process more automatic. It supports health care people for making faster decision in a very cost effective way. AI helps to develop novel diagnostic management system for the COVID-19, via various algorithms. The combination of advanced medical imaging technologies like Computed tomography (CT), Magnetic resonance imaging (MRI) scan along with the AI help’s in quicker diagnosis of the infected patients. AI can be helpful in keeping check on COVID-19 in following ways (Fig. 1): (i) Monitoring the treatment with Automatic process (ii) Quick identification of the hotspots and tracing of the individuals. (iii) Finding precise number of COVID-19 cases and their future projection. It will help in tracing and controlling the mortality rate (iv) Drug and vaccine discovery for COVID-19 (v) Prevention of the disease.

2.2 Deep Neural Network Highly contagious nature of SARS-CoV-2 has led to COVID-19 spread across borders. Laboratory based testing systems were not only expensive but were time consuming also. Further, the availability and efficacy of testing kits was also a major issue. Therefore, researcher sought for alternatives which included computational analysis of chest X-ray, radiography and CT scan. Ozturk et al., used deep neural networks to analyzed X-rays using advanced technology of deep neural networks algorithm and was able to identify COVID-19 with 98% accuracy [10]. While Wang and Wong described the use of convolutional neural network design for identifying COVID-19 patients using chest radiography images COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. Similarly, Yang et al. applied Densely Connected Convolutional Networks (DenseNet) for diagnosis of COVID-19 on high resolution computed tomography (HRCT) with efficacy of 98%. The major short coming of these detection techniques is that they can’t be used for identification of COVID-19 at initial

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Fig. 1 AI and non-AI process flow. Adapted from Raju et al. [12]

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Fig. 2 Model flow of dark Covid net. Adapted from Ozturk et al. [10]

stage [17] as they can detect damaged lungs but major advantage is their higher efficacy in differentiating lung damage due to COVID-19 and other diseases [18] like tuberculosis and pneumonia. The model can be access at https://github.com/ muhammedtalo/COVID-19 (Fig. 2).

2.3 UniqueKMER: A Sequence Based Diagnostic Tool for COVID-19 UniqueKMER is a sequence based toolset where short-read and laong-read sequences have been used for rapid identification of COVID-19. The basic principle is of using k-mer technology for mapping and extension of the genes. UniqueKMER tool has power to generate and ensemble sets of unique k-mers for each genome from the COVID-19 genome. The used the running platform known as fastv. The fastv require FASTQ data as input and export the output in the form of HTML and JSON formats. The pre-processing of the sequences have been performed to find good accuracy for k-mer analysis. It provide a useful support for fast track identification of the SARS-CoV-2 infection. It has been proven by experimental methods that it has cent percent sensitivity and specificity for identification of SARS-CoV-2 from sequencing data. This method can easily pin-point SARS-CoV-2 and not other corona viruses [19]. The online version of fastv can be access at https://github.com/OpenGene/fastv (Fig. 3).

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Fig. 3 Graphical representation of fastv process flow. Adapted from Chen et al. [19]

2.4 Image Segmentation Using the Artificial Intelligence (AI) The implementation of AI based technology enhance the diagnosis of the COVID19 disease in the patients. The speedy detection of the patients are always needed due to rapid increasing in the number of the patients. The most tradition technique such as combination of X-ray and Computational tomography (CT) are now highly recommended and used in the early detection of COVID-19 [11]. The system of Xray and CT workflow have been automated for making more accurate prediction of COVID-19 pandemic. In the work flow, the health care expert evaluate the positioning of the patients by first emanation of head than the feet. Further, the expert examine the supine than prone in CT [11]. These help to adjust accurate identification and setting the pose of the between the X ray machine and the patient. The automated approach using the AI technology reduce the change of error and easy and minimize the risk of viral exposure (Fig. 4).

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Fig. 4 a Automated imaging with CT and X-ray using the AI technology; b image capturing; c positioning of patient. Adapted from Feng et al.[11]

3 Case Study: Development of Low Cost Electrochemical Based Method Using Electrode DNA Chip to Detect COVID-19 RNA: For Early Stage Detection of Disease 3.1 Introduction Many members of corona family cause mild respiratory diseases [1]. But some of them are able to cause epidemic like severe acute respiratory syndrome coronavirus (SARS-CoV) and the Middle East respiratory syndrome coronavirus (MERS-CoV) [2]. It is important to note that both of these viruses jumped from their animal host to effect humans [3, 4]. Since, no medication was available these epidemics were controlled by traditional methods involving quarantine practices [20]. December 2019 saw the surge of respiratory syndrome virus from China which resulted in pandemic across the world and was termed as COVID-19 [6]. The major issues with COVID-19 are that although it is highly contagious and but doesn’t relay symptoms of infection in many of its carriers resulting in its massive spread across the globe [12]. By the time we are writing this proposal the detected cases of COVID-19 has cross the 20 million mark with nearly a million dead and has resulted lockdown of billions to contain its spread. Thus making it imperative for development of low cost diagnostic kit for scanning of masses. Till date all the kits developed for detection of COVID-19 RT-PCR based kits, which although have high accuracy but needs expensive instrumentation and trained personnel to handle the process [12]. We propose ultra-low cost portable (palm size device) electrochemical based method using the Electrode DNA Chip and based on

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principle of electrical conductance of DNA/RNA hybrid. The proposed method is modification of the method developed earlier [21].

3.2 Materials and Method Concept Note: DNA Biosensor is a device for the detection of an analyte i.e. redox probe that combines a biological component i.e. cDNA-RNA (Covid-19 genetic material) in our case with a detector (electrodes). Biosensors Components (Fig. 5): • Biological element-probe cDNA solution • Transducer • Detector element (optical or electrochemical). An ideal DNA detection method must be portable, rapid, sensitive and quantitative. Currently, the viral DNA or RNA are detected in big laboratories with higher cost and also time consuming. Motivated by this clear need of portability, robustness and cost effective technique, the Amity University, Mumbai and Annotation Analytics Pvt. Ltd., Delhi jointly have been promise to develop Electrochemical based Commercial method using Electrode DNA chips and a non-optical improved DNA detection method and kit based on the electrochemical monitoring of a DNA intercalating

Fig. 5 DNA biosensor

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redox probes such as Osmium/Ruthenium that have very high affinity for the probe cDNA-RNA (COVID-19) hybrid. Here, we proposed a proof of concept and innovative idea to detect the probe cDNA-RNA (covid-19) hybrid concern with the Respiratory disease spreaded by Covid-19 viral infection. This virus use RNA as genetic material for replication and therefore it is very important to develop a very affordable and serial monitoring method has been needed to detect these infections a right time in order to provide quick treatment.

3.3 Objectives Development of DNA-RNA hybrid Detection Kit based on electrochemical method of DNA Biosensor by application of experimental and rational approach via finding new organometallic redox molecules (Osmium/Ruthenium) with higher affinity for double-strand cDNA-RNA hybrid. For earlier detection of infections COVID-19 Virus:

3.3.1

Sub Objectives

1. Proof of concept with binding analysis between DNA-DNA and DNA-RNA hybrid with Osmium probe. 2. Development of cheap and domestic cDNA-RNA hybrid Detection Kit based on electrochemical method of electrode DNA Chip for early covid-19 infection detection. 3. Domestic DNA-RNA hybrid Detection Kit based on electrochemical method. 4. Molecular Docking between DNA-DNA and DNA-RNA hybrid with Osmium probe.

3.4 Designing Structure of the Genetic Material The 3D structure of DNA-DNA and DNA-RNA hybrid was designed by Discovery studio software (Fig. 6). The osmium probe structure was obtained by Guess view software and docked with the genetic material hybrids by Autodock docking software.

216 Fig. 6 DNA-DNA and DNA-RNA hybrids

Fig. 7 Steps for our electrochemical DNA biosensor

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3.5 Research Methodology and Approach The overall research plan is divided into several steps.

3.5.1

Our Electrochemical DNA Biosensor (Fig. 7)

Here, we propose a novel inexpensive, simple, sensitive and easy-to-handle method for electrochemically detecting a DNA-RNA hybrid using a unique combination of compounds as a mixed intercalator and a detection kit using same. Our technique can detect in small drop solution. Slow mixing of the reactant solution in the range of 1–10 µL in the reaction chamber (15–50 µL) will also enhance the detection rate. There are important components and steps.

3.5.2

Commercial Electrode DNA Chips

This is a platform for ssDNA probe with bound organometallic intercalating redox probes such as Osmium/Ruthenium that have very high affinity for the double-strand cDNA-RNA. Step 1: Electrode DNA chips: It will have carbon electrode platform for single strand probe cDNA (SScDNA) (Specific to sample COVID-19 RNA). This cDNA-RNA hybrid will bind to Osmium/Ruthenium chemical probe. Step 2: Sample COVID-19 RNA from SARS covid-19 virus in nasal sample will be injected to bind with the sscDNA and also added bounded intercalater. Note that reaction between the probe cDNA and sample COVID-19 RNA can be enhanced by shaking or mixing over the surface of a non moving working electrode. Step 3: The cDNA (probe)-RNA (from sample covid-19 virus) make dsDNA-RNA hybrid where organometallic redox probes such as Osmium/Ruthenium compound make intercalating complex with hybrid (Fig. 8). Step 4: Further, redox probe undergo oxidation and reduction process while the analyzer records the ensuing transient faradaic current related to the transformation of one of the components. Step 5: The signal generation will utilize the concept of oxidation and reduction reaction of the redox probe which will generate the faradaic current and the difference of the current with and without the presence of DNA-RNA hybrid further detected by the analyser based on the method of Cyclic voltammetry (CV) and chrono-amperometry (CA). This research leads to (i) a better understanding of the interactions between DNARNA and small organometallic molecules, (ii) the development of Commercial Electrode DNA chips based on redox probes using Osmium/Ruthenium metals which are capable to detect target covid-19 RNA with a significantly higher efficiency

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Fig. 8 Oxidation and reduction reaction of the redox probe

and specificity. (iii) Development of DNA-RNA hybrid Detection Kit based on electrochemical method using these Commercial Electrode DNA chips. This setup is able to detect the infections covid-19 RNA sample and can able to monitor the amount of COVID infection in the population with affordable price. The aim of the project is to combine a systematic electrochemical experimental methodology and a theoretical methodology (molecular dynamics and QM/MM) to improve the efficiency of electrochemical DNA biosensor using Commercially available Electrode DNA Chips with Osmium/Ruthenium redox probes in order to develop very sensitive, fast and cost effective commercial DNA-RNA hybrid detection kit which will reduce the detection time and will produce the accurate results.

3.6 Outputs In the molecular docking proof of concept, we have performed molecular docking between DNA-DNA and DNA-RNA hybrid with the osmium probe. Osmium probe formed strong complex with DNA-RNA hybrid with docking energy of −11.67 kcal/mole as compare to DNA-DNA hybrid (Fig. 9). Indigenously developed DNA-RNA hybrid detection kit for early stage, quick and fast detection of COVID-19 infection in population. Affordable, simple and easy to implement DNA diagnostic device that can be used by large population. It is important to note that electrode chips are cheap and disposable.

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Fig. 9 Docking complex between DNA-DNA and DNA-RNA hybrids with osmium probe

3.7 Commercial Product Commercial Electrode DNA chip incorporates cDNA solution, Osmium/Ruthenium redox probes (or as solution bottles), carbon electrode surface which can easily detect the target sample RNA from nasal samples when connected to analyser (Fig. 10).

3.8 Technology Transfer Product This modified version of technology will be transfer to big molecular biology machine such as to make electrochemical PCR for detection of real time DNA amplification. Novel DNA detection methodology using Osmium/Ruthenium Probes is inexpensive, simple, sensitive and easy-to-handle. This Osmium/Ruthenium Probe based technology can be incorporated into improving efficiency of DNA biosensors.

3.9 Applications Can be used in DNA diagnostic lab in the form of disposable electrode DNA chips method for detecting the diseases related DNA such as bacterial, viral or fungal DNA with low cost and more accuracy. Can be implemented in real time electrochemical

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Fig. 10 DNA detection kit incorporating the commercial electrode DNA chips method for fast and simple detection of covid-19 RNA

PCR for online detection of progress of DNA amplification with low cost and more sensitivity.

3.9.1

Commercial Viability

Novel electrochemical based Commercial Electrode DNA chips with Osmium/Ruthenium redox probes for DNA detection kit will be inexpensive, simple, sensitive and easy-to-handle. It will be cost-effective for commercial production scale, which in turn open the door for moving ahead from laboratory setting. Using Commercial Electrode DNA chips and methodology, the small amount of DNA or hybrid sample (10–30 µl) can be detected speedily and with higher accuracy. Electrode DNA chips with layered solution of Osmium/Ruthenium Probes are smaller in size and can be taken to any places. Probe based methodology can able to easily discriminate between different sequences of DNA (GC or AT rich) or DNARNA hybrid based on their predicted binding affinity constant. As probe can bind with any DNA or RNA, therefore our Commercial Electrode DNA chips and detection kit can detect DNA or RNA or their hybrid from all verities of pathogens such as bacteria, virus, fungal present in blood sample, food or in soil. Pre-selection of probes via computational modeling (Molecular Dynamics and QM/MM) opens the door for pharma industry as service provider for designing probe for DNA detection kit.

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3.10 Market Potential and Competitive Advantage Over Existing Technologies for Electrode Based COVID-19 Detection Kit 3.10.1

Advantages

When combined the PCR process with a electrochemical based detection method using Osmium/Ruthenium organometallic redox probes based electrode, It will significantly increase the efficiency of the PCR detection of the DNA amplification than other probes like Methylene blue. So, again decrease the market price for DNA detection. Osmium/Ruthenium metal based DNA chips will do fast detection of sample covid-19 RNA as compare to current marketed RT PCR because of fast oxidation and reduction properties. Electrochemical techniques offer significant edge over optical methods because of being less prone to interference and hence better suited for one step target detection method using blood and other grossly complex samples. The integrated system could achieve rapid diagnosis of bacterial-viral-fungal infections and serve as an accurate POC device as compared to PCR method.

3.10.2

Market Potentials

The Indian diagnostic services industry is expected to witness a staggering growth of 27.5% for coming five years, as per Indian Diagnostic Services Market Outlook 2020. The growth estimate has been credited to improved medical diagnostic and pathological labs, better healthcare facilities, multiple private–public projects, and the growing health insurance sector. By 2025, the market is expected to reach INR 860 Billion, with rise of societal health consciousness and rising chronic diseases burden. Currently, one covid-19 test cost minimum $100 in the DNA diagnostic laboratory. However, DNA detection kit make it possible to perform covid-19 test at very affordable price of $10. As Commercial Electrode DNA chips and DNA detection kit, both are commercial and marketable. So, it is creating a big market potential for generating high revenue with the growing diagnostic services market. Providing Commercial Parameters Database is a novel idea where all the MD software are dependent of compounds parameters. Therefore, this database is also able to make good price. In this, we will be first to develop AMBER/GROMACS Molecular dynamics Topology parameters database of complex organometallic compounds such as osmium and ruthenium compounds and precise methodology to parameterize these compounds for molecular dynamics software. Parameterization service can be marketed to pharma industries with affordable market price. This will create good market penetration of our database. Commercial electrode DNA chips technology can be useful for research institutions and companies which are working in the field of Diagnostic Services.

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4 Conclusion Computational technology is beyond doubt is able to enhance the accuracy and detection of the COVID-19 disease. Rapid increases in the number of cases needed a fast and robust technique which can detect the disease in few minutes to cover large number of patients and also with less false negative or positive results. Computational methods evolved in amalgamation with radiography are already used in the health care sector for diagnosis of the COVID-19 disease. The major short coming of these detection techniques is that they can’t be used for early detection of COVID-19 as they can detect damaged lungs but major advantage is their higher efficacy in differentiating lung damage due to COVID-19 and other diseases. Further, our case study revealed that the digital improvement in the RT-PCR level for diagnosis of the SARS-CoV-2 infection where the virus infection can be detected in nano-drop solution and also excludes use of use of enzymes like in RT-PCR methods there using osmium probe based methods in the form of electrochemical PCR reduces the time and cost of COVID-19 disease identification. The electrochemical PCR technology is associated with a limitation of using best metal probe which will be very sensitive and able to generate electron to detection the genetic material. Our case study will give future insight about detection of other microbial infection.

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10. Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O., Rajendra Acharya, U. (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine,121, 103792 (2020). 11. Shi, F., et al. (2020). Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering, 2020, 1–1. 12. Curti, L., Pereyra, B., Federico, & Gimenez, C. (2020). An ultrasensitive, rapid, and portable coronavirus SARS-CoV-2 sequence detection method based on CRISPR-Cas12. https://doi. org/10.1101/2020.02.29.971127. 13. Qazi, S., & Raza, K. (2020). Smart biosensors for an efficient point of care (PoC) health management. In J. Chaki, N. Dey, D. De (Eds.), Advances in ubiquitous sensing applications for healthcare, smart biosensors in medical care (pp. 65–85). Academic Press. 14. Raju, V., Mohd, J., Ibrahim, H. K., Abid, H. (2020). Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337–339. 15. Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., et al. (2020). Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases. Radiology. https://doi.org/10.1148/radiol.2020200642 16. Luo, H., Tang, Q. L., Shang, Y. X., Liang, S. B., Yang, M., Robinson, N., & Liu, J. P. (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. Chinese Journal of Integrative Medicine. https://doi.org/10.1007/s11655-020-3192-6 17. Wong, H. Y. F., Lam, H. Y. S., Fong, A. H.-T., Leung, S. T., Chin, T. W.-Y., Lo, C. S. Y., et al. (2020). Frequency and distribution of chest radiographic findings in COVID-19 positive patients. Radiology, 201160. 18. Cohen, J. P., Morrison, P., & Dao, L. (2020). COVID-19 image data collection, arXiv:2003. 11597. 19. Chen, S., He, C., Li, Y., Li, Z., & Melancon, C. (2020). A computational toolset for rapid identification of SARS-CoV-2, other viruses, and microorganisms from sequencing data. https:// doi.org/10.1101/2020.05.12.092163. 20. Markus, H., Hannah, K. W., Simon, S., Nadine, K., Tanja, H., Sandra, E., Tobias, S. S., Georg, H., Nai, H. W., Andreas, N., Marcel, A. M., Christian, D., & Stefan, P. (2020). SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor. Cell. 21. Moreau, M., Delile, S., Sharma, A., Fave, C., Perrier, A., Limoges, B., & Marchal, D. (2017). Detection of a few DNA copies by real-time electrochemical polymerase chain reaction. The Analyst, 142. https://doi.org/10.1039/C7AN00978J.

Rapid Computer Diagnosis for the Deadly Zoonotic COVID-19 Infection Peter Mudiaga Etaware

Abstract The life cycle of SARS-CoV-2 is complexly linked with that of its host, thereby, rendering all prospective treatments ineffective. Recently, there was a drift from Cross-species transmission (Zoonosis) → Intra-species → Nosocomial transmission, thereby, increasing the risk of infection. In consortium with WHO, rapid computer diagnosis (RCD) was exigent, as it will increase the chances of identification of suspected cases and minimize false-positive diagnosis. EtawareCDT-2020 RCD Model “Y = α + β1 X1 + β2 X2 + β3 X3 + … β26 X26 ” was developed using broad-spectra symptoms catalogue for COVID-19. The best-fit model was adjudged by R2 , R-SqAdj , AIC, BIC, MSEPred. , MAE, LOO_Press, LOOPreR2 , LOOMAE, LGO_Press, LGOPreR2 , LGO-MAE etc., validated by bootstrapping and trial diagnosis. The R2 and R-SqAdj values were positive (1.00 and 1.00, respectively), while AIC and BIC values were negligible (−3677.10 and −3659.60, respectively). The mean error of diagnosis was least in Hubei cases (11.1), while the standard error of diagnosis was insignificant in confirmed cases outside Hubei (2.0), and those linked (or not) to Wuhan (2.0). The similarity index of diagnosis (R and R2 ) was best-fit in Hubei cases (0.78 and 0.49, respectively). Etaware-CDT-2020 is a better alternative for COVID-19 diagnosis and it is very easy to setup. It can be utilized in hospitals, clinics, homes, offices, and public places with ease. Keywords Zoonosis · Nosocomial transmission · Etaware-CDT-2020 · rRT-PCR · SARS-CoV-2 · Coronavirus disease 2019

Electronic supplementary material The online version of this chapter (https://doi.org/10.1007/978-981-15-8534-0_12) contains supplementary material, which is available to authorized users. P. M. Etaware (B) Department of Botany, Faculty of Science, University of Ibadan, Ibadan, Oyo State, Nigeria e-mail: [email protected]; [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 K. Raza (ed.), Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, Studies in Computational Intelligence 923, https://doi.org/10.1007/978-981-15-8534-0_12

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Abbreviations RCD BSL-2 RIT COVID-19 SARS-CoV-2 WHO rRT-PCR Etaware-CDT-2020 SCD-Boundary ACR

Rapid Computer Diagnosis Biosafety Level 2 Rapid Immunodiagnostic Test Coronavirus Disease 2019 Severe Acute Respiratory Syndrome Coronavirus 2 World Health Organization Real time Reverse Transcription Polymerase Chain Reaction Etaware Computer Diagnostic Tool 2020 Suspect Case Definition Boundary for COVID-19 Infection Actual Clinical Report

1 Introduction The novel “Coronavirus disease 2019” is a serious threat to global health security due to its high tendency for rapid host adaptation [6], immense affinity for mutation (Genome recombination) and viral evolution [11], high transmissibility index through a vast array of zoonotic, nosocomial, symptomatic or asymptomatic carrier channels [7, 17, 30, 31], diverse human infection pathways during the viremic or latent infection phase [2, 8, 16] and a geometric increase of outbreak of new cases of infection worldwide [10]. Despite the increasing number of confirmed cases, there is still a dearth of information from clinical investigation of patients conducted worldwide [30, 31]. This is a major setback to the development of rapid immunodiagnostic test (RIT) kits, production of effective broad-spectrum vaccines or clinical therapies for treatment of COVID-19 infection, and downturn on the update of fundamental knowledge of viral dynamics, physiology, host affinity, genome mutation and evolution, and DNA/RNA recombination of SARS-CoV-2 [7, 8]. In response to the increasing global threat of the pandemic COVID-19 infection, compounded by the absence or scarcity of BSL-2 Standard facilities worldwide, the lack of ultra-modern molecular equipment for virological investigations, a shortfall in supply of reagents or relevant primers for amplification of test samples for optimal diagnosis, and the global unavailability of medical personnel (Experts in Virology) trained or specialized in handling pandemic situations; multiple diagnostic test manufacturers have developed and commercialized the marketing of new easy-to-use test kits to enhance rapid diagnosis of COVID-19 infection in clinics, hospitals and other isolation centres [28]. These simple immune diagnostic test kits are based on detection of proteins from SARS-CoV-2 (Antigens) in respiratory samples (e.g. sputum, throat swab) or “Antibodies” in blood or serum samples of patients responding to infection [20, 21, 25, 33]. WHO applaud the efforts of these developers and manufacturers for their astuteness and rapid response to the rising need of qualitative and quantitative diagnostic tool for COVID-19 infection [28].

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The limitations to the use of some of these RIT kits include but not limited to the following facts: the antigen(s) detected are expressed only when the virus is actively replicating, therefore, such tests are best used to identify acute or early COVID-19 infections [28]. The accuracy of the tests conducted using this kind of RIT kit depend on several factors such as the duration of illness or infection stage, the concentration of virus in the specimen, and the precise formulation of the reagents in the test kits. The sensitivity of these tests kits vary from 34 to 80% [1]. Based on this information, most COVID-19 infected patients might escape detection or uninfected persons will be diagnosed with false-positive results if the antibodies on the test strip also recognize antigens of viruses other than COVID-19 i.e. other α- or β-coronaviruses that cause similar respiratory or febrile illnesses [28]. Little or no antibody response had been reported in some patients with COVID-19 infection, some had late production of antibodies while others respond spontaneously and proportionately to the presence of SARS-CoV-2; however, the disease diagnosis for all the aforementioned cases was confirmed by molecular diagnostic tool (real-time reverse transcription polymerase chain reaction “rRT-PCR”) [19, 24, 34]. Recent researches conducted worldwide showed that most patients develop antibody response to COVID-19 infection at week two when symptoms are manifested [20– 22, 24, 29, 32–34]. This is an indication that a confirmatory diagnosis of COVID-19 infection hinged on antibody response will only be feasible or even possible at the recovery phase, sadly, the chances or possibilities of clinical or medical intervention or interruption of disease transmission might have already elapsed. Test kits developed using antibody detection mechanism may also possess the tendency to react to the presence of other similar pathogens e.g. other human coronaviruses leading to a false-positive diagnosis [5, 24, 26]. Currently, the chaotic spread of the pandemic coronavirus disease 2019 is now a major threat to human health, global economic and financial growth. In an attempt to cushion the effects of this disease, the development of “Etaware-CDT-2020” (A novel computerized rapid diagnostic tool) was imminent. The implementation of Etaware-CDT-2020 in conjunction with other RITs could help reduce the level of False-Positive results generated from RIT kits, while maintaining high detection speed, proficiency and quality. Etaware-CDT-2020 was carefully designed from an all-inclusive list of symptoms associated with COVID-19 infection. The computerized diagnostic model was validated using primary and secondary clinical information. The novel Computerized COVID-19 diagnostic tool “Etaware-CDT-2020” will further increase the chances of rapid detection, isolation and quarantine of infected patients. The use of rapid qualitative diagnostic test can help limit the spread of COVID-19 disease and ensure global safety of lives. WHO encourages the sharing of data to better understand and further manage COVID-19 outbreaks, and to develop countermeasures for combating the disease [27].

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2 Methodology 2.1 Symptoms Catalogue According to WHO [27], the decision to diagnose COVID-19 infection should be based on clinical symptoms or other known epidemiological factors associated with the disease development irrespective of time or stage of infection, this could be linked to the likelihood of infection based on the already established clinical assessment. The computerized diagnostic model “Etaware-CDT-2020” was carefully designed from a comprehensive list of symptoms associated with COVID-19 infection [3, 4, 6, 12–15, 19], MAYO [23, 25, 30, 31]. The symptoms were categorized thus: Prominent Symptoms • Fever • Dry or Chesty Cough • Fatigue or Tiredness. General Symptoms • • • • • • • • • • • • • • • • •

Shortness of breath Muscular aches and pains (Myalgia) Chill or Shivering Sore throat Headache Diarrhoea Vomiting or Nausea Drowsiness Loss of appetite Loss of sense of smell Loss of sense of taste Nasal congestion Abdominal pain Chest pain Neurological illness Gastrointestinal illness Dizziness Specific Symptoms

• • • • • •

Stroke Pneumonia High body temperature Rhinorrhoea (Runny nose) Body rash Conjunctivitis

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2.2 E-clinical Library WHO advised that all suspected patients should be tested for the presence of other pathogens that causes respiratory diseases alongside COVID-19 to avoid false alarm [27]. The clinical diagnostic features investigated and programmed into EtawareCDT-2020 rapid computer diagnostic model are: • • • •

The probability of COVID-19 infection Respiratory diseases Gastrointestinal disorders E-Clinical Diagnosis of COVID-19 infection (Table 1 in Chap. 12).

2.3 E-clinical Assessment Etaware-CDT-2020 is an easy to use diagnostic model with the E-Clinical questionnaire formulated to give maximum coverage of all the symptoms associated with COVID-19 infection (Fig. 1). No medical expertise is required to collect data for diagnosis.

2.4 Data Simulation (Clinical Data/Medical Report) According to WHO [27] all screening protocols should be adapted to the local situation i.e. clinical symptoms or epidemiological factors associated with familial, communal, local or national clusters of human population, as the suspect case definitions are regularly reviewed and updated as new information becomes available. Therefore, Etaware-CDT-2020 was carefully developed from computerized data using the appropriate screening protocols from a collection of universal clinical symptoms and global epidemiological factors of 120 test patients covering all categories and levels of COVID-19 severity indices (Supplementary File 1). Note: Supplementary File 1 contains the computer simulated data for one hundred and twenty (120) candidates, some of which were healthy; others were affected by mild to severe respiratory or gastrointestinal diseases, and some infected by COVID19 disease. This is the primary source of data used for development of the rapid computer diagnostic model (Etaware-CDT-2020).

2.5 Data Analysis Qualitative and quantitative data generated by simulation or directly collected from documented clinical/medical reports of COVID-19 infected patients from China,

70–100

60–69

50–59

40–49

30–39

20–29

10–19

01–09

5–10 µm in diameter of an infected person, these droplets

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travel quickly and reach the lung. The virus can be spread by touching the contaminated surface or an object and then touching their own mouth, nose and eyes [5]. The key presenting clinical features of COVID-19 are acute respiratory illness (e.g., coughing, sneezing or talking, shortness of breath, difficulty breathing, dyspnoea) fever (temp. over 37.5 °C), anorexia headache, fatigue or myalgia [3]. The median incubation period or the time has been recorded for novel coronavirus is generally four days to up to two weeks by WHO [2].

4 Features and Mechanism of Human COVID-19 The novel coronavirus (SARS-CoV-2) is a spherical particle. The SARS-CoV-2 represents the 79.6% identical feature to other coronaviruses genomes. It encodes four structure genes namely, envelope, membrane and nucleocapsid and spike(s) formation. The spikes glycoprotein of coronavirus binds to specific receptors named human angiotensin-converting enzyme 2 (hACE2) through its rigid binding domain (RBD) on the cell surface (Fig. 2) [8, 9]. This spike protein contains a 3D structure and maintains the Vander Waal forces. The COVID-19 spike(s) promotes virus entry into the host cell surface and cell fusion. This virus recognizes the receptors from a different domain. The mechanism of COVID-19 entry depends upon the cellular proteases including, cathepsins, human airway trypsin-like protease, and transmembrane protease serine 2 that split the spike protein and establish further penetration changes. The COVID-19 also expressed in the other membrane proteins such as RNA polymerase, papain-like protease, helicase, polyproteins, nucleoproteins and accessory proteins [10, 11]. Fig. 2 Schematic representation of mechanism of COVID-19

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5 Repurposing Drugs: Used for COVID-19 Treatment Drug repositioning (also known as drug repurposing redirecting and reprofiling) plays an important role in drug development at a very reduced cost. It is a promising and effective tool for finding new indication for existing drugs that helps as a crucial way to hasten the drug development. Researchers are interested in dealing with the detection and development of new drugs from the old drug in the different phases of the clinical trial. Drug repurposing consists of four stages; compound identification, compound acquisition, clinical testing and post-marketing safety of drugs and therapeutic biologics, whereas the computational drug designing and discovery initiated with discovery and development, preclinical testing, clinical testing, Food and Drug Administration (FDA) reviews and post-marketing safety of drugs and therapeutic biologics [12]. The World Health Organization initiated an international clinical trial platform entitled—”Solidarity” to determine the best and more effective treatment against COVID-19. This initiative has been developed to achieve a point of care healthcare management for coronavirus patients. This clinical trial aims to identify novel drugs that can be put in immediately globally. Currently, there are many reports of drug candidates such as—chloroquine, remdesivir, ritonavir, etc. are in practice for COVID-19 (Fig. 3). Drugs presently subjected to the use of clinical trials and are being tested against the novel coronavirus (nCoV-19) are presented below [13].

5.1 Antiviral and Antiretroviral Drugs (a) Remdesivir An antiviral drug administered intravenously has been employed for viruses such as—coronaviruses, Ebola etc. It has been discerned as effective against SARS-CoV2 in mice infected with the novel coronavirus (nCoV-19). It blocks the potentially essential viral targets preventing them from replicating. Remdesivir has been tested on some patients enduring novel coronavirus in the US, howbeit; large clinical trials are still required to validate its efficacy[14]. Remdesivir was granted emergency use drug authorization by food and Drug Administration on 1st May 2020. (b) Favipiravir Favipiravir drug known as Avigan is one of the finest drugs for the avian or novel influenza treatment. This drug has been helpful used to treat infectious diseases. It has been developed by Fujifilm Toyama Chemical, Japan in 2014 and also being recommended by the Council of Scientific and Industrial Research (CSIR), India. The drug favipiravir first enters the cytoplasm by endocytosis in the infected cells. Then, it transformed into the active favipiravir ribofuranosyl phosphates through phosphoribosylation and phosphorylation. It inhibits the RNA polymerase, responsible for the intracellular sub-genomic RNA production. Currently, this drug is using for clinical trial studies for COVID-19[15].

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Fig. 3 Schematic Representation of repurposing Drugs using for COVID-19: 3D structure of SARS-CoV-2 3CL protease (PDB ID: 6M2N) using PyMoL version 2.3.4 in the centre and repurposing drugs

(c) Lopinavir/ritonavir It has been utilized to treat HIV and this drug combination functions the same way as remdesivir by inhibiting the viral targets, namely—“proteases.” This combination has also been observed to be effective towards mice infected with SARS-CoV2[16]. This combination is currently under clinical trials against COVID-19. (d) Umifenovir Umifenovir is mainly a hydrophobic molecule that interacts with both the lipids and the proteins and plays a vital role in inhibiting the viral replication. It is a drug, primarily used only in Russia and China, to treat the influenza virus; however, this drug still needs to be tested and validated against the novel coronavirus. Studies suggest that umifenovir is better than the combination drug lopinavir/ritonavir at reducing viral loads in patients [17].

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(e) Nitazoxanide It has been deployed against parasitic, bacterial and viral infections and is a thiazolide compound that functions by inhibiting the nucleocapsid (N) protein of the MERSCoV infection[18]. The studies have been carried out in in vivo antiviral activity against the MERS-CoV and COVID-19.

5.2 Antimalarial Drugs (a) Chloroquine and hydroxychloroquine Numerous studies are reported and under process for antiviral activities against COVID-19. Both drugs chloroquine (CQ) and hydroxychloroquine (HCQ) have been identified as antimalarial and antirheumatic drugs[19] for the treatment of malaria and lupus, viz., an autoimmune disease. For decades, chloroquine was first used drug for prophylaxis and malaria treatment. It is the commonly prescribed drugs globally [20] which have been tested against various other infections as it holds the potential to inhibit the virus replication. Hydroxychloroquine, an analogue of chloroquine, reveals better antiviral activity [19]. HCQ has a better clinical safety profile for long-term use then chloroquine. Recently, some clinical studies suggested that the use of hydroxychloroquine (HCQ) alone or with azithromycin (AZT) could be beneficial for the coronavirus patients as it reduces the time to clinical recovery and viral shedding [21]. However, research is ongoing on whether it is effective against the novel coronavirus as well. (b) Ivermectin It is an ant parasitic medication and is lipophilic in nature. It binds to glutamate-gated chloride ion channels causing loss of cell shape and thus killing the parasite. As far as COVID-19 is concerned, it has been considered effective as it halts the major celltransport proteins that used to enter the nucleus, thus killing the virus replication eventually. Two clinical trials are underway, one on Indian patients infected with coronavirus and the second, on patients in Kentucky employed in combination with hydroxychloroquine[13, 22].

5.3 Anti-inflammatory Drugs (a) Thalidomide Thalidomide is an anti-inflammatory and immunomodulatory agent, designed to boost T cells to treat inflammation. It inhibits cell proliferation. The most successful reprofiling drug is thalidomide, repurposed from morning sickness to leprosy and multiple myeloma. The central role of thalidomide in COVID-19 is to protect the

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lungs injury caused by immunological reactions. A case report has reported from Wenzhou medical university, demonstrated that the thalidomide drug has an effective therapeutic drug for COVID-19 treatment [23]. (b) Famotidine Famotidine has also been examined as a potential drug against the deadliest coronavirus only after Micheal Callahan and his research group suggested its efficacy in Chinese patients infected with COVID-19. It is an H2 receptor and is used mainly against heartburn[13]. Famotidine, along with hydroxychloroquine, is tested in phase III trials for patients with COVID-19 in New York[13]. Howbeit, the actual functioning of famotidine is not clear yet.

5.4 Miscellaneous Agents (a) Azithromycin Azithromycin is an antibiotic and well-known antiviral activity by upregulating the interferon (IFN) pathway. It inhibits the growth of bacteria and treats acute bacterial infection. It has active against Ebola, Zika and Rhinoviruses. This antibiotic use to treat patients who suffer respiratory tract infections from viral infection [24]. Some studies suggest that a combination of azithromycin and hydroxychloroquine may have possible effects for fighting COVID-19, but there is inadequate data to show a better outcome[25]. (b) Ascorbic Acid (Vitamin C) The essential nutrient Vitamin C is well-known as antioxidant properties and antiviral agents against influenza viruses [19]. It affects the immune system. Ascorbic acid involves in the blood vessels formation, muscle, cartilage, and collagen in bone and is vital for the recovery process in COVID-19. It plays a vital role in reducing the inflammatory response and preventing the common cold. Ascorbic acid has recently added to the clinical trial registry (Clinical Trial ID: NCT04264533) https://clinicalt rials.gov/ct2/show/NCT04264533 to investigate vitamin C infusion for severe 2019nCoV treatment. (c) Tocilizumab and Sarilumab This combination of drugs is usually utilized for rheumatoid arthritis and systemic juvenile idiopathic arthritis patients, and are mainly recombinant, monoclonal antibody antagonists of humanized anti-human IL-6 receptor[26]. Nonetheless, the novel coronavirus has been shown to inflate the levels of IL-6. The trial outcomes of tocilizumab could be used as a potential drug against the novel coronavirus, but, sarilumab had a discrepant result where it was found effective for critical patients and didn’t work well enough for patients with severe infections[10, 27].

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(d) Anakinra Anakinra is a recombinant and slightly modified human IL-1 receptor antagonist (IL1RA) which has been used to treat rheumatic arthritis patients [19]. IL-1 family has associated with an innate immune response and inflammation. Anakinra is a highly reasonable drug for COVID-19 [27, 28].

6 Clinical Trial Studies Thousands of compounds are available in the literature with approved drugs and investigational drugs used in the clinical trial studies. Since January 2020, many potential drug candidates have been under clinical trials that are effective against the novel coronavirus (nCoV-19). Many drugs have been successfully recruited, while some have failed in the clinical trials. Table 1 shows some recruiting drugs used in Table 1 Recruiting COVID-19 drugs for clinical trials S. No.

Clinical trial ID

Drug

Phase

Actual study start date

Estimated completion date

1.

NCT04401579

Remdesivir + Baricitinib

III

08/05/20

01/08/23

2.

NCT04425538

Infliximab

II

01/06/20

31/12/20

3.

NCT04280705

Remdesivir

III

21/02/20

01/04/23

4.

NCT04358068

Hydroxychloroquine (HCQ) plus + Azithromycin (Azithro)

II

01/03/20

05/03/21

5.

NCT03891420

Galidesivir

I

09/04/20

31/05/21

6.

NCT04357782

l-ascorbic acid

I/II

16/04/20

01/08/20

7.

NCT04370834

Tocilizumab

II

28/05/20

01/04/22

8.

NCT04332991

Hydroxychloroquine

III

02/04/20

30/07/21

9.

NCT04252274

darunavir + cobicistat III

30/01/20

31/12/20

10.

NCT04255017

Umifenovir + oseltamivir + lopinavir/ritonavir

IV

01/02/20

01/07/20

11.

NCT04315948

Remdesivir + lopinavir/ritonavir + interferon beta 1a + hydroxychloroquine

III

22/03/20

31/03/23

13.

NCT04275245

Meplazumab

I/II

03/02/20

31/12/20

14.

NCT04364009

Anakinra + oSOC

III

27/04/20

24/10/20

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the different clinical trial phases from https://clinicaltrials.gov/ to fighting against the novel COVID-19.

7 Computational Intelligence and Machine Learning for Drug Repurposing Biological systems are complex and remarkable. Due to rapid advancement in artificial intelligence (AI) and computational intelligence (CI) methods, it has grown from industries to medical infrastructure and has the potential to reduce human intervention. Today, AI is employed in medicine, known as “Artificial Intelligence in Medicine (AIM)”, that encompasses an entirely different modus operand and varies from traditional artificial intelligence algorithms used in engineering and other industries. AIM strategies have been previously deployed for providing a better, efficient, point of care (PoC) healthcare to individuals [29]. With the advancement of ubiquitous healthcare, AIM has enabled the concept of internet of living things (IoLT) in healthcare [30]. AI-based systems are capable to predict the small molecules as potential therapeutics for disease target [31]. A lot of companies and research centres are gradually adopting AI and machine learning approaches to improve the drug discovery process. The computational intelligence methods, especially machine learning and deep learning, have been applied for molecular design (basic agents and their environments), modelling, drug interaction, simulation, and drug development. These can contribute to fighting this global COVID-19 outbreak, help in finding better hits for therapies treatment, vaccines design diagnosis approach [6]. In the era of big data, a massive amount of biomedical and genomic data are produced every day that can be utilized for knowledge discovery process [26, 32–34]. Machine learning is one of the effective ways for automated drug development pipeline, which may help us to better understanding of the underlying cause of disease and their associated biological phenomena [32, 35], for example, identification and characterization of drug target, protein structure and function molecular docking, protein-ligand interaction, networking pathway, for further research. A general pipeline for drug repositioning is shown in Fig. 4.

7.1 Computational Drug Repurposing Machine learning workflow typically comprises the four-step process; data preprocessing, extract feature, model fitting and the evaluation of its effectiveness [36]. It offers all possible repurposing drug candidates through the available data resources from biomedical and genome domains such as chemical structures, protein targets,

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Fig. 4 A general pipeline of drug repositioning

target sequences, genotypic and phenotypic expression etc. In silico drug repositioning approaches divided into five categories, named machine learning algorithms, network-based, signature-based, text mining, and semantic. Following the strategies of an in silico drug repurposing, the machine learning approach is applied to exploit the drug and disease-based characterization (drug-based and diseased based learning). It classifies the true and false drug-disease network through the prediction model. The combination steps of computational repositioning strategies and approaches generally signify the desired outcome. Recently, machine learning driven tool, called the Vaxign reverse vaccinology, has been proposed for discovering coronavirus candidates [37]. However, the huge amount of data is needed globally for machine learning techniques to analyze the therapeutic effect of confirmed cases. Drug discovery, designing, and reprofiling are very tedious, time-consuming and highly crucial in nature. Machine-learning algorithms are deployed in myriad programming languages such as R, MATLAB, Octave. It depends on the users to select the best machine learning algorithm to obtain his/her result of interest and to prevent misleading result generation in drug discovery [38]. For target identification and lead optimization and discovery, a generative adversarial neural network (GAN) is mainly deployed. This approach is able to produce random druggable compounds

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Fig. 5 Machine learning approaches in drug discovery and designing strategy

as well as target-based compounds at the same time [39]. The monitoring of drug compounds, to check the side effects and toxicity analysis many recommendation systems have been developed, the method from the recommendation systems literature to handle the drug repositioning problem. For instance, Pareto dominance and collaborative filtering have been used for amalgamating humongous medical data from various sources, drug repurposing, for identification of physicochemical properties, side effects, receptor targets etc. [40]. Artificial neural networks (ANNs) are utilised by many cheminformaticians, bioinformaticians and computational biologists for rectifying nonlinear relationships among the protein targets and drug candidates and also predicting the efficacy of the drug candidates before the actual drug discovery processes [41]. Figure 5 showcases the potential of machine learning strategies in drug discovery processes and the machine learning approaches are widely exercised to develop a highly effective drug for various diseases.

7.2 Computational Repurposing: Validation and Applications The hybrid approach of exploiting computational methods and experimental screenings (in vitro and in vivo model) progressively used for the preclinical drug evaluation and validating the better hits outcomes. The experimental work confirms the potential best drug interaction efficacy through different assay[34]. It may be supportive to validate the prediction and raising the rate of success in translational research. Numerous applications are accessible for repurposing drugs in disease/therapeutic areas e.g. infectious diseases (Ebola, HIV, Zika, COVID-19), cancers (Multiple myeloma, Breast cancer, Bladder cancer), personalized medicine or precision medicine, orphan/rare disease etc. In the present time, it’s in very high demand

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for machine learning which plays a significant role in contributing to COVID-19 [31]. It trains computers to design, represent, and predict the desired result (using massive information) for further experimental studies.

8 Conclusion Coronavirus outbreak is a remarkable public health emergency in this generation. As mentioned above, the specific repurposing drugs might be played a potential role in helping people fight this infection, but no specific vaccine or drug has been exposed effectively. The repurposed drug can directly enter the advanced clinical trial phase without early trials stage and toxicity tests. It will help to develop new diagnostics, evidence-based treatments, and improve outcomes. The computational intelligence technique for drugs repurposing are an inexpensive, effective, and quicker approach and can reduce the number of failures in clinical trials. These approaches have significantly quick screen against the marketed drugs to potentially identify drugs with anti-coronavirus activity and possibly treat COVID-19. Machine learning and deep learning-based in silico approach can boost this development by rapidly finding drugs with efficacy against the COVID-19, which allows the researcher to better understanding the biological problems, their analyzing and interpreting data. These computational based studies can strongly support for rapidly identifying repurposing drugs against COVID-19. Acknowledgements AS acknowledges funding from the Indian Council of Medical Research, New Delhi (Grant No. 45/17/2019-PHA/BMS) for financial assistance. SQ is supported by DST-Inspire Fellowship, Department of Science & Technology, Government of India.

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Computational Intelligence in Vaccine Design Against COVID-19 Kaushik Kumar Bharadwaj, Ankit Srivastava, Manasa Kumar Panda, Yengkhom Disco Singh, Rojali Maharana, Kalicharan Mandal, B. S. Manisha Singh, Dipanjali Singh, Mohinikanti Das, Devasish Murmu, and Sandeep Kumar Kabi

Abstract The pandemic Novel coronavirus disease (COVID-19) was aggressively expanding throughout the world, and no effective vaccines and drugs are available. Giant pharmaceutical industries and researchers use computer intelligence coupled with bioinformatics knowledge to accelerate the development process of designing an effective vaccine against SARS-CoV-2, a time-consuming, complicated, intricate, and complex process. Supercomputers are used to give power to the Artificial Intelligence (AI) and Machine Learning (ML) assistance for structural modeling of unresolved protein, molecular dynamics simulation (MD) of the modeled protein structure, target finding, selection of B and T cell epitopes and simulations study K. K. Bharadwaj Department of Bioengineering and Technology, Gauhati University Institute of Science and Technology (GUIST), Gauhati University, Guwahati, Assam 781014, India e-mail: [email protected] A. Srivastava Department of Ophthalmology, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, India e-mail: [email protected] M. K. Panda (B) · R. Maharana · K. Mandal · B. S. Manisha Singh · D. Singh · D. Murmu · S. K. Kabi Environment & Sustainability Department, CSIR-IMMT, Bhubaneswar, Odisha 751013, India e-mail: [email protected] R. Maharana e-mail: [email protected] K. Mandal e-mail: [email protected] B. S. Manisha Singh e-mail: [email protected] D. Singh e-mail: [email protected] D. Murmu e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 K. Raza (ed.), Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, Studies in Computational Intelligence 923, https://doi.org/10.1007/978-981-15-8534-0_16

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for vaccine development. In this chapter, we described an in-depth overview on the use and impact of various revolutionary and game-changing technology of computer intelligence like AI and ML which with the guide of computational biology, bioinformatics, structural biology, and genomics paved the way in understanding, design, and development of vaccines at a much diminished time and minimal cost. Various software and tools used in the developmental process are also consolidated here. Finally, the limitations and future prospects of overcoming the global crisis and tackling pandemics with the help of computational intelligence are speculated here. Keywords Novel coronavirus · Vaccine · Artificial intelligence (AI) · Machine learning (ML) · Molecular dynamics simulation (MD)

1 Introduction The world has already faced epidemic diseases such as Ebola, HIV/AIDS, enteropathogenic Escherichia coli, and novel H1N1 in recent years. But, the sudden outbreak of novel coronavirus pandemic has affected the world ecologically and economically in the last few months. The outbreak of coronavirus disease 2019 (COVID-19) from Wuhan, the capital of Central China’s Hubei province, in 2019 and distinguished as a pandemic by the World Health Organization (WHO). The infection has expanded globally and has become a severe concern for human health. The coronavirus’s mere existence and its attack rate have made us locked into our homes without any choice. As there is no particular treatment, commercial drugs or licensed vaccination methods for preventing the one to one transmission of the virus in self-isolation and social distancing have become the sole option. As a result of mandatory quarantine or isolation, everyone has struggled and been affected. Careful social distancing remains the only option to prevent one-to-one infection until commercial medicines, vaccines, or efficient methods have been developed to treat this disease pandemic. The increasing burden of COVID-19 pandemic could potentially bring challenges to public health and the economy [1, 2]. Now the question arises, how can the management and control of the critical health care and decrement of the COVID-19 infected individuals be done with the assistance of computational intelligence (CI)? Today, no vaccine and effective protocol S. K. Kabi e-mail: [email protected] Y. D. Singh Department of Post Harvest Technology, College of Horticulture and Forestry, Central Agricultural University, Pasighat, Arunachal Pradesh 791102, India e-mail: [email protected] M. Das Department of Botany, College of Basic Science & Humanities, Odisha University of Agriculture and Technology, Bhubaneswar 751003, India e-mail: [email protected]

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for SARS-CoV-2 has been available to fight against this disease effectively. The development of vaccines and therapeutic drugs is a time-consuming process; if we consider the attack rate of SARS-CoV-2, it can take the huger portion of human kind in no time. Therefore, in the current scenario, computer intelligence (CI) is a powerful technique in the fight against the COVID-19 [3]. It is realized that, applying the CI approach one can integrate and analyze the large scale data of COVID-19 infected individuals with improved information and accuracy, and at the same time develop the most potent approach to identify the susceptible individual based on physiological and genetic make-up, which can finally contribute to maximizing the effort to adopt improved healthcare management. Figure 1 describes about the utilization of the computational intelligence likes artificial intelligence (AI), machine learning (ML), data science and technology in integration with bioinformatics, structural biology and OMICS are being used in different domain such as: i. ii. iii. iv.

Healthcare wellness and management Development of biomarker and clinical diagnosis Delivery of medical supplies through drones Delivery of food items, cleaning, sanitization, and other tasks was done with the help of robots. v. Identification of infected and non-infected person through-temperature detection, facial recognition technology, etc. vi. Diagnosis of virus and drug discovery and development

One of the most important aspects of computational intelligence is to develop therapeutic agents or vaccine design against COVID-19. It is encouraging that artificial intelligence has created an effective solution to design and develop novel drug candidate-specific against SARS-CoV-2 [4, 5]. In search of novel drugs and repurposing the existing drugs, most of the research labs and data centers have already accelerated the process of applying the concept of artificial intelligence and machine

Fig. 1 Important application of computational intelligence system in fight against COVID-19

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learning to search for vaccine design and treatment for COVID-19. Repurposing of existing drugs ‘atazanavir’ through in silico and machine learning plays a breakthrough in treating COVID-19 [6]. On the other hand, immunology and artificial intelligence models are being used to design good vaccine candidates against SARSCoV-2 spike protein and other immunogenic protein fragments. AlphaFold, developed by Google’s DeepMind, has used its AI technology to understand the 3Dstructure of protein associated with the COVID-19, which could be very advantageous in providing information to propose new drug designing. A UK based startup Benevolent AI is also in the queue to design and generate the drug for the treatment of coronavirus [7]. In addition, Ong et al., the definition has been provided for COVID19 vaccine candidates [8] by a newly established Vaxigen Reverse Vaccinology tools incorporated into machine learning (ML). Currently, there is a paradigm shift in top pharma companies such as Glaxo Smith Kline (GSK), and Pfizer is now using AI-powered technology in collaboration with IBM for vaccine formulation. Researchers are currently able to request supercomputers access – an effective resource to simulate vaccine discovery and development assisted by AI. The researchers have now gained access to supercomputers having a prominent source for AI-assisted simulation of vaccine discovery. Development of Supercomputers of major companies such as DiDi, Tencent, etc. can simulate the process and help predict the evolution and distinct variants of the coronavirus to develop cures and vaccines for deadly COVID-19 in short duration, which are typically difficult for other computers [9]. In a global pandemic situation, the collection of different data of huge COVID-19 patients are managed and processed through artificial intelligence, data science, machine learning, and omics will be beneficial to design and develop biomarker, drug candidates, diagnostic and therapeutic strategies Fig. 1 and also effectively deal with similar outbreaks soon. The real prospect of this study was to assess the role of computational intelligence in artificial intelligence and machine learning for vaccine design against COVID-19.

2 Vaccine Development Vaccine development has proved to be an integral part for tackling the spread of infectious diseases from the beginning and it has progressed in so many directions. The fundamental technique to deal with the infection was established by Edward Jenner’s successful utility of cow pox virus in 1798, to immunize people against small pox. The principle was further carried out by Pasture to prevent anthrax with the help of attenuated cultures, which were termed as vaccines. This showed a path for conventional method of vaccine development for near future, which mainly dealt with two methodologies; firstly, obtaining live attenuated strains in vitro by attenuation of pathogens and secondly utilization of protective antigen in non-living subunits [10].Although the procedures has shown to be prominent it had its own drawbacks i.e. providing the vaccine against the pathogens which did not possess the immune dominant protective antigens and time consuming [11]. The genome based

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vaccine development then became the next step for elucubrating vaccine in silico, without cultivation of pathogen, which has been termed as ‘reverse vaccinology’ and succeeded to be providing new solutions for undeveloped vaccines for example Group B meningococcus (MenB) representing the successful application of reverse vaccinology [12]. The identification of gene variants and significantly circulating strain can lead to the designing of potent immunogens [13]. Though the method has proven to be successful in case of bacterial diseases, it has shown no reflection in the part of pathogens such as protozoa, helminthes and viruses. The evaluated mutation rate and antigenic diversity among the pathogen has somehow restrained the development of vaccine against viruses [14].

2.1 Reverse Vaccinology Reverse vaccinology has served as the fastest and most accomplished method for antigen discovery however it was unable to design vaccine for viruses having well developed antigenic determinants but are unable to induce protective immune response. In these conditions structural approach can play a key role in vaccine designing [15]. Structural biology approaches have made possible to comprehend the structural configuration of the whole genome, viral proteins and antigen antibody complexes. The three dimensional (3D) structure can signify the tertiary structure and position of the viral epitope. Ultimately leading to determine the intricate structural information of virus to resolve the challenges impending the vaccine development [16]. The structural vaccine development includes the following procedures i. ii. iii. iv.

Determination of atomic structure antigen antibody complex Utilization of reverse molecular engineering to remodel the antigen Incorporation of remodeled antigen suture into one of the vaccine platforms Testing the safety and efficacy of the candidate vaccine in vivo.

Recent scenario of the pandemic caused by novel corona virus (COVID-19) has become one of the priority to develop vaccine as soon as possible. The intricate study of the virus in structural and genomic level should be the main concern regarding the designing development of vaccine with high percentage of recovery from COVID-19 and with minimal side effects.

2.2 Synthetic Vaccine The term vaccination was adopted by Edward Jenner, describing the injection of the smallpox vaccine during 1976 [17]. Traditional methods of vaccine designing have been mainly based on the whole pathogen (bacterial or viral) infusion in the host body, which has been killed or weakened so as to prevent them from causing the disease, whereas these were effective producing immune response against them. In

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contrast to this present day, research involves the implication of synthetic vaccines which consists of mainly often requires the incorporation of adjuvant to elicit a strong protective immune response because the antigens alone are not sufficient to induce adequate long-term immunity. They are often regarded as subunit vaccines; however, other kinds of vaccines are viruses like particles, toxoid vaccines, peptide vaccines, and nucleic acid vaccines, and more recently, the use of T-cell epitomes [18]. Subunit vaccines: These are specified by the presence of only antigen components and excluded the viral particles. This is mainly used for reducing the adverse effect, which may be arisen by the introduction of other viral particles. However, this is a time-consuming process as it needs the identification of most promising antigens [18]. Virus-like particles: These include mainly the viral protein, which takes part in the assembly of virus structure and as devoid of any viral DNA or RNA; these are nonvirulent in nature [19, 20]. Toxoid vaccine: These are used where the bacterial toxins are responsible for the disease. Such vaccines are usually produced by inactivating the toxins by treating with formalin, which is still able to activate the immune system. The vaccine against diphtheria and tetanus are examples of such vaccines [18]. DNA vaccines: Genetically engineered DNA incorporated in the body to induce a humoral and cellular immune response. These have been proven to be advantageous over the conventional vaccines and inducing a wider variety of immune response types [21–28]. It has been observed that the risk associated with DNA vaccines are minimal [25]. Studies showed that these are also effective for the induction of specific immunity against cancer-associated antigens [26–28]. Peptide vaccines: The peptides used in these vaccines are typically synthesized to shape an antibody molecule of 20–30 amino acids containing a particular antigenic epitope [29]. Epitopes are typically a part of antigens with larger proteins and are therefore known to be adequate to cause appropriate cellular and humoral responses without accelerating allergens or reactogens [29]. Figure 2 describes about the different vaccinology methods. The conventional method of vaccine development is based on obtaining live attenuated strains in vitro by attenuation of pathogens. Targeted components are isolated from individual strain by using biochemical, microbiological, and serological methods. The targeted component produced become antigen by using the recombinant DNA technology and finally tested for its ability to induce an immune response. The genome based vaccine development then became the next step for elucubrating vaccine in silico, without cultivation of pathogen, which has been termed as ‘reverse vaccinology’. In this process, a particular sequenced genome is hybridized with DNN for the selection of more effective molecules. Than antigen is produced from the selected molecules. After that the antigen is tested for immune response. Application of X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy for determination of

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Fig. 2 Different vaccine development strategies

protein structure or recombinant antigen has led to the growth of the field of Structural vaccinology. After the determination of structure of antigen become incorporate into vaccine programme [30]. T cell epitopes: These are also known as the immune system’s antigenic determinants, especially the B and T cell antibodies. Paratope is the antigen binding component of the antibody. These epitopes can belong both to self and foreign proteins, and can be divided into conformational or linear T-cell epitopes on the antigen-presenting cell surface, where they are binding to MHC molecules in order to generate immune response [31], depending on the structure and integration of the paratopes [32]. The T cell-based induction of the immune system by the introduction of epitopes is determined to play a key role for individuals’ immune system and even the slightest deviation impacts in a greater way in an organism [18]. Different mutational variations were reported in many cases in which the T cells such ALS human immune virus (HIV), hepatitis C, and avian and swine infections may prevent recognition of the amino acid sequences of the proteins associated by virus genes [33, 34]. Therefore T cell epitopes provide a substantive way for designing synthetic vaccines for future use.

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3 Reverse Vaccinology The most essential and adequate appropinquation for wellbeing is disease anticipation, which can be certainly accomplished by the application of biological compounds is known as a vaccine [35]. Vaccines are very advantageous in the recovery of a person from disease by increasing their immunity [36]. The term vaccine was first coined by Edward Jenner in 1796 by disclosing the vaccine in opposition to smallpox disease with the help of contaminated substances that are detached from cows [37]. Later on, Louis Pasteur recommended the term vaccinology and its rule after the discovery that the infections are induced due to microbes. He suggested that for the preparation of particular vaccine isolation, inactivation, and injection of microbes should be done, which give rise to diseases [38]. Vaccination or Immunization is one of the very prosperous, auspicious, and economic practices for the recovery of social health, which has been consumed nearly about two centuries to secure nation lives [39]. The conventional method for vaccine production was done by the cultivation of pathogens, followed by its dissection through biochemical, immunological, and microbiological techniques [40]. This process requires more time and also unable to give a correct solution for so many pathogens causing diseases. To overcome the difficult situation, reverse vaccinology was introduced [40]. Lately, there are two main resolutions involved in vaccine subunits contingent on individual antigens, which are cultured in the laboratory [39]. In the second revolution, genomic sequencing technology is used for the pathogens, which encodes the protein sequence that further helps in the formation of the vaccine. It involves genome sequencing, computer analysis, the formation of antigen, and candidate vaccine [41]. The recombinant proteins are adopted as immunogens to encounter the requirements of vaccinology in the forthcoming [42]. Reverse vaccinology is a technique for the evolution or outgrowth of vaccine which upstarts along with the divination of vaccine intention through the evaluation of micro-organic genome series by bioinformatics process [11]. Out of acceptable and unacceptable characters, the expected proteins are selected for the conduction of the standard wet laboratory experiments to check the chosen vaccine targets [43]. Rino Rappuoli was the first who unfold a vaccine by using the reverse vaccinology method via the bioinformatics process that accelerates the production of vaccines [44]. From then, reverse vaccinology theory is widely being used in many other pathogens like Mycobacterium tuberculosis, Bacillus anthracis, and so on by sequencing their genomes [45].In this reverse vaccinology process, the genome of microbes holds the entire repository of probable antigens, which are being operated as the primary level to conquer data, facts, and knowledge for vaccine evolution [11]. He developed a meningococcal vaccine in the year 2000 from the genomic sequence of Neisseria meningitidis serogroup B strain [11].

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The initial perception after reverse vaccinology was to screen or analyze the pathogen’s genome for identification of genes that scramble proteins with the constitution of real vaccine destination [44]. Since reverse vaccinology has been accustomed time immemorial, the process, as mentioned earlier, is not approachable to the general laboratory by virtue of failure or need in software programs that are comfortable for execution and realization [46]. Various software programs have been advanced for identical vaccine projection, which forms obstacles in the apparatus and facts assimilation as it contains separate data arrangements and computer programming sites [47]. To utilize this equipment effectively, it generally demands confined engineering, control line administrations, and computerized strength [48]. In this emerging field of science, many research fields, including structural biology, immunoproteomics has been participated in vaccine development to surmount the boundaries of the conventional process in vaccine production and innovation, chiefly diseases that are caused by various microorganisms [49].

4 Current Computational Strategies in the Development of Coronavirus Vaccines Developing vaccines was very challenging, time taking, and an expensive process. But in this pandemic situation, there was an urgent need for the developed vaccine for SARS-CoV-2 within less time as much as possible. Current ground breaking and game-changing technologies like Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT) and Big Data in computer science in association with the knowledge of basic virology, immunoinformatics and deeper understanding of molecular and structural biology has helped to cut the time in developing vaccines for the novel COVID-19 (Coronavirus) disease. The structural proteins of the SARS-CoV-2 virus include S protein (Spike), M protein (Membrane), E protein (Envelope), and N protein (Nucleocapsid) [48]. The protein sequences of the coronavirus strain can be retrieved from the National Centre for Biotechnology Information (NCBI) (www.ncbi.nlm.nih.gov) and UniProtKB database [50] in FASTA format. Protein Data Bank (PDB) (www.rcsb.org) was the main repositories of COVID-19 virus protein structures. The Sequence similarity search can be performed by using BLASTp (http://blast.ncbi.nlm.nih.gov/blast). The structural proteins of coronavirus virus induced a robust immune response and considered mainly as the potential target for vaccine development [51]. Epitope predictions were the first important step in vaccine development. Tools like Immune Epitope Database (IEDB) (http://tools.immuneepitope.org/mhci/) [52] (uses artificial neural network). ABCPred tools (http://www.imtech.res.in/raghava/ abcpred/ABC_submission.html) [53] were used for the prediction of B and T cell epitopes. The secondary structures of the proteins were predicted and analyzed by the ExPASy server (http://web.expasy.org/protparam/) [54]. This server gives an

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overview of the molecular weight, isoelectric point (pI), aliphatic index, charged residues, and grand average of hydropathicity (GRAVY) of the construct protein sequence of the vaccine whereas the three-dimensional structure of the multiepitope vaccine was resolved by the Zhang Lab I-TASSER web server (https:// zhanglab.ccmb.med.umich.edu/C-I-TASSER/2019-nCov/) [55], PEP-FOLD server (https://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD/) [56] and RaptorX server (http://raptorx.uchicago.edu/StructPredV2/predict/). BetaWrap server (http:// groups.csail.mit.edu/cb/betawrap/betawrap.html) [57] predicted the motifs, was also considered important in vaccine design. The antigenic protein of the computationally synthesized vaccines can be identified by online servers like VaxiJen (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen_help.html) [58] and ANTIGENpro server (http://scratch.proteomics.ics.uci.edu). The constructed vaccine may trigger hypersensitivity reactions and causing the allergy. Therefore the allergenicity of the potent vaccine candidate was predicted by servers like the AllerTop server (https://www.ddg7pharmfac.net/AllerTOP/) [59], AllerHunter server (http://tiger. dbs.nus.edu.sg/AllerHunter/index.html) [60] and AlgPred server (http://www.imt ech.res.in/raghava/algpred/index.html) [61]. These servers were based on support vector machines (SVM) algorithm [60]. Various physiochemical properties like polarity, hydrophilicity or hydrophobicity, flexibility, charge, solvent accessibility, and turns of the designed vaccine construct were predicted by ProtParam—an ExPASy server (https://web.expasy.org/protparam/). The toxicity of the designed vaccine candidate was predicted by the ToxinPred tool (http://crdd.osdd.net/raghava/ toxinpred/). The binding affinity was calculated by molecular docking of the vaccine construct with the target protein receptor by the High Ambiguity Driven protein-protein Docking (HADDOCK) server (http://milou.science.uu.nl/services/HADDOCK2.2/ haddockserver-easy.html) and ClusPro (http://cluspro.bu.edu/login.php) [62] server. The binding affinity of peptide-peptide docking complex was studied by molecular dynamics simulations, by utilizing GROMACS (Groningen Machine For Chemical Simulations) software. In Fig. 3, the in silico strategies to design a vaccine against the COVID-19 was depicted. Firstly the researcher selected the virulent SARS-CoV-2 strain, and the amino acid sequences of protein were retrieved from databases. For vaccine design, target identification in the viral proteins by predicting B and T cell epitopes was an important task and performed using in silico prediction tools. The vaccine candidate was then constructed, and various properties like Antigenicity, Allergenicity, Toxicity, and Physiochemical properties were predicted by utilizing various online servers. The secondary and tertiary structure of the vaccine candidate was predicted, and docking servers predicted the protein–protein binding of constructed vaccine candidates with the antibodies. To top best-docked complexes was then further refined by performing molecular dynamics simulations (MD).

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Fig. 3 Flow chart depicting in silico vaccine design strategies against novel COVID-19

5 Artificial Intelligence and Machine Learning as a Game Changer Technology in Vaccine Design Against COVID-19 Artificial Intelligence is characterized as one modern technology that is incorporated to develop the competency of instruments or devices to the applicability of Intelligence [63]. It gives the actual health reports of patients, preventing them from visiting so many check-ups [64]. Artificial Intelligence is the approach to recognize and interpret the virus and to improve protections and disincentives. It is useful in understanding virus evolution, structure, and transmission through computation biology, structural biology, and mathematical design. It can also be used for the preparation of drugs & medicines [65]. Artificial intelligence and machine learning scrutinize the different techniques and methods of empowered machines knowingly to achieve the quick-witted work deprived of any manifest [66]. It establishes machine learning for the advancement of design executions contingent on complicated structures of impartial system, deep learning, which is distinguished in recent literatures as potential machine to improve or expand computerized platforms [67]. Now a days it is widely used in many fields and gaining popularity for its ongoing performances in computer technology, machinery, telecommunication, robotics, automation, engineering, systemization, icon evaluation, language interpretation and so on [68].

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The diligence of arising digital techniques comprised of coming generation sequencing has enhanced the mechanism of disease as well as possibility for the evolution of new therapies [69]. Machine learning has improved so many features of human optical sensation to distinguish the clinical specimen like data imaging, disjunction of figures, genesis, organization and affirmation of clinical details [70]. Artificial intelligence has tremendous application in three main sector such as pharmaceutical sector for the invention of novel drug, segmentation sector for the sequencing of clinical information, images etc and enlargement of deep learning method in which genomics and analytical data are combined together to expose modern prognostic pattern [71]. One of the serious obstruction in acceptance of machines learning and artificial intelligence method in medicinal scarcity of more numbers of better quality labelled data, embryonic adjustment Vitreous and juridical regards on data allotment. [72]. The outcome of next generation drugs followed by artificial intelligence and machine learning techniques has larger benefits in attestation and innovation of medical experiments, planning for judicious usage of AI and ML from the facts of actual world and supervisory carelessness for incorporation, interpretation and safety in therapeutical concern to the patients [73]. AI and ML is mainly used for the forecasting of new products characteristics like their harmfulness, ability to interact with the required targets. These attributes are termed as “Target variables”. If the target variable is having emblematic and algebraic issues, then it is termed as classification type whereas if the target value is constant and having many statistical issues, then it is referred to as regression type. Supervised and unsupervised learning problems are based on data labelling and inadequate sample information [74]. Artificial intelligence showing its wide application in research, medical and biotechnological fields has the ability to reconstitute the drug discovery method which has been proved by widespread usage of assimilation, absorption, dispersion, secretion, virulence appliances and effective screening [75]. It is application associates with statistics planning, collection of suitable info, categorization, organisation, deterioration, projection of DNA sequence as well as secondary structure of amino acids and expansion [74]. It is also used in clinical drug development process for the enhancement of man optical conception to detect clinically important samples like data visualization, recognition and segmentation [71]. Artificial Intelligence is the compilation of genetic, biological, and environmental information, which may lead to the evolution of treatment for coronavirus diseases. Machine learning methods can also be used for knowing the factors that are responsible for the death, and the mortality rates can be also be controlled [76]. Artificial Intelligence can rapidly explore all the vast information of medicines that are already convenient, which can be accustomed across COVID-19, or maybe with the collaboration of those, a new vaccine can be invented in less time for the treatment of coronavirus disease [77]. High-Performance Computing (HPC) was surprisingly playing a significant role in the fight against COVID-19 vaccine designing. They are implemented in big data analysis to forecast the spread of the disease [78] to epitopes prediction, solving unresolved protein structures, high throughput molecular docking and molecular

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dynamics (MD) simulations [79]. They are also being used to solve complex computational problems and vast volumes of generated big data, which helps in the understanding of the disease mechanism of novel coronavirus strain and their interactions with the human protein at the molecular and atomic levels. Development of a potential vaccine for COVID-19 by using the huge amount of generated big data in combination with computational intelligence like Artificial Intelligence (AI) and Machine learning (ML) within a very short duration of time was being attempted by many researchers worldwide. By using the GISAID database (www.gisaid.org/CoV2020/), potent targets in the SARS-CoV-2 virus based on immunological studies for developing COVID-19 vaccines were being performed [48]. The spike glycoprotein of SARS-CoV-2 interacts with the host (human) Angiotensin-Converting Enzyme 2 (ACE2) and transmits its RNA genome and selfpropagates in the human body system [80]. Epitope screening on the spike sequence of the novel coronavirus strain based on energy was performed, which would enable to develop a potential vaccine [81]. A huge data of strain SARS-CoV-2 can be collected from the National Center for Biotechnology Information (NCBI) for facilitating vaccine design and development [82]. Online server based on machine learning algorithms like Immune Epitope Database and Analysis Resource was utilized for T and B cell epitope prediction and synthetic vaccine design [83, 84]. Rommie Amaro of Texas Advanced Computing Center (TACC) has created the SARS-CoV-2 spike protein model on the NSF-funded Frontera supercomputer at UT Austin. The Texas Advanced Computing Center (TACC), together with Enamine, was trying to identify the most potential drug molecule out of 2.6 million molecules and finally tested in the laboratory to fight against SARS-CoV-2. Department of Energy (DOE) Argonne and Brookhaven National Laboratories are applying Artificial Intelligence (AI) approach with the supercomputers for molecular docking of drugs [85]. Google Deep Mind created an AI-based software named AlphaFold, which can predict the three-dimensional structure of various unsolved proteins structure associated with the SARS-CoV-2 virus [86]. This deep learning system accurately predicted the membrane protein structure of the SARS-CoV-2 spike protein and shared the structures free for the researcher to access. Amazon Web Services (AWS), Microsoft, and members like IBM, Google, federal agencies, and some Universities formed a COVID-19 HPC Consortium, which will perform high-performance computing (HPC) for the advancement of vaccine and drug development for COVID-19 [87]. In Fig. 4, the various applications like solving unresolved protein structure of SARSCoV-2, epitope mapping, molecular docking, and molecular dynamics simulations performed by high-performance computing (HPC) in association with AI and ML was depicted. To fight against COVID-19, the world over advanced and the most powerful supercomputers are executing the complex computational research. India also joined the battle against COVID-19 by forming SAMAHAR-COVID-19 Hackathon by Centre for Development of Advanced Computing (C-DAC) in association with NVIDIA & OpenACC. SAMAHAR stands for Artificial Intelligence (AI), Machine Learning (ML), Healthcare Analytics based Research [87, 88]. Computational Intelligence

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Fig. 4 Application of high performance computing (HPC) in associations with artificial intelligence (AI) and machine learning (ML) in the fight against COVID-19

(CI) will facilitate the researcher to track and drug discovery research. By the combination of supercomputers high-performance computing (HPC) with Computational Intelligence (CI) likes Artificial Intelligence (AI) and Machine Learning (ML), it has facilitated a potent, faster, and accurate means in the designing and development of COVID-19 vaccines and drugs.

6 Limitations and Challenges of Computational Intelligence As artificial intelligence and machine-learning scientists have been intensively searching and waiting for real-time data produced by this pandemic all over the world, it is necessary but difficult to provide COVID-19 patient details in a timely manner, including COVID-19 therapeutic characteristics and result, followed by the data transformation for easy access. There is still a primary impediment to the provision of clinical data associated with COVID-19, which can be accessed and stored in readily available repositories. Therefore, it is necessary to build cyber-infrastructure in order to boost global partnerships [89]. There are other reasons for the lack of use of AL in tracking and predicting the spread of the disease. These include the lack of historical training data and using such a “big data”, e.g., data obtained from social media. Lazer et al. said that these include inadequate data as well as excessively

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(noisy and outlier) data and growing tension between data privacy and public health issues [90]. Therefore, there is a lack of data, noisy social media, outlines, hybrid data and dynamic algorithms. AI therefore not yet very accurate or reliable COVID-19 spread [91].

7 Conclusion and Future Prospect Computer intelligence (CI) is a well define and challenging area of research which is fast developing during this COVID-19 pandemic to tackle the race in vaccine designing. We need a vaccine right now and the scientific community is at its high time in searching the vaccine for this virus. Various vaccine types are under the trial and researchers are trying to investigate vaccine strategies for treating this deadly disease. As it was also known that the traditional vaccine consisting of entire pathogens may not work properly in developing an effective vaccine. Hence, a proper and well execute toxic free vaccine is much to counter the present COVID-19 pandemic. The type of vaccine that scientific community is interested in, are subunit and nucleic acid vaccines as observed. This type of vaccines can be injected to human intravenously in order to develop immune response by modifying the pathogens genetically to have safe and secure vaccines. In most of the ongoing vaccine trials, nucleic acid-based vaccines seem to be more preferable and successfully developing to tackle this virus. CI like Artificial Intelligence (AI) and Machine Learning (ML) has showed tremendous potential in keeping real time tracking of the spread of the SARSCoV-2 disease, managing huge amount of generated big data and investigating and developing drugs and vaccines in much accelerated time then never before. In conclusion, the use of CI in association with the knowledge of immunoinformatics, structural biology, and system biology has helped to design and developed vaccines for SARS-CoV-2. CI is useful and helping in accelerating the development process of vaccines both in subunit and nucleic acid type vaccines. In future, CI will play a major role in preventing many other diseases also which will significantly improve the preventive healthcare sector worldwide and accelerate the vaccine and drug development process.

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Big Data, Intelligent IoMT, and Image Processing to Combat COVID-19

Big Data Analytics for Understanding and Fighting COVID-19 Sandhya Verma and Rajesh Kumar Gazara

Abstract The coronavirus disease of 2019 (COVID-19), outbreak has hit millions of people and claiming thousands of lives worldwide. In times of unforeseen adversity like COVID-19, big data and advanced technologies are one of the few effective means to combat fast-disseminating flu for which vaccines are yet unknown. Today, many countries are employing big data, machine learning, and other digital tools to track and control this pandemic. In addition, the big data analytics propelled comparative genomic studies of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) strains have opened the door to information on mutations, virulence, evolutionary selection, and more. This has enabled the pharmaceutical and healthcare industries to improve diagnostics, aid drug discovery, and develop personalized medicine strategies. The recurrent mutations and genetic diversity identified in SARS-CoV-2 strains provide the basis to develop a cocktail of vaccines and also facilitate to develop region-specific diagnostic tools, thereby decreasing the chances of failures in testing in the fields. This chapter highlights the chief aspects of big data in current COVID-19 outbreak to figure out the best responses to fight against SARS-CoV-2 and future pandemics. Many software and applications have been developed to track and predict the infection. Similarly, mobile apps are launched for COVID-19 preliminary diagnosis and advanced diagnosis is achieved by medical image processing assisted by AI technologies. Therefore, the big data analytics has accelerated the processes of tracking, prediction, diagnosis and prognosis that have facilitated the health workers, scientists, epidemiologists, and policy makers to make more informed decisions in fighting SARS-CoV-2.

S. Verma (B) Shri Vaishnav Institute of Science, Shri Vaishnav Vidyapeeth Vishwavidyalaya, Indore, Madhya Pradesh, India e-mail: [email protected] R. K. Gazara Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Roorkee, India e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 K. Raza (ed.), Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis, Studies in Computational Intelligence 923, https://doi.org/10.1007/978-981-15-8534-0_17

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Keywords Big data analytics · COVID-19 · SARS-CoV-2 · High-throughput · Machine learning · Artificial intelligence

1 Introduction In December 2019, a novel coronavirus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was identified in Wuhan city of China [1] that causes corona disease-19 (COVID-19). Due to its high transmission efficiency from one person to another, it spread rapidly in the whole world and soon after, on 11 March 2020, World Health Organization (WHO) declared it a global pandemic. Since then innumerable studies have been performed to understand SARS-CoV-2 at genomics and molecular levels in order to elucidate its taxonomic classification and origin. Such information proves vital for strategically planning the effective treatment and control of spread of the virus. Researchers are trying hard to unravel the nature of this virus, the reason why it affects some people more than the others, and the most probable trajectory of the disease. It has been found that the genome of SARSCoV-2 is approximately 30 K nucleotide long. Apart from this, SARS-CoV, MERSCoV, HCoV-OC43, HCoV-229E, HKU1, HCoV-NL63 are some of the other identified human-infecting coronaviruses whose genome sizes vary from 26.4 to 31.7 K nucleotides [2]. It has been observed that SARS-CoV-2 is spreading at an alarming rate than the previously identified coronaviruses [3]. Though the primary symptoms of COVID-19 are fever, cough, shortness of breath, and muscle pain, but people without symptoms have also been found corona positive [3]. As a result, this renders SARS-CoV-2 to spread even faster throughout a community. Furthermore, unintentionally the screening and identification of these kind of cases are being ignored, which further adds to the severity of the disease. According to WHO, by 4:44 pm CEST, 24 June 2020, a total of 9,129,146 people are infected with COVID-19 and 473,797 people have been died. Highest numbers of COVID-19 cases have been reported in USA followed by Brazil, Russia, India, UK, Spain, Peru and Chile. Till date, no exact medicine or vaccine is available for COVID-19. Therefore, many research organizations, government entities, and industries have come together and are using advanced technologies such as big data and analytics along with artificial intelligence (AI) to fight against coronavirus pandemic. One such commendable example is of Taiwan where big data analytics and cell phone tracking was successfully used to control the spread of the coronavirus [4]. Shanghai and the USA are also taking the advantage of big data analytics to overcome coronavirus risks. Information, such as, temperature and travel history of hundred thousands of people was recorded and used to manage pandemic. The advanced analytics technologies not only help with recognizing the signs and symptoms of the disease and tracking the virus but also monitor the availability of hospital resources. Moreover, many big organizations and consortiums are offering free access to datasets and analytics tools so that researchers, scientists, and data analysts can better understand and interpret the

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impact of this disease. Additionally, scientists are printing and analyzing coronavirus genome to track its move [5]. There are various big data components where AI offers to play significant role [6]. One such noteworthy component is biomedical research. With the help of molecular modeling in combination with AI and machine learning, tremendous efforts have been made to develop vaccine and to figure out the most effective drugs from the available ones against COVID-19. In fact, much before COVID-19 outbreak, AI has been acclaimed for its potential to contribute for discovery of new drugs [7–10]. For COVID-19, several research labs and data centers are employing AI in order to find out an effective treatment or a vaccine against it. The biggest advantage of using AI is that it significantly accelerates the processes of new drug discovery and repurposing of the existing drugs. Thus, even though the effective treatment or vaccine against COVID-19 is not likely to be available in the near future due to various medical and scientific checks, it would enable us to deal with such outbreaks in future. This chapter provides important insights into the big data analytics to fight against COVID-19, which includes: (1) sources of big data generation during COVID-19; (2) processing of big data using computational algorithms and AI tools; (3) role of big data to track COVID-19; (4) prediction of COVID-19; and (5) diagnosis (based on X-ray and CT scan) and prognosis of COVID-19. Also, the chapter deals with challenges and limitations of Big Data and analytics to fight against COVID-19.

2 Big Data to Fight Against COVID-19 A massive amount of data is being generated from various sources [11]. Data can be classified into social data (social websites such as Facebook), machine data (i.e. mobile devices, sensors), and transactional data (such as purchase orders, passport applications, credit card payments, and insurance claims) in various formats, including text and video [12]. Rapid increase of data volume and technology development has brought big data era in picture [11]. In the COVID-19 scenario, the patient data such as physician description, X-Ray reports, case record, list of doctors and nurses, and information of epidemic locations are the example of big data. Since millions of people are attacked by COVID-19 in the world, the huge amount of data is generating and being stored in computer databases. It is complicated to analyze each data set and establish a solution to control the pandemic. Now, here big data plays the role in. Big data is becoming a potential and a powerful tool in analyzing datasets and determining patterns that can be used in tracking, prediction, diagnosis, and prognosis of COVID-19. Figure 1 represents how big data analytics provide opportunity to fight against COVID-19.

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Fig. 1 Big data analytics for fighting against coronavirus. a Represents the Big Data generation sources, b shows the computational operations required for Big Data process, and c Displays the applications of Big Data analysis

2.1 Tracking and Prediction Several data scientists and researchers are using new big data-driven approaches to track COVID-19 cases in real-time. Outbreak analytics combined all the data including positive cases, deaths, people recovering from disease, monitoring contacts of positive cases, population flow, travel history, population densities, and others, and then data is processed through AI and machine learning to construct disease models that could be used to predict disease infection rates (high or low) and their effects. Zhao et al. developed multiple linear models to compute the potential infectious people using the traffic flow data, which was downloaded from Baidu Map [13]. Similarly, Jia et al. developed “risk source” model using population flow data

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to create fast and precise risk estimation [14]. Another research group has developed modified Susceptible-Exposed-Infectious-Removed (SEIR) model along with AI on population migration data in China and predicted that the coronavirus outbreak gradually decline by the end of April [15]. As cases are spreading across the world, volume and variety of data related to COVID-19 and infected patients are significantly increasing. Moreover, coronavirus spreads faster than the appearance of its symptoms. Therefore, people could be warned for pandemic (i.e. coronavirus) with the high speed and effectiveness, which is possible using big data and AI as a modern technology. Harness of big data in order to speed up the surveillance of infected populations is being carried-out in various countries. For example, Chinese government is applying big data analytics and AI in forms of millions of security cameras, drone, and facial recognition technology to track the movement of its civilians, to determine if people are following quarantine rule, and to discover suspected COVID-19 cases [16]. Apart from this, they are also monitoring and accessing the information of citizens’, such as, the use of social websites and telecommunications apps. This big data generated by big technologies is collected and used by Chinese government to discover various options to inhibit the spread of coronavirus. Taiwan security systems and health care departments had understood the power of technology and their quick action against spread of coronavirus slowed down the infection rate of coronavirus. By developing the electric fence, to control the coronavirus infection, Taiwan has set a tremendous example of implementing the big data in human health care system [4]. This coronavirus pandemic is creating new opportunities for data scientist to develop new tools and understand the pandemic data to transform it in healthcare and build disease models. In order to warn about locations of corona cases, a data visualization tool has been developed by scientists working in Southern Illinois University (SIU) using GPS data (https://news.siu.edu/ 2020/05/050520-virus-tracker.php). The US government wants to monitor Americans’ smartphone data to track their movements. In this regard, government is dealing with Facebook and Google so that officials could use the data to discover patterns and the possibilities to fight coronavirus (https://www.cnbc.com/2020/03/19/facebook-google-could-share-sma rtphone-data-to-fight-coronavirus.html). Though, this might be against the privacy and ethics policies, therefore, not all are in favor of this. An Indian startup company has launched aiisma app with coronavirus tracking system (https://aiisma.com). This app rewards users for dealing their behavioral data with aiisma app. Another online tool, dashboard by the Center for Systems Science and Engineering at John Hopkins University, drags information related to corona cases from WHO which displays the confirmed cases, deaths, and place. Moreover, data scientist can build models and discover hubs for the disease using these complete datasets and warn health care specialist in advance. Furthermore, a spate of scientific publications ( 0, a larger value of K leads to an isotropic solution, K = 0.1 λ (Integration constant): 0 ≤ λ ≤ 1/7, λ = 0.24

Non linear tensor diffusion filter

Number of iterations: 10 Edge strength (K): K > 0, larger value of K leads to isotropic solution, K = 0.1 λ (Integration constant): 0 ≤ λ ≤ 1/7, λ = 0.24 Uscale (Standard deviation of Gaussian function): 0.7 ≤ Uscale ≤ 1.4, Uscale = 1.2

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where D is a decision map of image structure and is calculated by using Canny detector; H(.) is the Heavy side function. The discrete entropy is represented as follows. Entr opy = −



pk log2 ( pk )

(30)

k

The image quality assessment metrics validate the efficiency of enhancement algorithms; the metrics used in this chapter does not require reference image and hence called as non-reference metrics. Higher value of discrete entropy and lower value of JND reveals the efficiency of enhancement technique. The discrete entropy depends on overall contents of the image and depicts the overall quality of image. JND investigates the edge preservation and lower the value, better the edge preservation. Table 2 depicts the JND values of enhancement algorithms. The discrete entropy table of the enhancement algorithms is depicted in Table 3. The average mean brightness error (AMBE) is represented as follows; where E( p) and E(q) are the entropy values of input and processed images. AM B E = |E( p) − E(q)| E( p) =

i=I h −1 w −1 j=I   i=0

E(q) = E(q) =

q(i, j)

j=0

i=I h −1 w −1 j=I   i=0

p(i, j)

j=0

i=I h −1 w −1 j=I   i=0

(31)

q(i, j)

j=0

The average mean brightness error (AMBE) values of enhancement algorithms is depicted in Table 4. The input COVID-19 CT images are depicted in Fig. 2. The Fig. 3 depicts the mean values of JND, DE and AMBE of enhancement algorithms. In Fig. 4, First and second column depicts the GF algorithm results, Third and Fourth column depicts the GF-UM algorithm results. In Fig. 5, First and Second column depicts the BF algorithm results, Third and Fourth column depicts the BF-UM algorithm results. In Fig. 6, First and Second column depicts the AD algorithm results, Third and Fourth column depicts the AD-UM algorithm results. In Fig. 7, First and Second column depicts the NLSD algorithm results, Third and Fourth column depicts the NLSD-UM algorithm results. In Fig. 8, First and Second column depicts the NLTD algorithm results, Third and Fourth column depicts the NLTD-UM algorithm results. Along with the filtering results, histograms of the resultant images are also depicted here.

GF

0.1029

0.3245

0.6787

0.2897

0.5029

0.7867

0.7129

0.5467

Data set details

ID1

ID2

ID3

ID4

ID5

ID6

ID7

ID8

0.5118

0.6236

0.6125

0.4564

0.2435

0.6110

0.3143

0.6024

GF-UM

0.5754

0.7769

0.7967

0.5126

0.3897

0.6988

0.3267

0.6129

BF

Table 2 JND values of enhancement algorithms

0.5213

0.6898

0.6998

0.4989

0.2546

0.6123

0.2998

0.5024

BF-UM

0.4669

0.6089

0.5038

0.4024

0.2392

0.5756

0.2293

0.0599

ADF

0.3669

0.5089

0.4038

0.3014

0.1392

0.4756

0.2162

0.0584

ADF-UM

0.3870

0.5095

0.4010

0.3001

0.2388

0.4940

0.2758

0.0596

NLSD

0.3670

0.5082

0.4037

0.3007

0.1388

0.4740

0.2158

0.0584

NLSD-UM

0.3669

0.5041

0.4017

0.3016

0.2216

0.4727

0.2154

0.0530

NLTD

0.2669

0.4081

0.3037

0.2006

0.1316

0.3737

0.1156

0.0486

NLTD-UM

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GF

6.0598

5.2086

6.0331

5.8717

6.1542

5.3339

4.7645

5.8124

Data set ID

ID1

ID2

ID3

ID4

ID5

ID6

ID7

ID8

7.0146

5.1616

6.8437

7.3083

6.9927

7.0467

6.1007

7.1056

GF-UM

6.8124

5.7645

6.3339

7.1542

6.8717

7.0331

6.2086

7.0598

BF

6.0146

5.1616

7.1437

7.2083

7.9927

7.1467

6.2007

7.2056

BF-UM

Table 3 Discrete entropy values of enhancement algorithms

6.9010

6.3666

6.7182

7.2562

6.9052

7.3190

6.4351

7.0249

AD

AD-UM

7.6664

7.3012

7.6049

7.6003

7.0274

7.7055

6.9509

7.8668

6.5865

5.5211

6.1322

7.0087

6.7547

6.9128

6.0584

6.9087

NLSD

7.6422

7.2784

7.5823

7.5687

7.0146

7.6638

7.6221

7.8961

NLSD-UM

6.6710

5.4981

6.1656

7.0549

6.8098

6.8977

6.0671

6.9856

NLTD

7.7417

7.6765

7.5887

7.8688

7.8130

7.7692

7.9208

7.9348

NLTD-UM

Non Linear Tensor Diffusion Based Unsharp Masking for Filtering … 427

GF

1.0100

0.9144

1.0196

1.0957

1.0990

1.4411

0.5610

1.1377

Data Set details

ID1

ID2

ID3

ID4

ID5

ID6

ID7

ID8

0.0645

0.1639

0.0687

0.0551

0.0253

0.0060

0.0223

0.0358

GF-UM

0.1377

0.4390

0.4411

0.0990

0.0957

0.0196

0.0856

0.0100

BF

0.9355

0.1639

0.3687

0.0449

1.0253

0.0940

0.0777

0.1358

BF-UM

0.0491

1.0411

0.0568

0.0030

0.0622

0.2663

0.3121

0.0449

AD

Table 4 Average mean brightness error (AMBE) values of enhancement algorithms

0.7163

1.9757

0.8299

0.3471

0.0600

0.6528

0.8279

0.7970

AD-UM

0.3636

0.1956

0.6428

0.2445

0.2127

0.1399

0.0646

0.1611

NLSD

0.6921

1.9529

0.8073

0.3155

0.0472

0.6111

1.4991

0.8263

NLSD-UM

0.2791

0.1726

0.6094

0.1983

0.1576

0.1550

0.0559

0.0842

NLTD

0.7916

2.3510

0.8137

0.6156

0.8456

0.7165

1.7978

0.8650

NLTD-UM

428 S. N. Kumar et al.

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Fig. 2 COVID-19 input CT images (D1-D8) and their histogram 9 8 7 6 5 4 3 2 1 0

JND

DE

AMBE

Fig. 3 Mean values of JND, DE and AMBE of enhancement algorithms

The Statistical analysis was performed by One-way ANOVA for filtering algorithms coupled with unsharp masking approach (Table 5). The null hypothesis and alternate hypothesis are as follows; null hypothesis states that, there is no significant difference in the mean values of performance metrics obtained from the filtering approaches coupled with unsharp mask filtering approach, alternate hypothesis states

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Fig. 4 First and Second column depicts the GF algorithm results, Third and Fourth column depicts the GF-UM algorithm results

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Fig. 5 First and Second column depicts the BF algorithm results, Third and Fourth column depicts the BF-UM algorithm results

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Fig. 6 First and Second column depicts the AD algorithm results, Third and Fourth column depicts the AD-UM algorithm results

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Fig. 7 First and Second column depicts the NLSD algorithm results, Third and Fourth column depicts the NLSD-UM algorithm results

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Fig. 8 First and Second column depicts the NLTD algorithm results, Third and Fourth column depicts the NLTD-UM algorithm results

Non Linear Tensor Diffusion Based Unsharp Masking for Filtering … Table 5 Statistical analysis by ANOVA

435

ANOVA results/performance metrics

f-ratio

P value

Just noticeable distortion (JND)

5.35384

0.00179

Discrete entropy (DE)

6.36165

0.00058

Average mean brightness error (AMBE)

5.59447

0.00137

that, there is a significant difference in the mean values of performance metrics obtained from the filtering approaches coupled with unsharp mask filtering approach. The statistical analysis was done by social statistics calculator. The statistical analysis by ANOVA was depicted in Table 5. Results clearly indicates that, there is a significant variation in the performance metrics of filtering algorithms coupled with unsharp masking approach, since the p value is < 0.05. The NLTD with unsharp masking filtering approach was found to be proficient and can be used in the preprocessing phase prior to segmentation or classification.

4 Conclusion This chapter proposes filtering algorithms for the preprocessing of COVID-19 CT images. The classical unsharp masking is initially proposed and improved versions of unsharp masking based on bilateral filter, anisotropic diffusion, nonlinear scalar diffusion and nonlinear tensor diffusion are also proposed in this chapter. The nonlinear tensor diffusion based unsharp masking generates efficient results, when compared with the other approaches and was validated in terms of metrics like discrete entropy, just noticeable distortion (JND) and Average mean brightness error (AMBE). Future work is the incorporation of proposed filtering in clustering segmentation and deep learning classification model for ROI extraction and classification. Acknowledgements The authors would like to acknowledge the support provided by Nanyang Technological University under NTU Ref: RCA-17/334 for providing the medical images and supporting us in the preparation of the manuscript. Parasuraman Padmanabhan and BalazsGulyas also acknowledge the support from Lee Kong Chian School of Medicine and Data Science and AI Research (DSAIR) centre of NTU (Project Number ADH-11/2017-DSAIR and the support from the Cognitive NeuroImaging Centre (CONIC) at NTU. The author S.N Kumar would also like to acknowledge the support provided by Schmitt Centre for Biomedical Instrumentation (SCBMI) of AmalJyothi College of Engineering.

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