Remote Sensing for Malaria: Monitoring and Predicting Malaria from Operational Satellites [1st ed.] 9783030460198, 9783030460204

Table of contents :
Front Matter ....Pages i-xvi
Why This Book? (Felix Kogan)....Pages 1-13
Malaria Burden (Felix Kogan)....Pages 15-41
Environment in Relation to Parasite, Mosquitoes and Affected People (Felix Kogan)....Pages 43-61
NOAA Operational Environmental Satellites for Earth Monitoring (Felix Kogan)....Pages 63-101
New Satellite-Based Vegetation Health Technology (Felix Kogan)....Pages 103-134
Modelling Malaria With Vegetation Health (Felix Kogan)....Pages 135-190
Early Warning Malaria Outbreaks Using ENSO Climate Forcing (Felix Kogan)....Pages 191-218
1981–2019 Vegetation Health Trends Assessing Malaria Conditions During Intensive Global Warming (Felix Kogan)....Pages 219-263
Main Points to Think About (Felix Kogan)....Pages 265-280
Back Matter ....Pages 281-286

Citation preview

Springer Remote Sensing/Photogrammetry

Felix Kogan

Remote Sensing for Malaria Monitoring and Predicting Malaria from Operational Satellites

Springer Remote Sensing/Photogrammetry

The Springer Remote Sensing/Photogrammetry series seeks to publish a broad portfolio of scientific books, aiming at researchers, students, and everyone interested in the broad field of geospatial science and technologies. The series includes peer-­ reviewed monographs, edited volumes, textbooks, and conference proceedings. It covers the entire area of Remote Sensing, including, but not limited to, land, ocean, atmospheric science and meteorology, geophysics and tectonics, hydrology and water resources management, earth resources, geography and land information, image processing and analysis, satellite imagery, global positioning systems, archaeological investigations, and geomorphological surveying. Series Advisory Board: Marco Chini, Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg Manfred Ehlers, University of Osnabrueck Venkat Lakshmi, The University of South Carolina, USA Norman Mueller, Geoscience Australia, Symonston, Australia Alberto Refice, CNR-ISSIA, Bari, Italy Fabio Rocca, Politecnico di Milano, Italy Andrew Skidmore, The University of Twente, Enschede, The Netherlands Krishna Vadrevu, The University of Maryland, College Park, USA

More information about this series at http://www.springer.com/series/10182

Felix Kogan

Remote Sensing for Malaria Monitoring and Predicting Malaria from Operational Satellites

Felix Kogan National Oceanic and Atmospheric Administration College Park, MD, USA

ISSN 2198-0721     ISSN 2198-073X (electronic) Springer Remote Sensing/Photogrammetry ISBN 978-3-030-46019-8    ISBN 978-3-030-46020-4 (eBook) https://doi.org/10.1007/978-3-030-46020-4 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

This book is dedicated to my family, who supported me during all stages of its writing and took very good care of me. I appreciate their extreme tolerance that I could not pay too much attention to domestic issues.

Foreword

The Earth system consists of atmosphere, ocean and land. Their interaction contributes to the development of biosphere, which provides life for humans and other biological activity. Some biological activities are favorable for humans and some are not. Malaria creates the most unfavorable conditions for nearly half of the Earth’s population. Malaria is endemic to 109 countries around the world, and is responsible for over 200 million clinical cases and more than a million deaths each year. It is affected by social, economic and political situations in the affected countries as well as by environmental conditions, especially climate and weather. Climate creates long-­term malaria development, while weather stimulates short-term malaria conditions. Some researchers have used precipitation and temperature, measured by weather stations, for small-area monitoring annual malaria, while others investigated weather-based climate warming impacts on malaria activity in some locations. Unfortunately, the number of weather stations used for malaria monitoring is limited and they do not have uniform spatial distribution, creating problems for assessments of malaria conditions in advance of malaria impacts on populations. The book “Remote Sensing for Malaria” present a new approach of using high resolution satellite data (1 and 4 km2 spatial and weekly temporal) for short- and longterm monitoring and predicting malaria activity on large and small areas. A very important advantage of this book is the application of the new Vegetation Health method for estimation of moisture and thermal conditions of vegetation, which is the place of parasite and mosquitoes’ dwelling. It is important that the Vegetation Health method is applied to both climate- and weather-based malaria monitoring and prediction. The book is written by a very experienced scientist who has broad knowledge on how to use satellite data for monitoring vegetation. The author has developed innovative ideas and algorithms for estimation of Vegetation Health as an indicator of malaria activity. The book “Remote Sensing for Malaria” is an excellent guide for malaria monitoring. It contains 9 chapters, covering an overview of the global and regional malaria burden, dependence of vector-parasite activity on the environment, principals of operational satellites, sensors, and data downloading and use, and a comprehensive review of Vegetation Health (VH) method, malaria-VH modeling, ix

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Foreword

analysis of global warming and nearly 40-year trends of VH indices in malaria endemic areas. Weekly high-resolution Vegetation Health data for the period 1981-2019 were used for modeling, monitoring and predicting malaria intensity and spread in the endemic areas of continents and countries. The book has also addressed how to predict malaria from climate-based El Niño and La Niña. One chapter is devoted to an interesting discussion of the global warming and CO2 increase, including 39-year VH time series. The author has also demonstrated how users can apply VH indices to monitor vegetation conditions. This book by Dr. Felix Kogan provides information about the monitoring and predicting future malaria scenarios based on assessments of healthy and stressed vegetation conditions during global warming. Anatoly A. Gitelson  University of Nebraska  Lincoln, NE, USA  Israel Institute of Technology (TECHNION)  Haifa, Israel

Acknowledgments

I deeply appreciate tremendous help from my daughter Maria Levinson and my son Eli Kogan who have done enormous work editing this book. Being extremely busy with their work and taking extremely good care of their families, they managed to find time to improve my writing. Besides, I am extremely proud of my children and their families that they provide excellent contribution to the prosperity of USA. My second enormous appreciation is going to my colleague Mr. Guo Wei, extra-expert in software development. I worked with Mr. Wei for many years. He developed excellently working software to retrieve satellite data, process them and convert satellite indices into numerous products based on my algorithms. He also developed excellent WEB site (https:// www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_browse.php), which is regularly attending by very many users, applying the delivered satellite data and products. My deepest appreciation is going to Professor Anatoly Gitelson, who found time to write a very positive Foreword. I also deeply obliged to my former colleagues Drs. Jerry Sullivan, Dan Tarpley, Doug Le Comte, Wendel Wilson and many others being very supportive in my research and development. Moreover, I appreciate suggestions and advises from many users communicating regularly with me. Their numerous comments are helping me to improve and advance my research and development.

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Contents

1 Why This Book? ��������������������������������������������������������������������������������������    1 2 Malaria Burden����������������������������������������������������������������������������������������   15 3 Environment in Relation to Parasite, Mosquitoes and Affected People����������������������������������������������������������������������������������   43 4 NOAA Operational Environmental Satellites for Earth Monitoring ������������������������������������������������������������������������������   63 5 New Satellite-Based Vegetation Health Technology������������������������������  103 6 Modelling Malaria With Vegetation Health������������������������������������������  135 7 Early Warning Malaria Outbreaks Using ENSO Climate Forcing����������������������������������������������������������������������������������������  191 8 1981–2019 Vegetation Health Trends Assessing Malaria Conditions During Intensive Global Warming��������������������������������������  219 9 Main Points to Think About��������������������������������������������������������������������  265 Index������������������������������������������������������������������������������������������������������������������  281

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List of Abbreviations

B Bias BT Brightness temperature CC Correlation Coefficients CFC Chlorofluorocarbon gases dM Malaria deviation from trend (anomaly) EDF Empirical Distribution Function ENSO El Nino Southern Oscillation ETC Equator crossing time GHG Greenhouse Gas HM High Malaria IO Indian Ocean IR Infrared LM Low Malaria NDVI Normalized Difference Vegetation Index NOAA National Oceanic and Atmospheric Administration NIR Near Infrared MC Malaria Cases MRI Malaria Risk Index OLS Ordinary Least Square regression P Precipitation PCC Pearson Correlation Coefficients pCC Partial Correlation Coefficient PC Principal Component PCR Principal Component Regression POES Polar-Orbiting Environmental Satellites Determination coefficient R2 RD Relative Difference RB Relative Bias RMSE Root Mean Square Errors SMN Smoothed NDVI SMT Smoothed Brightness Temperature xv

xvi

SRF Spectral Response Function SST Sea surface Temperature SSTa Sea surface Temperature anomaly T Temperature TA Temperature Anomaly TCI Temperature Condition Index TP Tropical Pacific UV Ultraviolet radiation VCI Vegetation Condition Index VHI Vegetation Health Index VH Vegetation Health VIS Visible

List of Abbreviations

Chapter 1

Why This Book?

Abstract  Malaria - the most deadly, parasitic, human disease, is endemic to 109 countries around the world. It is responsible for over 200 million clinical cases and more than a million deaths each year. Poor tropical and subtropical areas of the world, with nearly half of the world population are the most malaria affected. The African region contributes 60% of global malaria cases (18% children die under the age of five). From 1.4 billion people in Southeast Asia, around 1.2 billion (80%) are exposed to the risk of malaria, specifically in India. From nearly 1 billion people living in the two Americas, 14% of the population are at risk of malaria, mostly in South America (from Amazon rain forest area). Malaria is an extremely costly disease, accounting for 25–35% spending of all outpatient visits, 20–45% of hospital admissions and 15–35% of hospital deaths. In addition, billions of dollars are spent for malaria prevention, protection, work hours losses etc. Annual malaria cost in Africa is estimated at $12 billion, accounting for 40% of Africa’s spending for all health cases. The global financial input to malaria control since 2012 averaged US$ 250 million per year. The main sources of funding are the Global Fund to fight AIDS, Tuberculosis and Malaria, the United States President’s Malaria Initiative and the World Bank’s Booster Program. Eight chapters of this book address challenges and solutions of this disease, using remote sensing-estimated vegetation conditions for malaria monitoring. Chapter 1, (Why this Book?) introduces malaria as very damaging for human’s world problem. It focuses on economic, social and even political issues, specifically for the most affected countries. Chapter 2, (Malaria Burden) describes economic, social and long-term environmental aspects. Chapter 3 (Malaria Vector and Environment) reviews environmental impacts on malaria vector activity, which is important for model development. Chapter 4 (NOAA Operational Satellites for Earth Monitoring), describes satellite sensors, their physical background, produced observations, data collection and processing with derivation of initial indices as indicators of the environment. Chapter 5 (Vegetation Health Technology) presents absolutely new satellite-based Vegetation Health (VH) method, which showed to be very useful for detection and monitoring of environmentally-­dependent events, such as malaria. Chapter 6 (Vegetation Health Modelling Malaria), describes the principal of malaria-VH modeling successfully applied to a few countries. Chapter 7 (VH-based Early Warning Malaria Outbreaks using ENSO Climate Forcing), is describing a method for 3–5  months advanced malaria warning based on a specific climate signal, called ENSO (El Niño-Southern Oscillation). Following ENSO events low or high precipitation and temperature © Springer Nature Switzerland AG 2020 F. Kogan, Remote Sensing for Malaria, Springer Remote Sensing/ Photogrammetry, https://doi.org/10.1007/978-3-030-46020-4_1

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1  Why This Book?

might be predicted in some areas of the world, especially in tropical and sub-­tropical ecosystems. Chapter 8 (1981–2019 Vegetation Health Trends Approximating Malaria Tendency during Intensive Global Warming) addresses the issues of nearly 4-decades of climate warming impacts on changes in VH indices characterizing vector activity in spreading malaria. Chapter 9 (Concluding Remarks Regarding VH-estimated 39-year Weather-Climate Impacts on Malaria) emphasizes s­ pecifically current potential for annual prediction of malaria and assessment of 39-year climate impacts on malaria from satellite-based VH indices. Keywords  Malaria · Environment · Operational satellites · Vegetation Health (VH) · Climate weather

1.1  Malaria Incidents Malaria is the deadliest parasitic human infections, accounting for millions of clinical attacks worldwide annually. Malaria is endemic to 109 countries around the world, and is responsible for over 200 million clinical cases and more than a million deaths each year (WHO 2019, 2018a, b, 2017a, b, 2016, 2015, 2008, 2005, Nizamuddin et al. 2013, a, Rahman et al. 2011, a, 2010, 2006, Montanari et al. 2001, Faiz et  al. 2002). Figure  1.1 presents the dynamics of world malaria-sick people during 2010–2017 (WHO 2018a). Although the number of cases has generally been declining since 2010, the most recent 4-year (from 2015) period, this number increased around ten million cases and based on WHO estimates would keep remaining around 220–223 million in 2018, with 435 thousand expected death (WHO 2019, 2018a).

Fig. 1.1  World number of malaria cases (in millions) in 2009–2018 (2018 data are preliminary)

1.1 Malaria Incidents

3

Malaria occurs mostly in poor tropical and subtropical areas of the world, where nearly half of the world population (3.2 billion people) resides (CDC 2018). Each year, nearly 40% of the world’s population, living mostly in the poorest countries of Africa, Southeast Asia and South America, are at risk of malaria. Slightly less affected malaria regions are the Middle East & southern Europe. The African region carries a disproportionately high share of the global malaria burden, contributing to 60% of global malaria cases, with 18% children dying under the age of five (“every 30 seconds a child die” (USAID 2007)). From 1.216 billion people living in Africa, 66% are at risk of malaria each year. For Asia’s 4.463 billion total population, 49% of the people live under threat from this disease, most of them are living in 11 highly populated countries of the Southeast Asia. From 1.4 billion people in this region, around 1.2 billion (80%) are exposed to the risk of malaria (Shiv et al. 2010). Every year, nearly 2.5 million of malaria cases are reported in these countries, 75–85% of which are reported from India. From nearly 1 billion people living in the two Americas 14% of the population are at risk of malaria, mostly in South America (WHO 2018a, CDC 2018), where mosquito-transmitted malaria remains an important public health concern (Recht et al. 2017). Most malaria cases come from the Amazon rain forest areas in northern countries. In 2015, the four rain forest countries (Brazil, Venezuela, Columbia and Peru) accounted for 83% of malaria cases of the entire South America. These countries contributed 24, 30,10, and 19% cases, respectively, to the total South America annual malaria. Among other world countries, the largest loss life from malaria occurred in Nigeria, Democratic Republic of the Congo, Burkina Faso and India, which account for 58% of all global death (WHO 2018a, b, 2017a, b, Bhatt et al. 2015). Moreover, since nearly 125 million travelers visit malaria-endemic countries annually, about 10,000 cases of malaria are reported after returning home (Texier et al. 2013). According to World Health Organization reports (WHO 2019, 2018a, b, 2017a, b, 2016), in the last three to four years, fifteen countries in sub-Saharan Africa and India carried almost 80% of the global malaria burden. Five countries accounted for nearly half of all malaria cases worldwide: Nigeria (25%), Democratic Republic of the Congo (11%), Mozambique (5%), India (4%) and Uganda (4%). The most important that 10 highest burden countries in Africa reported increase in 2017 malaria cases compared to 2016. Of these, Nigeria, Madagascar and the Democratic Republic of the Congo had the highest estimated increases, all greater than half a million cases. However, in contrast, India reported three million fewer cases in the same period of 2017 (24% decrease compared with 2016), Rwanda has noted a reduction in its malaria burden, with 430,000 fewer cases in 2017 than in 2016, and Ethiopia and Pakistan estimated decreases of more than 240,000 cases over the same period. Preliminary estimate indicates that the slight relief from malaria is related to unfavorable weather, specifically drought, for mosquitos’ activity in spreading the disease.

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1  Why This Book?

1.2  Malaria Cost and Death Burden Although malaria is curable, one of a very specific features of the disease is that a single bite from an infected mosquito can kill a person (especially child) within hours. Therefore, malaria has caused almost one fifths of estimated annual worldwide deaths. Between 1.5 and three million people die annually, accounting for 4–5% of global fatalities (WHO 2018a, b, 2017a, b). Children are the most vulnerable, since up to 50% of the estimated annual malaria mortality fall on persons less than 15 years of age (CDC 2018, Allard 1998). A small child whose body is not yet able to fight the disease can be dead within a day. Pregnant women are highly vulnerable too, since nearly one million (10% all maternal death) die annually from malaria. In some parts of the world there is barely a child who has not suffered from malaria by the time of his or her first birthday (Russel et al. 1963). When the author of this book was 5–9 years old and lived in malaria-endemic Tajikistan (a part of USSR), he had malaria every year. I can still remember those terrible days of suffering (even after taking medicine), when your body has 42–43 °C fever, resulting in extreme chills and shivering. Malaria is an extremely costly disease. According to US estimates, global malaria accounts for 25–35% spending of all outpatient visits, 20–45% of hospital admissions and 15–35% of hospital deaths (WHO 2018a, 2017b, MMV 2017, CDC 2018, Sachs and Malaney 2002). The global financial input to malaria control since 2012 averaged US$ 250 million per year. The main sources of funding are the Global Fund to fight AIDS, Tuberculosis and Malaria, the United States President’s Malaria Initiative and the World Bank’s Booster Program (WHO 2008). Annual malaria cost in Africa is estimated at $12 billion, accounting for 40% of Africa’s spending for all health cases (CDC 2018, MMV 2017). In addition to human burden and death, malaria also imposes drastic economic production losses (Nagpal and Sharma 1995). Total funding for malaria control and elimination reached an estimated US$ 2.7 billion in 2016; contributions from governments of endemic countries is up to US$ 800 million, accounting for 31% of all health funding (WHO 2018a, b, CDC 2018, MMV 2017). From several malaria varieties, two types Plasmodium (P) falciparum (P.f.) and P. vivax (P.v.), are the most widespread and dangerous, being the deadliest parasite (Paresul 2008, Smith and Mckenzie 2004, WHO 2002, 1999, Srivastava et al. 2001, Najera et al. 1998, Ingrid and Van 2004). Among these two (P.f. and P.v.), the first one is the most frequent, contributing 75% of all global cases. Moreover, disease is caused by P.f., the most fatal, contributing to nearly one million deaths each year (80% of global cases). Plasmodium falciparum is the most frequent in Africa and Southeast Asia, while in South America, more than half of malaria cases are caused by Plasmodium vivax (Recht et al. 2017, Shiv et al. 2010). Among other varieties, there is less spread malaria types such as A. balabacensis as a vector of malaria in northeast India, Anopheles Philippinensis Ludlow in Bangladesh, Anopheles fluviatilis and Anopheles culicifacies in relation with malaria in forest and deforested riverine ecosystems in northern Orissa, India, malaria transmission risk by the

1.2 Malaria Cost and Death Burden

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mosquito Anopheles baimai (formerly known as An. Dirus, breeding habitats of Anopheles baimai (Dutta et  al. 1989, Elias and Rahman 1987, Mohapatra et  al. 1998, Nanda et al. 2000, Prakash et al. 2005, 2006, Prafulla Dutta et al. 2010, FAO 2017, Wickramasinghe et al. 2002). Fortunately, malaria is a curable and preventable disease. (WHO 2018a, b 2016, 2005). Following the most important social and economic goals, the world is making considerable contributions to cure malaria, prevent its proliferation and reduce human death. In addition to considerable funding each year, the World Health Organization (WHO 2018a, 2017a, b, 2015) assesses global and regional malaria trends, highlighting progress towards global targets, and describing opportunities and challenges in controlling and eliminating the disease. For example, following the WHO (2017b) report, in 2016, there were an estimated 216 million cases of malaria in 91 countries, an increase of five million cases over 2015 (Bhatt et  al. 2015). Malaria deaths reached 445,000  in 2016, compared to 446, 000  in 2015. From those cases, 91% of deaths have occurred in Africa, of which 80% of cases were reported by only 15 countries (MMV 2017). Since the African region carries a disproportionately high share of the global malaria burden, funding for malaria control and elimination was US$ 2.7 billion in 2016 from international organizations, plus nearly one billion from governments of endemic countries, representing 31% of the total health funding (WHO 2018a, b, CDC 2018, MMV 2017). Malaria-fighting fund is normally allocated for malaria control and prevention. Malaria control includes treating sick people, drug distribution and also quite effective preventive measures. However, a new serious obstacle - drug resistance, appeared recently affecting strongly malaria control; chloroquine, the cheapest and most widely used antimalarial drug, has lost its clinical effectiveness in most parts of the world (WHO 2005, 2002). Malaria preventive measures include protection for the most vulnerable populations. However, this program is very important but difficult to implement since it requires a lot of regularly continued efforts over a long period of time. This program includes sleeping under a mosquito net treated with insecticides that kill mosquitoes or stop them from biting. This is a powerful prevention against malaria. Spraying insecticides inside dwellings that leave a residue on walls, special protection for pregnant women using insecticide treated nets (ITNs) and intermittent preventive treatment with antimalarial drugs given as part of normal care, can protect the mother and her unborn child. In addition, rapid treatment with effective antimalarial drugs for anyone suspected of having malaria can save lives, plus improved early warning, detection and response to malaria epidemics can avert catastrophe (WHO 2017a, b, 2015, 2005, 2002, Bhatt et al. 2015). Meanwhile, some obstacles have appeared recently since the disease is getting new strength, as the parasites developed resistance to the most commonly used antimalarial drugs (Ingrid and Van 2004), and the mosquitoes became resilient to insecticides; moreover, malaria has also re-emerged in several Central Asian and Eastern European countries and in some parts of Southeast Asia (WHO 2005). Additional obstacles to fighting malaria are global socioeconomic challenges. One of the main problems is intensive world population growth, especially in poor

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countries strongly affected by malaria. Therefore, considering a strong population increase and a much slower increase in malaria cases (compared to population growth), the time series of malaria rate (per 1000 population at risk), promoted by international organizations, provides misleading characteristics of malaria progress. The example in Fig. 1.2 (WHO 2019) confirms that during 2010–2017, the rate of malaria has not changed, remaining stable in the world (around 80 people per 1000 world malaria-affected population), in eastern Mediterranean and eastern Pacific WHO regions. Moreover, in Africa and south-east Asia, where total population increase is the world’s strongest, the rate of malaria progression (per 1000 malaria-­ affected population) is even declining. This contradicts other WHO statements, indicating that the WHO African Region was home not only to the highest number of malaria cases with increasing trends, but also to the highest number of malaria deaths in 2017. It also accounted for 88% of the 172,000 fewer global malaria deaths reported in 2017 as compared to 2010. Only the WHO Americas’ region is recording a rise, largely due to increases in malaria transmission in the most populated countries: Brazil, Nicaragua and Venezuela.

Fig. 1.2  Number of malaria cases per 1000 population in the world, Africa and other WHO regions

1.2 Malaria Cost and Death Burden

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Unfortunately, malaria is most dangerous for children, especially under the age of five. In 2017, this group accounted for 61% (266,000) of all malaria deaths worldwide. 2017 was the worst malaria year for the world since 2015 (Fig. 1.1). The WHO African Region had the worst malaria situation, accounting for 93% of world malaria deaths in 2017. Nearly 80% of global malaria deaths in 2017 were concentrated in 17 countries of the WHO African Region and India. Seven of these countries accounted for 53% of all global malaria deaths (WHO 2019), including Nigeria (19%), Democratic Republic of the Congo (11%), Burkina Faso (6%), United Republic of Tanzania (5%), Sierra Leone (4%), Niger (4%) and India (4%). The African, South America and Southeast Asia (specifically India) countries are facing more difficulties for fighting malaria due to a complication with a lack of food and people malnutrition. A strong disproportion between an intensive population increase and slow agricultural production growth in these regions (especially Africa and India), are intensifying a negative balance between food supply and demands, especially in the frequent years of intensive droughts. Unfortunately, the amount of agricultural production increase is behind the demands from intensive population growth, indicating that there are many hungry people, which are more vulnerable to contract malaria. Nearly one billion people, in poor countries, are hungry every year. Shortages of food became unbearable in drought years due to agricultural losses. As the result, hungry and malnourished people in Africa, Southeast Asia and South America became extremely vulnerable to malaria. In addition, there are some tendencies in malaria-affected countries towards ecosystems deterioration (deforestation etc.) and exhaustion of environmental resources, (soil fertility loss etc.), intensifying vector activity in spreading malaria. Moreover, the recent 40-year climate warming has also contributed to unbalanced food supply-­ demands, increasing the number of hungry people more vulnerable to malaria (Godfray et al. 2018, 2010, WFP 2014, FAO 2018, 2017, 1999, Zhou et al. 2004, Hay et al. 2002, Githeko et al. 2000). Since malaria parasites are transmitted to people through the bites of infected female mosquitos, the mosquito vector is also the main contributor to malaria burden and correspondingly to the number of malaria cases (CDC 2018, Bouma 2003, Githeko et  al. 2000, WHO 2005). Vector activity and ability to transmit malaria changes from year to year depending on weather conditions. Principally, a mosquito vector is extremely active in wet and moderately warm conditions. If the conditions are hot and dry, the vector activity reduces considerably, leading to a smaller number of people infected with malaria. Therefore, weather parameters (precipitation, temperature, air humidity etc.), collected by meteorological stations, have been used traditionally as the indicators for monitoring malaria epidemic (Texier et al. 2013, Thomson et al. 2006, 2000, Thomson and Connor 2001). Unfortunately, the number of weather station is limited and they do not have uniform spatial distribution creating problems of using weather data for effective malaria monitoring. For example, in malaria-endemic areas of sub-Sahara Africa, one weather station is available for the area from 10,000 to 35,000 km2 (depending on the country, ecosystem and climate). Therefore, operational environmental

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satellite data were investigated as a tool for malaria monitoring (Nizamuddin et al. 2013, a, Rahman et al. 2011, a, 2010, 2006, Ceccato et al. 2005, Kaya et al. 2002, Elias and Rahman 1987). An important success has been achieved in a few malaria-­ endemic countries of Africa, Southeast Asia and South America when satellite-­ derived Vegetation health (VH) methodology (Kogan 2018, 2002, 2001, 2000, 1997, 1995) was used as an estimator of malaria vector activities and as predictor of the number of malaria cases (Nizamuddin et al. 2013, a, Rahman et al. 2011). It is important to emphasize that VH assessments have been developed using land and atmosphere observations by the NOAA operational afternoon polar-orbiting environmental satellites since 1981. Between 1981–2012, the Advance Very High Resolution Radiometer (AVHRR) sensor was used (Cracknell 1997). In 2013, the most advanced Visible-Infrared Imaging Radiometer Suite (VIIRS) has been used. VIIRS accommodates the best technical and scientific features of its predecessors (including AVHRR) and has new features such as a wider swath, higher resolution (0.375, 0.5, 0.750 km2), a sharper view at the swath’s edge, faster data processing and availability, advanced climate/weather impacts monitoring and other attributes (JPSS 2014). Following the current plans, the new generation of NOAA operational satellites, called Joint Polar Satellite System, (JPSS) will continue these observations until the mid-2040 and is scheduled to be replace with more advanced system (JPSS 2014). This book presents a new and comprehensive Vegetation Health (VH) system and provides practical advices for using VH for malaria monitoring, predicting the number of affected people and assessing long-term prospective for malaria development in the current climate warming. An enhanced attention of the book is (a) outlining the principals for monitor malaria from operational polar-orbiting satellites; (b) developing a few country’s models for predicting malaria area; (c) modeling malaria intensity based on weather-­ related mosquito-vector activity; (d) applying the new satellite-based Vegetation Health (VH) method for assessment of moisture and temperature conditions of mosquito habitat and digitized vector activity in spreading malaria; (e) estimating if a recent climate warming has expanded malaria area and intensified transmission of disease to people; and (f) providing advice to users on the application of VH technology for an early assessments of malaria area, intensity and risk level in the number of affected people.

1.3  Book Composition This book contains eight chapters. Chapter 1, “Why this Book?” introduces malaria as a very damaging phenomenon for human world problem. It focuses on economic, social and even political issues, specifically for the most affected countries. This chapter introduces the main goals of the book, which is to combine operational satellite measurements of land cover with the new Vegetation Health (VH)

1.3 Book Composition

9

method to control weather-related malaria start, development (expansion and timing) and impacts. Such unique combination of satellite-measured land cover reflectance and emission with VH method provides a unique option of an early detection of environmental conditions stimulating malaria intensification and spreading. This Chapter provides short descriptions of the remaining chapters. Chapter 2, “Malaria Burden” describes economic, social and long-term environmental aspects of malaria. Chapter 3, “Malaria Vector and Environment,” provides a comprehensive review of climate, ecosystems and weather impacts on malaria distribution and vector activity, which is important for model development, designed to predict malaria affected area, intensity, duration and the number of affected people. Chapter 4, “NOAA Operational Satellites for Earth Monitoring,” describes satellites and sensors, their physical background, produced observations, data collection and derivation of initial indices. Since the collected data and indices are extremely noisy (both over space and time), a very important part of this Chapter is data calibration, sampling and multiple methods applied for data correction. Chapter 5, “New Vegetation Health Technology,” presents theoretical basis of uniquely advanced method for development of satellite-­based Vegetation Health indices and products. The products include malaria start, area and intensity, numerical approximation of weather-related moisture and thermal conditions of vegetation as mosquitos’ habitat, identification of drought area, intensity and duration, identifying the area and intensity of healthy and stress vegetation as a predictors of malaria development. Chapter 6, “Modelling Malaria with Vegetation Health,” describes the principal of modeling and application of VH indices and products for modeling the number of malaria cases in a number of countries. An important part of this chapter is that the models were independently validated based on in situ data and used for prediction of the number of malaria cases in a few countries on all malaria-affected continents. Some assessments were made to address malaria world security. Chapter 7, “VH-based Early Warning Malaria Outbreaks using ENSO Climate Forcing” describes a method for 3–4 months advanced prediction of malaria intensity based on ENSO development and intensity. Chapter 8, “1981-2019 Vegetation Health Trends Predicting Malaria Tendency during Intensive Global Warming” provides numerical analysis on how the recent 40-year climate warming changed the area and intensified malaria and what to expect in the near future. The chapter also describes causes (including new ideas) and intensity of climate warming, changes in land cover, such as vegetation greenness, temperature, moisture and thermal conditions. Chapter 9, “Conclusion” summarizes major points of the Book and provides suggestions to users on practical applications of VH indices and products. In some instances, the models and, in the others, VH data images and time series, were used to predict shot-term malaria performance and long-term trends for estimation of future tendencies in vector activity in spreading malaria.

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1  Why This Book?

References Allard, R. (1998). Use of time-series analysis in infectious disease surveillance. Bulletin of World Health Organization, 76, 327–333. Bhatt, S., Weiss, D.  J., Cameron, E., Bisanzio, D., Mappin, B., & Dalrymple, U. (2015). The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature, 526(7572), 207–211. Bouma, M. (2003). Methodological problems and amendments to demonstrate effects of temperature on the epidemiology of malaria. A new perspective on the highland epidemics in Madagascar, 1972-1989. Transactions of the Royal Society of Tropical Medicine and Hygiene, 97, 133–139. CDC (Center for Disease Control and Prevention) (2018). Malaria’s Impacts Worldwide. Jul 24. https://www.cdc.gov/malaria/malaria_worldwide/impacts.html Ceccato, P., Connor, S. J., Jeanne, I., & Thomson, M. C. (2005). Application of geographical information system and remote sensing in malaria risk. Parasitologia, 47, 81–96. Cracknell, A. P. (1997). The advanced very high resolution radiometer (534 p). USA: Taylor & Francis. Dutta, P., Bhattacharyya, D. R., Sharma, C. K., & Dutta, L. P. (1989). The importance of Anopheles dirus (A. balabacensis) as a vector of malaria in Northeast India. Indian Journal Malariology, 26, 95–111. Elias, & Rahman. (1987). The ecology of malaria carrying mosquito Anopheles Philippinensis Ludlow and its relation to malaria in Bangladesh. Medical Research Council Bulletin, Bangladesh, 13, 15–28. Faiz, M. A., Yunus, E. B., Rahman, M. R., Hosain, M. A., Pang, L. W., Rahman, M. E., & Bhuiya, S. N. (2002). Failure of national guidelines to diagnose uncomplicated malaria in Bangladesh. American Journal of Tropical Medicine and Hygiene, 67, 396–399. FAO (2018). FAO Cereal Supply and Demand Brief. World Food Situation. http://www.fao.org/ worldfoodsituation/csdb/en/ FAO (2017). How Close We are to Zero Hunger. http://www.fao.org/ state-of-food-security-nutrition/en/. FAO (1999). Food Insecurity: When people must live with hunger and fear of starvation. The State of Food Insecurity in the World Report, Rome, 76 pp. Githeko, A., Lindsay, S., Confalonieri, U., & Patz, J. (2000). Climate change and vector- borne diseases: A regional analysis. Bulletin of World Health Organization, 78, 200–207. Godfray, H. C. J., Aveyard, P., Garnett, T., Hall, J. W., Key, T. J., Lorimar, J., Pirrehumbert, R. T., Scarborough, P., Springmann, M., & Jebb, S.  A. (2018). Meat consumption health and the environment. Science, 361(caam 5324), 1–8. Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., & Toulmin, C. (2010). Food security: The challenge of feeding 9 billion people. Science, 327, 812–818. https://doi.org/10.1126/science.1185383. Hay, I., Rogers, J., Randolph, E., Stern, I., Cox, J., Shanks, D., & Snow, W. (2002). Hot topic or hot air? Climate change and malaria resurgence in east African highlands. Trends in Parasitology, 18, 530–534. Ingrid, F., & Van, B. (2004). Drug resistance in Plasmodium falciparum from the Chittagong Hill tracts, Bangladesh. Tropical Medicine & International Health, 9, 680–687. JPSS (2014). Joint Polar Satellite System. http://www.jpss.noaa.gov Kaya, S., Pultz, T.  J., Mbogo, C.  M., Beier, J.  C., & Mushinzimana, E. (2002, June). The use of radar remote sensing for identifying environmental factors associated with malaria risk in coastal Kenya. IGARSS, 24–28. Kogan, F. (2018). Remote sensing for food security (p. 255). Springer. ISBN 978-3-319-96255-6. Kogan, F. N. (1995). Droughts of the late 1980s in the United State as derived from NOAA polar orbiting satellite data. Bulletin of the American Meteorological Society, 76, 655–668.

References

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Kogan, F. (1997). Global drought watches from space. Bulletin of the American Meteorological Society, 78, 621–636. Kogan, F. N. (2000). Global drought detection and impact assessment from space. In D. A. Wilhite (Ed.), Drought: A global assessment (Hazard and Disaster Series) (pp. 196–210). London and New York: Routledge. Kogan, F. N. (2001). Operational space technology for global vegetation assessment. Bulletin of the American Meteorological Society, 82, 1949–1964. Kogan, F. (2002). World droughts in the new millennium from AVHRR-based vegetation health indices. Eos, 83, 557–564. MMV (Medicine for Malaria Venture) (2017). World Malaria Report (Five species). https://www. mmv.org/newsroom/publications/world-malaria-report-2017 Mohapatra, P. K., Prakash, A., Bhattacharyya, D. R., & Mahanta, J. (1998). Malaria situation in north-eastern region of India. ICMR Bulletin, 28(3), 21–30. Montanari, R., Bangali, M., Talukder, K., Baqui, A., Mashewary, N., Gosh, A., Rahamn, M., & Mahmood, A. (2001). Three case definitions of malaria and their effect on diagnosis, treatment and surveillance in Cox’s Bazar district, Bangladesh. Bulletin of the World Health Organization, 79, 648–656. Najera, J.A., Kouznetzsov, R.L., & Delacollette, C. (1998). Malaria Epidemic: Detection and Control, Forecasting and Prevention. World Health Organization, Geneva. WHO/MAL/98.1084. Nagpal, B., & Sharma, V. (1995). Indian anophelines (pp. 416–423). New Delhi: Baba Barkha Nath Printers. Nanda, N., Yadav, R.  S., Subbarao, K., Sarala, Joshi, H., & Sharma, V.  P. (2000). Studies on Anopheles fluviatilis and Anopheles culicifacies in relation with malaria in forest and deforested riverine ecosystems in northern Orissa, India. Journal of the American Mosquito Control Association, 16(3), 199. Nizamuddin, M., Kogan, F., Dhiman, R., Guo, W., & Roytman, L. (2013). Modeling and forecasting malaria in Tripura, INDIA using NOAA/AVHRR-based vegetation health indices. International Journal of Remote Sensing Application, 3(3), 108–116. Nizamuddin, M., Akhand1, K., Roytman1, L., Kogan, F., & Goldberg, M. (2013a). Optical remote sensing a potential tool for forecasting malaria in Orissa, India. In Remote Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring III (Ed. Šárka O. Southern), SPIE Proc. Vol. 8723, https://doi.org/10.1117/12.2014702. Paresul, A. (2008). Malaria country report. Malaria and Parasitic Disease Control Unit. Bangladesh: Directorate General of Health Services. Prakash, A., Walton, F., Bhattacharyya, D.  R., Samantha, O.  L., Mohapatra, P.  K., & Mahanta, J. (2006). Molecular characterization and species identification of the Anopheles dirus and An. minimus complexes in Northeast India using r-DNA ITS- 2. Acta Tropica, 100, 156–161. Prakash, A., Bhattacharyya, D. R., Mohapatra, P. K., & Mahanta, J. (2005). Malaria transmission risk by the mosquito Anopheles baimai (formerly known as An. dirus species D) at different hours of the night in Northeast India. Medical and Veterinary Entomology, 19, 423–427. Dutta, P., Khan, S. A., Bhattarcharyya, D. R., Khan, A. M., Sharma, C. K., & Mahanta, J. (2010). Breeding habitats of Anopheles baimai and its role in incidence of malaria in Northeastern region of India EcoHealth, https://doi.org/10.1007/s10393-010-0337-7. Rahman, A., Kogan, F., Roytman, L., Goldberg, M., & Guo, W. (2011). Modelling and prediction of malaria vector distribution in Bangladesh from remote-sensing data. International Journal of Remote Sensing, 32(5), 1233–1251. Rahman, A., Roytman, L., Goldberg, M., & Kogan, F. (2011a). Comparative analysis on applicability of satellite and meteorological data for prediction of malaria in endemic area in Bangladesh. The American Journal of Tropical Medicine and Hygiene, 82(6), 1004–1009. Rahman, A., Krakauer, N., Roytman, L., Goldberg, M., & Kogan, F. (2010). Application of Advanced Very High Resolution Radiometer (AVHRR)-based vegetation health indices for estimation of malaria cases. The American Journal of Tropical Medicine and Hygiene, 82(6), 1004–1009. https://doi.org/10.4269/ajtmh.2010.09-0201.

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Rahman, A., Kogan, F., & Roytman, L. (2006). Short report: Analysis of malaria cases assessing Bangladesh with remote sensing data. The American Journal of Tropical Medicine and Hygiene, 74(1), 17–19. Recht, J., Siqueira, A. M., Monteiro, W. M., Herrera, S. M., Herrera, S., & Lacerda, M. V. (2017). Malaria in Brazil, Colombia, Peru and Venezuela: Current challenges in malaria control and elimination. Malaria Journal, 16, 273. https://doi.org/10.1186/s12936-017-1925-6. Russel, F., West, L., Manwell, D., & Macdonald, G. (1963). Practical malariology. London: Oxford University Press. Sachs, J., & Malaney, P. (2002). The economic and social burden of malaria. Nature, 415, 680–685. Shiv, L., Sonal, G.  S., & Phukan, P.  K. (2010). Status of malaria in India. Journal of Indian Academy of Clinical Medicine, 5(1). Smith, D., & Mckenzie, E. (2004). Statics and dynamics of malaria infection in Anopheles mosquitoes. Malaria Journal, 3, 13. Srivastava, A., Negpal, B. N., Saxena, R., & Subbarao, S. K. (2001). Predictive habitat modeling for forest malaria vector species An. Dirus in India: A GIS–based approach. Current Science, 80(9), 38–44. Texier, G., Machault, V., Barragti, M., Boutin, J.-P., & Rogier, C. (2013). Environmental determinant of malaria cases among travelers. Malaria Journal, 12, 87–95. https://doi. org/10.1186/1475-2875-12-87. Thomson, M. C., & Connor, S. J. (2001). The development of malaria early warning systems for Africa. Trends in Parasitology, 17, 438–445. Thomson, M. C., Connor, S. J., O’niell, K., & Meert, J. P. (2000). Environmental information for epidemic prediction. Parasitology Today, 16, 137–138. Thomson, M.  C., Doblas-Reyes, F.  J., Mason, S.  J., Hagedorn, R.  S., Connor, J., Phindela, T., Morse, A. P., & Palmer, T. N. (2006). Malaria early warnings based on seasonal climate forecasts from multi-model ensembles. Nature, 439, 576–579. USAID (2007). Malaria Report. http//:www.centralchronicle www.fightmalaria.gov; http:// www.pmi.gov WFP (2014). World Food Program. https://www.google.com/search?q=world+food+program&oq =world+food+program&aqs=chrome..69i57j0l5.6319j0j7&sourceid=chrome&ie=UTF WHO (World Health Organization) (2019). Number of malaria cases (per 1000 population at risk) 2010–2017. https://www.who.int/gho/malaria/epidemic/cases/en/ WHO (2018a). Malaria No More, November 19. World Health Organization, Geneva, Switzerland, https://www.malarianomore.org/ WHO (2018b). Malaria Report, June 11. World Health Organization, Geneva, Switzerland http:// www.who.int/en/news-room/fact-sheets/detail/malaria WHO (2017a). World Malaria Report 2017. World Health Organization, Geneva, Switzerland http://apps.who.int/iris/bitstream/handle/10665/259492/9789241565523-eng.pdf?sequence=1 ISBN 978–92–4-156552-3. WHO (2017b). Framework for a national plan for monitoring and management of insecticide resistance in malaria vector. World Health Organization, Geneva. http://www.who.int/malaria/ publications/atoz/9789241512138/en/ WHO (2016). World Malaria Report. http://www.who.int/ WHO (2015). Global technical strategy for malaria 2016–2030. Geneva: http://www.who.int/ malaria/areas/global_technical_strategy/en. WHO (2008). Global Malaria Control and elimination. January, Geneva, Switzerland, 180  pp. http://apps.who.int/iris/bitstream/handle/10665/43903/9789241596756_eng.pdf;jsessionid=5 9A8258EC54D6C512577072C44D496AC?sequence=1 WHO (2005). World Malaria Report 2005, UNISEF, World Health Organization, Geneva 27, Switzerland. http://www.who.int/en/news-room/fact-sheets/detail/malaria WHO (2002). Final Report on the Third Meeting of the RBM Technical Resource Network on Epidemic Prevention and Control Geneva, Switzerland, http://www.rbm.who.int/ docs/.

References

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WHO (1999). Rolling Back Malaria. The World Health Report, Geneva, Switzerland, http://www. rbm.who.int/ docs/whr99.htm. Wickramasinghe, R., Gunawardena, M., & Mahawithanage, T. (2002). Use of routinely collected past surveillance data in identifying and mapping high risk areas in a malaria endemic area of SriLanka. The Southeast Asian Journal of Tropical Medicine and Public Health, 33, 678–684. Zhou, G., Minakawa, N., Gitenko, A. K., & Yan, G. (2004). Association between climate variability and malaria epidemics in the east African highlands. Proceedings of the National Academy of Sciences USA, 101, 2375–2380.

Chapter 2

Malaria Burden

Abstract  Malaria is a mosquito-borne infectious disease, which ranks among the major world health challenges affecting people in the poorest countries of sub-­ Sahara Africa, Southeast Asia, Western Pacific and Latin America. Among 3.2 billion people living in these regions, 10–15% are at risk of malaria with up to one million deaths annually, mostly children under age five. Following WHO, between years 2010 and 2017, the number of world malaria-infected cases and death were over 200 million annually, from which over 400 thousand people died. This Chapter classify malaria as a very important health, economic and social burden, providing general information about malaria’s impact on human, causes and symptoms, explain interactions between parasite-vector and human and how these processes are regulated by the control and eradication measures, economy, social, politics and what challenges we are still facing. The Chapter show that malaria extremely active in tropical and subtropical areas, is reaching into some temperate zones. The African region carries a disproportionately high share of the global malaria burden. Among extrinsic factors, economic and social conditions, poverty, environment (ecosystem, climate and weather), political commitment, control and prevention efforts and even behavioral customs are the most important determinants of malaria’s burden. Malaria control includes indoor and outdoor residual spraying, insecticide-treated nets, medicine, vaccination. Challenges facing malaria impacts on human include intensive population growth, lack of funding, increasing mosquitos’ resistance to insecticides and parasite resistance to drugs, insufficient surveillance, economic and social problem, climate and weather changes. Keywords  Malaria · Parasite · Mosquitoes · Humans · Environment · Weather stations · Climate · Weather

© Springer Nature Switzerland AG 2020 F. Kogan, Remote Sensing for Malaria, Springer Remote Sensing/ Photogrammetry, https://doi.org/10.1007/978-3-030-46020-4_2

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2.1  Introduction Malaria is a mosquito-borne infectious disease affecting humans and animals. It ranks among the major world health challenges, which affect people living mostly in the poorest countries of sub-Sahara Africa, Southeast Asia, Western Pacific and Latin America. Among 3.2 billion people living in these regions, 10–15% are at risk of malaria with up to one million deaths annually, mostly children under age five (WHO 2018, a, 2017, Caraballo 2014, Nizamuddin et al. 2013, a). According to the World Health Organization (WHO) between years 2010 and 2017, the number of world malaria-infected cases and death were over 200 million annually, from which over 400 thousand people died (Table 2.1). Although the number of malaria-infected people declined slightly from 2010, still in the most recent three years (2015, 2016 and 2017) they were gradually increasing. Moreover, in nearly 100 malaria-affected countries over 400,000 people died during 2015–2017 (Table 2.1), majority of dead are children (Caraballo 2014, WHO 2018, 2017a, 2016). In 2017, nearly 80% of all global malaria deaths occurred in 17, mainly African, countries and even 53% of all global malaria deaths were concentrated in 7 countries only: Nigeria (19%), Democratic Republic of the Congo (11%), Burkina Faso (6%), United Republic of Tanzania (5%), Sierra Leone (4%), Niger (4%) and India (4%). However, it is important to emphasize that the past 8-year (since 2010) declining trend is observed in the number of dead people (Table 2.1), indicating that malaria control and prevention measures are still working and should be continuing. Unfortunately, presented statistics is too short to make a reliable judgment if the new direction in malaria tendency has started. Following WHO (2018) report, the last eight-year funding for malaria has remained relatively stable, although the level of investment in 2017 is far from what is required to reach considerable decline in malaria rate (WHO 2018). Moreover, since the world population has been growing with an intensified rate (WHO 2017, 2016, Kogan 2018), the number of malaria-dead people supposed to be adjusted for such global tendency. Therefore, WHO evaluated malaria death rate per 100,000 population, which is shown in Fig.  2.1 (WHO 2018). Following this figure, the number of world malaria-death cases has practically not changed from 2010. It is known that the African region carries a disproportionately high share of the global malaria; for example, in 2017, the region contributed 92% malaria-affected people and 93% of malaria death cases to the world total (WHO 2018a). African region is showing nearly 26% decline tendency in death rate between 2010 and 2017 (Fig.  2.1a), indicating that the measures to control and prevent malaria in Africa provide positive results. The other four WHO’s malaria regions (Southeast Asia, Table 2.1  WHO’s Estimated World Malaria Cases (in millions) and death (in thousands) during 2010–2017 (WHO 2017a, 2018a) Data Malaria cases (x106) Malaria death (x103)

2010 239 591

2011 229 529

2012 226 487

2013 221 465

2014 217 459

2015 214 446

2016 217 445

2017 219 435

2.1 Introduction

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Fig. 2.1  Malaria mortality rate during 2010–2017: Number of dead people per 100,000 population at risk: (a) World and Africa region, (b) Other malaria regions (WHO 2018, a, 2017, a); Dashed line provides preliminary estimates for 2017 (WHO 2018)

Eastern Mediterranean, Western Pacific, and South Americas), shown in Fig. 2.1b, indicate no changes in death rate (per 100,000 population) except Southeast Asia, which indicates 30–35% decline in death rate up to 2013 and no change (stable death rate) thereafter (WHO 2018, a). Some population groups, such as children, especially infants and under 5 years of age, pregnant women and patients with HIV/AIDS, non-immune migrants, mobile populations and travelers are at considerably higher risk of contracting malaria, and developing severe consequences. Malaria imposes substantial costs to both individuals and governments. Costs to individuals and their families include purchase of drugs for treating malaria at home; hospital expenses, and treatment at dispensaries and clinics, lost days of work, absence of children from school, decreased productivity due to brain damage from cerebral malaria, expenses for preventive measures, expenses for burial in case of death (Greenwood et al. 2005). Costs to governments include maintenance, supply and staffing of health facilities, purchase of drugs and supplies, public health interventions against malaria, such as insecticide spraying or distribution of insecticide-treated bed nets, lost days of work with the following loss of income and lost opportunities for joint economic ventures and tourism. Direct costs (for example, illness, treatment, premature death) have been estimated to be at least US$ 12 billion per year. However, the cost in economic growth decline is much more. Poverty can increase substantially the risk of malaria since those people in poverty do not have the financial capacities to prevent the

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disease treat (CDC 2018, WHO 2018, CIA 1997). Malaria statistics shows its heavy burden in some countries, where it is responsible for 30–50% of hospital admissions, up to 50% of outpatient visits, and up to 40% of public health spending (Ricci 2012, Sabot et al. 2010). This Chapter classify malaria as a very important health, economic and social burden. In order to understand malaria as an adverse global phenomenon, the chapter provides general information about malaria’s impact on human, causes and symptoms, explain interactions between parasite-vector and human and how these processes are regulated by the control and eradication measures, economy, social, politics and what challenges are expected.

2.2  Malaria and Human 2.2.1  Malaria Cause Malaria is caused by Plasmodium protozoal-type parasitic single-cell microorganisms transmitted by an infected female Anopheles mosquito (WHO 2018a, CDC 2018, Wikipedia 2018, Catteruccia et al. 2003). Female mosquito bites since it need blood for reproduction. Biting, mosquito injects the parasites into a person’s blood (WHO 2018, 2018a, 2016, Caraballo 2014). The parasites travel to the liver where they mature and reproduce. Since the parasite is in a person’s blood, the disease can also be transmitted to other people through blood transfusions, organ transplant or using the same needle and syringe as a person who is infected (Bartoloni and Zammarchi 2012). Pregnant women can also pass the disease on to their child before or during birth (Caraballo 2014). In areas with high malaria transmission, children under five are particularly susceptible to infection, illness and death; nearly 70% of all malaria deaths occur in this age group (WHO 2018). Some population groups are at considerably higher risk of contracting malaria, and developing severe disease, than others. In addition to infants and children under five-year of age, pregnant women and patients with HIV/AIDS, as well as non-immune migrants, mobile populations and travelers are at the highest malaria risk (Caraballo 2014, Bartoloni and Zammarchi 2012). Most malaria cases and deaths occur in sub-Saharan Africa; the other WHO regions are Southeast Asia, Eastern Mediterranean, Western Pacific, and Latin America (WHO 2018). Although mosquitoes vector is spreading Plasmodium parasite, there are many factors associated with malaria occurrence, intensity, area and duration. Among them the most important those related to socioeconomic, ecosystem-climate-­ weather, population migration, health service, community participation, knowledge, practice, age, household size, occupational exposure, health resistance, education, vulnerability (individual risk) and others (Carrasquilla 2001, Hoek et  al. 1998). Economic activity is a very important factor since malaria is very intensive in the places with such activities as mining, lumbering, and fish farming, because working people are exposure to forest and open-water fishing sites. Additional malaria

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problem appears if the urban places located close to tropical plants, bushes and many creeks, which are forming pools along their beds. Migration provide additional source of increasing the number of malaria cases, since migrants who are often taking farming and wood cutting jobs, are normally living in the tents and even open air and do not have much protection against mosquito’s bites (Nieto et al. 1999, Mendez et al. 2000). Unfortunately, the rapid trend toward population increase and urbanization in developing countries will bring more malaria to large cities and will expose susceptible populations to outbreaks (WHO 2005, 2009, 2013). It should be taken into consideration that some population groups are at considerably higher risk of contracting malaria, and developing severe disease, than others. The most vulnerable are infants, children under five years of age, pregnant women and patients with HIV/AIDS, as well as non-immune migrants, mobile populations and travelers. Malaria prevention is important socioeconomic activities, which include health services and community practice to fight with malaria. Availability of medical facility inside villages to provide immediate help against the disease would reduce malaria death and spread. If medical care is not available in closed proximity the patients had to go to either to a far-located hospital or malaria eradication service facilities, which is not always possible. In such cases community leaders must actively participate in resource allocation for development of malaria-health facilities, promotion of spraying program, net for bed delivery etc. (Waltner-Toews and Wall 1997, WHO 2015, 2010, 1992). Another way of reducing malaria is spreading knowledge about malaria activity and symptoms, importance of using nets for bed (bednet) and other preventive measures (Carrasquilla 2001, Mendez et  al. 2000). The recent two decades global experience of fighting with malaria has showed that measures to prevent malaria can be easier to implement and more cost-effective in urban and pre-urban areas rather than in rural zones (Enayati and Hemingway 2010, van den Berg 2009, WHO 2006). Although malaria is curable, one of very specific features of the disease is that it can kill within hours, especially young children, sick and malnutrition people. Therefore, malaria epidemic causes almost one fifths of estimated annual worldwide deaths, accounting 4–5% of global fatalities (WHO 2018, 2017). Children, as was indicated, are the most vulnerable. Up to 50% of the estimated annual malaria mortality fall on persons less than 15 years of age. Being 5–9 years old, the author of this Book has lived in malaria-endemic Tajikistan (a part of USSR), and has had malaria several time, either relapsing or reinjected. I am remembering up to now these terrible almost two weeks suffering (before the taken medicine (called “akrihin”) starts working), when your body has 42 °C fever with an extreme chill and shivering; my mother covered me with all available blankets, which did not help to reduce incredible chill and shaking. In some parts of the world (Africa, Southeast Asia, South America etc.) there is barely a child who has not suffered from malaria by the time of his or her first birthday (Russel et al. 1963). Another very vulnerable for malaria mortality human category are pregnant women: nearly one million die annually (accounting 10% all maternal death (Hoek et al. 1998, Nieto et al. 1999)).

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2.2.2  Plasmodium and Mosquitoes There are five types of Plasmodium (P), which cause malaria in human and animals. For human these five parasites include P. falciparum (P.f.), P. vivax (P.v.), P. ovale (P.o.), P. malariae (P.m.) and P. knowlesi (P.k.) (Recht et al. 2017). The two of the strongest species, P. falciparum and P. vivax, pose the greatest human’s threat (Caraballo 2014). The other three species, P.o., P.m. and P.k, cause generally a milder form of malaria (WHO 2018, 2016). However, they also have some dangerous features for human. Thus, P. malariae is associated with renal complications if untreated; patients may remain parasitotic and asymptomatic for years; P. ovale is a relapsing species, found principally in Africa but rarely cause infection. Postmortem P. knowlesi findings have recently identified one fatality case similar to fatal P.f. malaria (Cox-Singh et al. 2010, Craig et al. 2004). Much more frequent malaria occurred from P. vivax, which is the dominant malaria parasite in most countries outside of sub-Saharan Africa, specifically, in Central and South America and Asia. P.vivax can be also relapsed months after an infection, it is associated with substantial morbidity but has fewer severe complications; recently, low birth weight was reported with placental infection by P. vivax (Yegorov et al. 2016). Meanwhile, the most virulent malaria is from P. falciparum, specifically on the African continent. Besides, it is responsible for most malaria-related deaths globally. Moreover, it is causing the most severe symptoms, which might lead to deaths, especially in young children and pregnant women. Evidence is accruing that genetic diversity of parasites is frequent within species, patients, and localities, probably because of recombination and selection, they have an impact on clinical presentation in various age groups and on malaria transmission (Kattenberg et al. 2011, Abba et al. 2014). This may account for a lack of protective immunity, frequently repeated infections and clinical episodes of malaria in persons, particularly young children, living in areas of intense and stable transmission. These features must be considered while vaccines are developing. There are about 400 species of Anopheles mosquitoes (called “malaria vectors.”). However, only 60 of them transmit malaria, and 30 are extremely dangerous as the transmitters (Kajfasz 2009, Montanari et al. 2001). From these thirty, two types, the Anopheles (An) gambiae and Anopheles funestus, are the most efficient vectors for transmission of P. falciparum. The An. gambiae has the highest rates of development and is widespread throughout tropical Africa. Transmission is directly proportional to the density of the vector, the number of human bites per day per mosquito, and the probability of the mosquito’s survival on daily basis (Kajfasz 2009). Mosquito longevity is particularly important, because the portion of the parasite’s life cycle that takes place within the mosquito—from gametocyte ingestion to subsequent inoculation—can take from 8 to 30 days, depending on ambient temperature. In general, sporogony within the mosquito is not completed at temperatures below 16–18 °C, and transmission does not occur. The entomologic inoculation rate (EIR) or the number of sporozoite-positive mosquito bites per year is the most common measure of malarial transmission; this varies from near zero in some areas

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21

of South America and Southeast Asia to greater than 300 in some parts of tropical Africa. A high inoculation rate results in intensive and stable transmission, especially to young children with severe illnesses. Higher EIRs are, in general, associated with increased frequency and density of parasitemia, febrile episodes, anemia and cerebral malaria in heavily endemic areas (Palmer 2012, Raghavendra et  al. 2011, Lengeler 2004). The lower the EIR, the greater the number of susceptible individuals who can get severe infection and develop illness (Raghavendra et  al. 2011, Lengeler 2004). Anopheles mosquitoes lay their eggs in water, which hatch into larvae and finally emerging as adult mosquitoes. In order to nurture their eggs, the female mosquitoes seek a blood meal. Each species of Anopheles mosquito has its own preferred aquatic habitat: some prefer small, shallow collections of fresh water, such as puddles and hoof prints, which are abundant during the rainy season in tropical countries (Abba et  al. 2014, Ansari and Razdan 2005, Ansari et  al. 1999, 1990). Transmission is more intense in places where the mosquito lifespan is longer (so that the parasite has time to complete its development inside the mosquito) and where it prefers to bite humans rather than animals. All of the important vector species bite between dusk and dawn. The intensity of mosquitoes’ bites and transmission depends on factors affecting parasite, vector, human host, and environment (Bhatt et al. 2015, Abba et al. 2014, Kajfasz 2009, Breman 2001, Ansari et al. 1999).

2.2.3  Malaria Cycle and Symptoms Malaria is an acute febrile illness, which in extreme cases can kill within 24 hours of symptom onset (Bhatt et al. 2015, Texier et al. 2013). Malaria is transmitted by infected adult female mosquitoes that bite to get blood for laying eggs. The mosquito hatching period from laying eggs to an adult stage is between 7 and 15 days. An entire cycle, when the female mosquito is able to bite, transmit the parasite and malaria was recorded to 15–50  days (Pampana 1969, Boe¨te and Koella 2002). Therefore, during April to October, four to five cycles of mosquito population are able to transmit malaria. The incubation period for development of malaria after infected mosquito bites is between 8 and 35 days. Following the infective bite by the Anopheles mosquito, some incubation time goes by before the first symptoms appear. The shorter incubation periods are observed most frequently with P. falciparum and the longer ones with P. malariae. Antimalarial drugs taken for prophylaxis by travelers can delay the appearance of malaria symptoms by weeks or even months, long after the traveler has left the malaria-endemic area. This can happen particularly with P. vivax and P. ovale, both of which can produce dormant liver stage parasites. Fever, chill, headaches, tiredness and flu-like illness are typical malaria symptoms, which start one-two weeks (in some cases up to one month) after the mosquito bite and inject the parasite. More severe symptoms include fatigued, nausea, vomiting and even diarrhea. As the disease intensifies, the extreme symptoms might include seizures, breathing problems, liver or kidney failure,

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anemia, confusion, cardiovascular issues, brain swelling, unconsciousness, coma and even death (Bartoloni and Zammarchi 2012, Beare et al. 2006). If not properly treated, people may have recurrences of the disease months later. For those people who have recently survived an infection, reinfection usually causes milder symptoms. This partial resistance disappears over months to years if the person has no continuing exposure to malaria. Children with severe malaria frequently develop one or more of the following symptoms: severe anemia, respiratory distress in relation to metabolic acidosis, or cerebral malaria (Bardají et al. 2012). In adults, multi-­ organ involvement is also frequent. In malaria endemic areas, people may develop partial immunity, allowing asymptomatic infections to occur. Neurologic defects may occasionally persist following cerebral malaria, especially in children. Such defects include trouble with movements (ataxia), palsies, speech difficulties, deafness, and blindness. Recurrent infections with P. falciparum may result in severe anemia, especially, in young children of tropical Africa with frequent infections that are inadequately treated. Malaria during a women pregnancy (especially from P. falciparum) may cause severe disease in the mother, and may lead to premature delivery or delivery of a low-birth-weight baby (Beare et al. 2006). On rare occasions, P. vivax malaria can cause rupture of the spleen. Nephrotic syndrome (a chronic, severe kidney disease) can result from chronic or repeated infections with P. malariae. Hyperactive malarial splenomegaly (also called “tropical splenomegaly syndrome”) occurs infrequently and is attributed to an abnormal immune response to repeated malarial infections (Bartoloni and Zammarchi 2012). The disease is marked by a very enlarged spleen and liver, abnormal immunologic findings, anemia, and a susceptibility to other infections (such as skin or respiratory infections).

2.2.4  Malaria Distribution Ecology and climate are two principle factors determining long-term malaria distribution on the Earth. Therefore, malaria is extremely active in tropical and subtropical areas. However, malaria’s furthest influence reaches into some temperate zones (southern Europe) that have extreme seasonal changes. In addition, since malaria is commonly associated with poverty and in some cases is causing poverty and a major hindrance to economic development (Worrall et al. 2005), some countries out of tropical ecosystems, might be strongly affected by malaria. For example, during the late 19th and early 20th centuries, malaria was a major factor in the slow economic development of the American southern states (Humphreys 2001). A very interesting fact, malaria is so important world problem that different publications indicates practically the same Earth’s malaria- affected areas and countries (Fig. 2.2). Following these maps, Africa is the most affected continent where a very efficient mosquito responsible for high transmission is Anopheles gambiae. The predominant parasite species is Plasmodium falciparum, which causes severe malaria and death. Local weather conditions often allow transmission to occur year-round (CDC 2018). Besides, scarce African resources and socio-economic instability have

2.2  Malaria and Human

23

Fig. 2.2  Malaria-affected world areas and countries (a) – Malaria transmission (CDC 2018), (b) – Stable-Unstable malaria (WHO 2010), (c) – Malaria per 1000 population (PP 2015), (d) - Malaria pre-elimination, control and prevention (WHO 2009)

hindered efficient malaria control activities. In other areas of the world malaria is a less prominent cause of deaths, but can cause substantial disease and incapacitation, especially in rural areas of some countries in South America, Central America and South Asia (WHO 2018, a, 2017, a, 2009, 2008, 2005, Elias and Rahman 1987). Global malaria is studied quite well, which resulted in development of measures for prevention and eradication the disease. The major contribution to this task was due to the efforts of the World Health Organization (WHO) and the health services of malaria-affected countries (WHO 1992, 2005, 2010, 2015, 2018, Van den Berg 2009, Raghavendra et al. 2011, Nieto et al. 1999, Williams 1963). Following these efforts, the distribution of malaria around the world was very well represented from different aspects shown in Fig. 2.2. Based on the most recent map form US-based Center for Disease Control (CDC 2018) the sub-Sahara Africa has the largest malaria area, followed by southern Asia and northern half of South America (Fig. 2.2 a). In the last two continents, permanent malaria area (brown zone) is relatively small compared to malaria general distribution (yellow zone). The older map of malaria distribution in Fig. 2.2b (WHO 2010) indicates that a stable malaria area in the past was larger compared to the most recent information (Fig. 2.2a CDC 2018), emphasizing some progress in fighting malaria eradication. This progress is clearly seen (light green color in Fig.  2.2b) in south-east of USA, east-central South America, southern Europe, southeast Asia and southern part of former USSR.  A very interesting map in Fig. 2.2c is showing malaria distribution per 1000 population, indicating how dangerous is malaria for a growing population. Again, sub-­ Sahara Africa is the most malaria-affected region, having more than 50 people with malaria for each 1000 population based on the most recent years (Fig. 2.2c, dark

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brown color). North-eastern South America is also strongly affected having more than 50 people (per 1000 population) with malaria. However, southeast Asia has less than 50 people with malaria, except for the countries on the islands with large population. The map (d) in Fig. 2.2 (WHO 2009) is also interesting, showing not only areas where malaria is under control (Africa, Southeast Asia and South America), but also countries with strong direction towards “pre-elimination” (southern South America, Mexico, and some countries in the Middle East). Meanwhile in some regions, typical malaria transmission is seasonal, with the peak either during or just after the rainy season (Olson et  al. 2009, Tanser et  al. 2003). Malaria epidemics can occur when climate and other conditions suddenly favored transmission in areas where people have little or no immunity to malaria. They can also occur when people with low immunity move into areas with intense malaria transmission, for instance to find work, or as refugees (WHO 2018). Besides the principal malaria regions, some smaller areas in northern Australia, southern USA and southern Europe have also had subtropical-type climate with potential for malaria development. However, these regions have practically eradicated malaria and if some cases of malaria might appear, they are distributed by travelers getting disease visiting malaria-endemic areas. Fortunately, malaria is quickly eliminated from the medical treatments. Following the indicated mosquitoes’ requirements to water and thermal conditions, human engineering projects, such as construction of dams, small lakes, roads, and industrial or residential centers, can result in disruption of the terrain, allowing increased mosquito breeding. Geographic areas where populations are susceptible to epidemics are of particular importance; these zones are often inundated by ecosystems, unseasonal rains, influxes of migrants and refugees, and breakdowns of malaria and other disease control and prevention programs. Epidemics, linked to rainfall, temperature, geography, and, above all, population susceptibility and unresponsiveness to incipient epidemics, will become more frequent (Enayati and Hemingway 2010, van den Berg 2009, WHO 2006). The rapid trend toward urbanization in developing countries will bring more malaria to large cities and will expose susceptible populations to disease outbreaks. Measures to prevent malaria can be easier to implement and more cost-effective in urban and pre-urban areas than in rural zones. Some population groups are at considerably higher risk of contracting malaria, and developing severe disease, than others. These include infants, children under five years of age, pregnant women and patients with HIV/AIDS, as well as non-immune migrants, mobile populations and travelers. National malaria control programs need to take special measures to protect these population groups from malaria infection, taking into consideration their specific circumstances. Children with severe malaria frequently develop one or more of the following symptoms: severe anemia, respiratory distress in relation to metabolic acidosis, or cerebral malaria. In adults, multi-organ involvement is also frequent. In malaria endemic areas, people may develop partial immunity, allowing asymptomatic infections to occur (WHO 2018).

2.3  Malaria Burden

25

2.3  Malaria Burden Malaria has been always a huge economic, social and environmental burden (Gallup and Sachs 2001, Breman 2001, Faiz et al. 2002). Among extrinsic factors, economic and social conditions (poverty, hunger, low medical services etc.), environment (ecosystem, climate and weather), political commitment, effectiveness of control and prevention efforts and even behavioral customs are the most important determinants of malaria’s burden. Therefore, it should be emphasized that malaria burden, in relation to impacts on human, is multi-factorial process, controlled by long- and short-term conditions. Principally, short-term conditions in 90% cases are presented by a changing weather (Kogan 2018, Nizamudding et  al. 2013, a, Rahman et  al. 2006), which will be presented and explained in the next chapters. This chapter’s discussion is associated only with long-term cases, presented by Fig. 2.3. According to the scheme, long-term malaria burden is regulated by the three groups: socio-­ economic and political factors, effectiveness of malaria control and preventive measures and also climate and ecosystem impacts. A contribution of socio-economic, political and behavioral factors into malaria-fighting process depends on the amount of investments to control and reduce malaria in the society. It is well-known that malaria transmission is associated with poverty and in turn the cause of poverty (Worrall et al. 2005, Humphreys 2001). During 1965–1990, GDP of the countries with intensive malaria increased 0.4% per year, compared to 2.4% in no malaria countries (Nabarro and Taylor 1998). Such situation has continued after 1990, when countries with intensive malaria in 1995 had five-time smaller their Gross Domestic Product (GDP) compared to the countries without the disease (Sachs and Malaney 2002). In most of sub-Sahara Africa, malaria might be responsible for 30–50% of hospital admissions, up to 50% of outpatient visits, and up to 40% of public health Fig. 2.3 Scheme summarizing long-term environmental factors controlling interaction between human, malaria-­ infected mosquito and parasite

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spending (UOG 2018, WHO 2013, Gallup and Sachs 2001, AEZ 2018). On Asian continent, India, the country with the greatest number of poor people in the world, has a very serious malaria problem. In the Western Hemisphere, Haiti has the worst malaria situation being the poorest country in the hemisphere (Gallup and Sachs 2001). Unfortunately, this situation has not been improved in the twentieth century (WHO 2018, 2017, CDC 2018, UOG 2018, Sabot et  al. 2010). Economic and behavioral situation are the major problem for low effectiveness of control and prevention malaria measures (Howitt et  al. 2012, Gallup and Sachs 2001, Kajfasz 2009, Lengeler 2004). As was mentioned, malaria is confined mostly to the tropical and sub-tropical zones, that is a broad band area around the Equator, which includes sub-Sahara Africa, South East Asia, South and Central America and Oceania. Moreover, in these areas, not only population is the subject of malaria disease, but also people who travel to these areas of the world should take precautions to protect themselves from contracting the disease (WHO 2018, 2013, 2010, 2005). At the same time, it is important to indicate that in many malaria-endemic countries, within tropical and sub-tropical areas, malaria transmission does not occur in all parts of the country. The less (or not) affected areas include very high-altitude places, deserts (excluding the oases), colder seasons and areas where transmission has been interrupted through successful control or elimination programs. On the opposite side, in warmer regions closer to the Equator, malaria transmission is normally more intensive and continue year-round. The highest transmission is found in the countries of sub-­ Sahara Africa and Southeast Asia (India). An elevated transmission is also known in South America and in parts of Oceania, such as Papua New Guinea (WHO 2013). In cooler regions, transmission will be less intense and more seasonal. There, P. vivax type malaria might be more prevalent because it is more tolerant to lower air temperatures. In many temperate areas, such as western Europe and the United States, economic development and public health measures have succeeded in eliminating malaria. However, most of these areas have Anopheles mosquitoes that is resistant to prevention measures of malaria transmission, and reintroduction of the disease is a constant risk. Although tropical regions are the most affected, malaria influence reaches into some temperate zones (adjusted to tropics) that have extreme seasonal changes. More than 70-year ago, malaria has penetrated from purely tropical regions to the north and south (Fig. 2.4, 1946), affecting such countries as Italy, southern France, Spain, Portugal, Greece, southern USA, southern-central former USSR, north Australia and a few others (Gallup and Sachs 2001). Since mid-1960s (Fig.  2.4, 1965), most of these countries, are practically free (or have a negligible malaria transmission, mostly from infected travelers), following an improvement in medicine and also social, economic, behavioral and even political situation (WHO 2018, 2005, CDC 2018, Gallup and Sachs 2001). This was also the results of improvement in general hygiene and public health, recreational activities following filling in swamps, drainage ditches and other mosquito breeding sites, screening windows and doors, use of air conditioning, and availability of rapid diagnostic methods and new very effective drugs to treat illnesses (Gallup and Sachs 2001). Plus, alleviation of poverty, economic and educational gains, along with the ability to manufacture,

2.3  Malaria Burden

27

Fig. 2.4  World malaria risk area changes between 1946, 1965 and 2005 (Gallup and Sachs 2001) Fig. 2.5  Number of malaria cases in the tropical South America’s countries

purchase, and deploy effectively insecticides, have had the greatest impact on a reduction of malaria burden in those countries. Besides, socioeconomics, political conditions sometime play a very huge role in spreading malaria. One of the best current example is Venezuela’ humanitarian crisis resurging vector-born malaria (Recht et al. 2017). Figure 2.5 shows that at the background of stable 5-year malaria conditions in four other South American’s tropical countries during 2011–2015, the number of malaria-affected people in Venezuela by 2015 increase almost three times (from 9 to 25 per 1000 population) compared to 2011 following humanitarian crisis. Unfortunately, in the currently malaria-affected countries, socio-economic status may not reflect access to or use of malarial control or prevention measures or may reflect the relative ineffectiveness of those methods (WHO 2018, UOG 2018, CDC

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2018). However, in some poor countries, where the health service delivery systems have been not appropriate for malaria control and other diseases, vector control has had limited success in heavily endemic areas, especially in Africa, northern half of South America and India (CDC 2018, Gallup and Sachs 2001). Indeed, outside of a few urban centers and research projects, vector control has not been a major strategy, with the exception of a few areas in southern Africa (Howitt et al. 2012, Noor et al. 2009, Kajfasz 2009.). There is increasing interest in attacking the mosquito both with classical vector control technologies to prevent and control epidemics and with insecticide-impregnated bed nets and other materials for personal protection (Howitt et al. 2012, Miller et al. 2007, Noor et al. 2009, Perkins and Bell 2008). With the realization that the current drug-use strategy alone will have a minimal impact on transmission and limited success in decreasing the malaria burden, newer vector-focused approaches are needed.

2.4  Roll Back Malaria Although malaria is a deadly disease, illness and death from malaria can usually be prevented (WHO 2014, 2018). Roll back malaria (RBM) is extremely important to reduce the number of affected people (Nabarro and Taylor 1998). A success in the RBM depends on the principal of malaria presence in an area. Normally, the presence of malaria in an area requires a set of conditions, including a combination of high human population density, high mosquito population density and high rates of parasite transmission from humans to mosquitoes and from mosquitoes to humans. If any of these conditions are lowered sufficiently, or even eliminated, the parasite and malaria would eventually disappear from that area, as this has happened a few decades ago in southern North America, southern Europe, parts of the Middle East and some other small places of malaria distribution (Gallup and Sachs 2001). However, unless the parasite is eliminated complete, malaria could become re-­ established if conditions revert to such combination that favors the parasite’s reproduction. In addition to parasite, mosquitoes should be eliminated as well. Meanwhile, the cost of mosquitoes’ elimination rises with decreasing population density, making it economically not efficient for poor countries (Bardají et al. 2012, Raghavendra et al. 2011, Castro et al. 2004). But in the long run, malaria prevention may be more cost-effective way than treatment of the disease (Bardají et  al. 2012, Gallup and Sachs 2001). However, the initial individual costs required for prevention is out of reach for many of the world’s poorest people (Howitt et al. 2012, Raghavendra et al. 2011). There is a huge difference in the costs of control and elimination programs between countries. For example, in China, whose government in 2010 announced a strategy to pursue malaria elimination in the their southern provinces, the required investment was a small proportion of public expenditure on health. In contrast, a similar program in Tanzania would cost an estimated one-fifth of the public health budget (Raghavendra et  al. 2011, Sabot et  al. 2010). The goal of most current National Malaria Control Programs and most malaria activities is to reduce the

2.4  Roll Back Malaria

29

number of malaria-related cases and deaths. To reduce malaria transmission to a level where it is no longer a public health problem is the goal of what is called malaria “control” (CDC 2018). The methods used to fight malaria include treatment of the disease, mosquito control and elimination, preventing bites, malaria prevention and eradication, elimination of poverty and malnutrition, economic investments, improving social conditions, education and others (Al-Taiar et  al. 2009, Ansari et al. 1990, 1999, 2005, Barat 2006, Castro et al. 2004, Catteruccia et al. 2003, Alonso and Noor 2017).

2.4.1  Indoor Residual Spraying Of the different strategies for vector control, the most successful are indoor residual spraying (IRS) and insecticide-treated nets (ITN), especially from long-lasting materials. During the Global Malaria Eradication Campaign (1955–1969), DDT (dichloro-diphenyl-trichloroethane chemical compound) was the primary malaria control method. This campaign has eliminated malaria from several areas and sharply reduced the burden of malaria disease in others (CDC 2018). Specifically, in southern USA and Europe, DDT spray has shown good success in decimating disease vectors. However, DDT has not achieved its stated objective. Besides, the DDT was banned, causing human health problem and stimulating development of insecticide resistant mosquitoes (Ansari and Razdan 2005). The recent success of IRS in reducing malaria cases in South Africa by more than 80% from similar to DDT component has revived interest in this malaria prevention tool. Few other vector controls, using space spray, biological control agents, etc., were successful when used on a limited scale (Barat 2006). Another important ITN method is using the insecticide-treated net (Van den Berg 2009). Between 2000 and 2008, the use of indoor treated net (ITN) saved the lives of an estimated 250,000 infants in Sub-­ Saharan Africa (Howitt et al. 2012). In 2007 and 2008, about 13 and 31%, respectively, of households in sub-Saharan countries used ITNs (Noor et al. 2009, Miller et  al. 2007). In 2000, 1.7 million (1.8%) African children living in areas of the world where malaria is common were protected by an ITN. That number increased to 20.3 million accounting for 18.5% African children protected in 2007. However, 89.6 million African children still became unprotected in 2007. By 2015, the control situation improved, since 68% African children have been using mosquito nets (WHO 2015b). Most nets are long-lasting insecticide-treated nets (LLINs) that maintain effective levels of insecticide for at least 3  years, even after repeated washing and with low toxicity insecticides. Between 2008 and 2010, 294 million LLIN nets were distributed in sub-Saharan Africa (WHO 2018a, CDC 2018). The LLINs are most effective when used from dusk to dawn. It is recommended to hang a large “bed net” above the center of a bed and either tuck the edges under the mattress or make sure it is large enough such that it touches the ground (WHO 2015b, Howitt et al. 2012).

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2.4.2  Larval Control and Other Vector Control Interventions For a small-scale malaria prevention in Africa and other malaria-affected areas, larval mosquito control has achieved some success. This method was appropriate for such settings as urban environments or desert fringe areas where habitats are more stable and predictable. WHO (2015) has recently recommended larval control for areas where the larval habitats are small, stable, and easy to find (WHO 2018, 2015, 2013, 2010). Oils are normally spread on the water surface to suffocate larvae and pupae (WHO 2010). The used oils are rapidly biodegraded. The other compound is the toxins from the bacterium (Bacillus thuringiensis israelensis), which are applied similar to insecticides. These methods are affecting only mosquitoes, black flies, and midges. Other two approaches for larva control are fogging and area spraying, which are used in case of epidemics. Fogging and spraying must be timed with the time of peak adult mosquito activity, because resting mosquitoes are often found in areas that are difficult for the insecticide to reach (e.g., under leaves etc.). In addition, fogging and area spraying will have to be repeatedly applied to have an impact, and it can easily become too costly to maintain or result in the overuse of insecticides (WHO 2018a, 2017).

2.4.3  Malaria Prevention Malaria prevention is specified human activities to interrupt local transmission of specified malaria parasites in a certain geographical area (WHO 2015, 2010). These are continued measures to prevent re-establishment of malaria transmission. Two methods are normally used: population treatment and vector control. Population treatments relies completely on medications to prevent malaria where the disease is common (Caraballo 2014). In some geographic areas, entire population is treated with a curative dose of an antimalarial drug (so called “mass drug administration”, MDA) without testing for infection and symptoms. While MDA may result in a short-term malaria reduction, the negative effect is development of parasites’ drug resistance. (WHO 2018a). Occasionally, some doses of the combined medications are recommended for infants and pregnant woman to prevent the disease in the areas with high malaria rates. The most recommended vaccine treatment for malaria include such drugs as artemisinin, mefloquine, lumefantrine, (or sulfadoxine/pyrimethamine), chloroquine and quinine (WHO 2010, Caraballo 2014). Unfortunately, malaria vaccines are medium-effected, despite an intensive medical efforts to develop a very effective one (Caraballo 2014). Besides, over the years, malaria parasite develops some resistance to drugs. For example, P. falciparum, the most widely spread malaria has become resistant to artemisinin that is currently a problem in some parts of Southeast Asia; in other areas, the parasite become resistant to chloroquine (Caraballo 2014). Some problem with malaria prevention is development of human immunity, especially among adults in the areas of moderate or intense

2.5  Malaria Eradication

31

malaria transmission. Partial immunity is developing over years of exposure to malaria. While it never provides complete protection, it does reduce the risk that malaria infection will cause severe disease. For this reason, most malaria deaths in Africa occur in young children, whereas in areas with less transmission and low immunity, all age groups are at risk (WHO 2018). With the development of drug-­ resistant problem, new strategies have recently appeared to combat malaria. One of such approaches is development of antimalarial drugs using synthetic metal-based complexes (pyridoxal-amino acid adducts (Roux and Biot 2012)). However, since the drug is relatively new, the result of combat malaria has not known yet. Another, vector control method is the main way to prevent and reduce malaria transmission from vector activity. As was indicated, mosquito bites can be prevented through the use of mosquito nets and insect repellents, spraying insecticides and others. Two forms of vector control are widely used and effective: insecticide-treated mosquito nets and indoor spraying insecticides (Caraballo 2014, Van den Berg H. 2009). Indoor spraying is generally effective for 3–6 months, depending on the insecticide formula used and the type of surface on which it is sprayed. Meanwhile, for high efficiency the treatment must be done right. Specifically, it should be intensive, cover large area and many houses. For example, in some settings, multiple outside spraying is required to protect the population for the entire malaria season. In case of indoor treatment, high effectiveness is achieved when not less than 80% of houses are sprayed (WHO 2018, 2015, 2010, Caraballo 2014). The risk of malaria can be reduced by draining standing water, which provides mosquito-breading habitat (WHO 2018, 2017, 2005).

2.5  Malaria Eradication Eradication or elimination of malaria is extremely important task to improve human life. Eradication is more intensive and much costliest activity. Meanwhile, malaria has been successfully eliminated or greatly reduced in certain areas. Malaria was once common in the southern United States and southern Europe and other areas, but vector control programs, in conjunction with the monitoring and treatment of infected humans, eliminated it from those regions. A few intensive measures such as draining of wetland (breeding mosquitoes’ grounds), changes in water management practices, advances in sanitation, using glass windows and screens in dwellings, use of the pesticide, (including DDT), vaccine etc. As the result, malaria was eliminated from most parts of the USA in the late 19th and early twentieth century and from other areas (southern Europe, former USSR, China) shown in Fig.  4 (Williams 1963). Some malaria reduction was reported in Brazil, Eritrea, India, and Vietnam (Barat 2006). Meanwhile, malaria eradication in poor countries is more difficult task, although several global actions (“Malaria No More”, “The Global Fund to Fight AIDS, Tuberculosis and Malaria”, “Clinton Foundation”, “Malaria Policy Advisory Committee”, “Malaria Atlas Project” and others) have being established (WHO 2018, 2013, GF 2012, Strom 2011, Schoofs 2008, Guerra et al. 2007, PP

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2015). Therefore, these measures are mostly local or regional in scope. Some publications expressed an opinion that global eradication cannot be achieved until malaria is gone from the natural world (Caraballo 2014, GF 2012, Strom 2011, Schoofs 2008, Shiv Lal et al. 2010). Recent increases in resources, political will, and commitment have led to discussion of the possibility of malaria elimination and, ultimately, eradication (CDC 2018, WHO 2018, 2018a). Eradication should start form a very though malaria control. However, the areas where malaria is the largest burden (Africa, southeast Asia etc.), it has been extremely difficult to control malaria. Many reasons account for this: difficulties to find mosquitos that transmits the infection, a high prevalence of the deadliest species of the parasite, favorable ecosystem and climate, weak infrastructure to address the disease, and a very high intervention costs that are difficult to bear in poor countries. However, the scale-up of effective, safe, and proven prevention and control interventions made possible by global support and national commitment has shown that the impact of malaria on residents of malaria-endemic countries can be dramatically reduced when these are used together. Problems with malaria eradication. The development of P. falciparum resistance to drugs over the past 20-year has been a major cause of poor malaria program performance and increasing burden; resistance to chloroquine is widespread, multidrug resistance is increasing, and new strategies are being developed for their use (Perkins and Bell 2008, Sabot et al. 2010). Since 1980, following the demise of global malaria eradication, use of effective drugs to reduce mortality and morbidity has been the major strategy for malaria control, particularly in Africa (Kajfasz 2009). The increasing malaria burden reflects the inadequacy of this current strategy, which may become further compromised by the projected increasingly widespread use of sulfamethoxazole-trimethoprim as prophylaxis for patients with human immunodeficiency virus and acquired immune deficiency syndrome (Palmer 2012, Kajfasz 2009). Resistance to sulfamethoxazole-trimethoprim may cross-react with a major antimalarial drug and vice versa (Palmer 2012). This would have ominous implications, because sulfadoxine-pyrimethamine is becoming a cost-effective first-line therapy in eastern and southern Africa (Kajfasz 2009, Breman 2001). Because the health service delivery systems have been so poor for malaria control and other diseases, vector control has had limited success in heavily endemic countries, especially in Africa; indeed, outside of a few urban centers and research projects, vector control has not been a major strategy, with the exception of a few areas in southern Africa. There is increasing interest in attacking the mosquito both with classical vector control technologies to prevent and control epidemics and with insecticide-­impregnated bed nets and other materials for personal protection (Howitt et al. 2012, Miller et al. 2007, Noor et al. 2009), With the realization that the current drug-use strategy alone will have a minimal impact on transmission and limited success in decreasing the malaria burden, newer vectorfocused approaches are needed.

2.6  Malaria Challenges

33

2.6  Malaria Challenges Although a very considerable success has been achieved over the recent 40–50 years in drug production to treat malaria, success of vaccine and insecticides to treat mosquito habitats, stable funding for malaria prevention and eradication etc., there are many challenges facing malaria impacts on humans and malaria still continues to be the major public health problem, especially in poor countries of sub-Sahara Africa, Southeast Asia and South America (Raghavendra et  al. 2011). Most of the challenges are presented in (Table 2.2). Following this table, it is clear that malaria has continued to be a huge world problem human must continue fighting with the disease. First of all, we should indicate that one of the most important challenges to fight malaria is extensively large number of people (nearly 3 billion) on a large area (nearly a quarter of the world) are affected. In addition, this number is permanently increasing due to the intensified long-term tendency of world population growth, especially in poor countries (WHO 2017, 2016, Kogan 2018). Malaria is a very specific disease since, people of all ages are affected and need to be treated; but a lot of additional efforts are required to save young children, pregnant women and millions of hungry people. The next big challenge is mosquitoes and parasite have become resistant to insecticides and drugs, following a long period of their use and many technical and administrative reasons, including poor or no adoption of Table 2.2  Challenges facing malaria impacts on human

Malaria can be transmitted to people of all ages Children under age 5, pregnant women, hungry people are the most vulnerable Increasing mosquitos’ resistance to insecticides Increasing parasite resistance to drugs (undermine malaria control) Changes in mosquitos’ behavior due to continuous treatments Changes in ecology due to human activities Diminishing number of effective insecticides Limited types of chemicals for vector treatment (DDT and pyrethroids) Limited treatments and impact Intensive population growth Climate changes (increase in weather extreme) Economic and social problem Surveillance problem Weather

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alternative tools (CDC 2018, WHO 2018). Besides, limited malaria treatments outside of cities, due to specific ecological conditions, limited and ineffective chemicals for vector treatment (for example DDT), economic and social problem, ineffective surveillance system (tracking of the disease and programmatic responses, and taking action based on the data received), antimalarial drug resistance, early diagnosis and treatment of malaria and even specific human activities are creating huge challenges to fight malaria (Table 2.2). In 2010–2016 the resistance to the four commonly used insecticides was widespread in all major malaria vectors across the WHO regions of Africa, the Americas, South-East Asia, the Eastern Mediterranean and the Western Pacific (WHO 2018). Therefore, in spite of all measures, malaria has continued to rise in countries with the highest burden of the disease, especially on a huge area of tropical/subtropical ecosystems supporting mosquito/parasite activities for spreading malaria and creating problem with efficient fighting. This is supported by the 2017 publications, indicating that by 2016 the fight against malaria has stalled (Alonso and Noor 2017). Currently, WHO (2018) is working with the governments of 21 the most malaria-­ affected countries to eliminate malaria by the year 2020 (“E-2020 countries” project). Although 10 E-2020 countries remain on track to achieve their goals, the other 10 have reported increase in indigenous malaria cases in 2017 compared to 2016 (WHO 2018). They included United Republic of Tanzania, Uganda, Ghana, Mozambique, Democratic Republic of the Congo, Niger, Mali, Nigeria, Cameroon and Burkina Faso (WHO 2018, Alonso and Noor 2017). In 2017, these 10 countries, plus India accounted for nearly 70% of estimated malaria cases and deaths globally. India was one of them in south east Asia and ten were in sub-Saharan Africa. Only India has reported some progress in reducing the number of malaria cases in 2017 compared to 2016 (WHO 2018), although, it was not examined if the unfavorable weather contributed to this reduction. Unfortunately, inadequate international and domestic funding poses serious threat to malaria fights progress following population increase, parasite resistance to antimalarial medicines, mosquito resistance to insecticides, economic and social problem, human tradition and others. Thus, in 24 out of 41 high-burden malaria countries, which rely mainly on external funding to fight malaria, the average level of funding available per person at risk declined in 2015–2017 compared to 2012–2014 (WHO 2018). The international malaria funds use all measures to remain malaria fund stable. However, with intensive population growth and the emergence of resilient transmission patterns, funding per capita population at risk has declined over the 2015–2017 compared with the previous 3 years, especially in the highest burden countries. In 2017, USA has been the largest single international donor for malaria, providing $1.2 billion, which is 39% of global funding (WHO 2018). This amount is not sufficient for eradication, but even for controlling malaria. Finally, two very important and specific challenges, where malaria funding has limited impacts, are contribution of climate and weather changes to malaria vulnerability. Since the mid-eighteenth century, Earth climate has been warming up. From the nineteenth century, this process has intensified, especially since the late-1970s, when by the turn of the century, global temperature anomaly increased around

2.7 Summary

35

0.5 °C (NASA 2017, NOAA 2017, WHO 2014, IPCC 2014). Earth climate warming has been reported to speed up ice melting in the northern pole, sea level rising, produce changes in biological systems (plants, birds, insects etc.), increase water scarcity and drought frequency, deteriorated agricultural system and other changes. One of very unfavorable consequences of the recent climate warming was intensification of weather extremes. Since malaria is confined mostly to the tropical and subtropical zones (broad band area around the Equator (AEZ 2018)) with warm and wet climate, increasing climate extremes might increase or decrease the number of affected people depending on intensity and area of climate changes. The tendencies of climate warming impacts on human life in the recent four decades have been still discussing in the scientific publications (Kogan 2018, Ward 2016). Since most of the discussions avoid malaria problem, the contribution of warmer Earth to malaria changes would be thoroughly analyzed with the land data in Chap. 8. Compared to long-term climate tendency, weather impacts on malaria is short-term, continuing from days to weeks, and months up to one year. Since mosquitoes require warm and wet conditions for their highest activity (Abba et al. 2014), it should be expected that a lack of rainfall, or hot temperature, or their combination might reduce their ability to transfer malaria and decrease the number of malaria-affected people. In case of the opposite weather conditions (wetter and cooler), mosquitoes might intensify malaria transmission, increasing the number of affected people. In such situation, weather might be a good estimator of malaria area and intensity and can be used as an advanced predictor of the number of people potentially able to get malaria. Analysis of scientific publications dealing with weather impacts on malaria is presented in the next Chapter.

2.7  Summary In the summary of tis Chapter it is important to emphasize briefly again: • Malaria is a life-threatening disease caused by parasites (Plasmodium) that are transmitted to people through the bites of infected female Anopheles mosquitoes; there are five types of Plasmodium, which cause malaria in human and animals. • Malaria epidemic causes almost one fifths of estimated annual worldwide deaths, accounting 4–5% of global fatalities • In 2017, there were an estimated 219 million cases of malaria in 90 countries with 435,000 death. • Malaria affects people living mostly in the poorest countries of sub-Sahara Africa, Southeast Asia, Western Pacific and Latin America • Children, especially infants and under 5  years of age, pregnant women and patients with HIV/AIDS, malnutrition and hungry people, non-immune migrants, mobile populations and travelers are at considerably higher risk of contracting malaria, and developing severe illnesses.

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• Although malaria is curable, the disease can kill within hours, especially young children, sick and malnutrition people. • Malaria is extremely active in tropical and subtropical areas; however, the furthest influence reaches into some temperate zones The African Region carries a disproportionately high share of the global malaria burden. In 2016, the region was home to 90% of malaria cases and 91% of malaria deaths. • The last eight-year funding for malaria has remained relatively stable, although the level of investment in 2017 is far from what is required to reach considerable malaria’s decline (WHO 2018). In 2016, total funding for malaria control and elimination reached an estimated US$ 2.7 billion; contributions from governments of endemic countries amounted to US$ 800 million, representing 31% of total funding (WHO 2018). • Among extrinsic factors, economic and social conditions, poverty, environment (ecosystem, climate and weather), political commitment, control and prevention efforts and even behavioral customs are the most important determinants of malaria’s burden. • Malaria control includes indoor and outdoor residual spraying, insecticide-­ treated nets, medicine, vaccination. • Malaria was eliminated from the southern USA and Europe, a part of USSR and China; some malaria reduction was reported in Brazil, Eritrea, India, and Vietnam • For the areas where malaria is the largest burden (Africa and Southeast Asia), it has been extremely difficult both to control and eradicate malaria. • Challenges facing malaria impacts on human include: intensive population growth, lack of funding, increasing mosquitos’ resistance to insecticides and parasite resistance to drugs, insufficient surveillance, economic, political and social problem, climate and weather changes. • Climate and weather information can be used to predict malaria development and extension • Since weather station network is very limited, high-resolution (1.0 and 4.0 km2) satellite-based Vegetation Health method and data has been serving as an excellent tool to predict and monitor malaria.

References Abba, K., Kirkham, A., Piero, O. L., Deeks, J. J., Donegan, S., Garner, P., & Takwoingi, Y. (2014). Rapid diagnostic tests for diagnosing uncomplicated non-falciparum or Plasmodium vivax malaria in endemic countries. Cochrane Database of Systematic Reviews, 12. https://doi. org/10.1002/14651858.cd011431. AEZ (2018). Agro-ecological zones of the world. https://www.google.com/search?q=agroclimatic+world+map&tbm=isch&source=iu&ictx=1&fir=RZRQZKWrn-cuvM%253A %252CK5mI6bLw-ePpYM%252C_&usg=AI4_-kQLCpj2NT19Y5oClA1TXInyGGzJxg&sa=X&ved=2ahUKEwi_u72T64bfAhWwpFkKHUDjAKUQ9QEwAHoECAIQBA#imgdii =VPnc4fimkV7g4M:&imgrc=Vifgz124U265PM:

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Chapter 3

Environment in Relation to Parasite, Mosquitoes and Affected People

Abstract  Understanding the environmental contribution to malaria’s distribution and intensity it is necessary to know how environment impacts on malaria parasite, vector and the corresponding number of affected people. This chapter discusses environmental features important for an appropriate development of vector and parasite and an intensity of malaria transfer to people. Generally, both mosquitoes and plasmodium need a warm and moist weather for excessive development. The environment in this cycle is represented by two parameters: long-term (multi-year) climate (principally moisture and temperature) and short-term (inside one year) moisture and thermal conditions. Climate-based malaria distribution and int4ensity have been well studied and described. However, the climate does not explain why one-year malaria affects a large number of people and the other year affects much less. Such a situation is controlled by weather. Moist and warm weather creates more malaria cases, while drought suppresses vector activity and malaria transmission. Many investigations across tropical regions used weather data to predict malaria area, intensity and the number of affected people. Unfortunately, the weather station network, which provides moisture and thermal data is too sparse, especially in tropical zones, to be used effectively. Available weather stations, controlled by the World Meteorological Organization (WMO), provide weather information in malaria endemic rea (if to assume that stations are equally distributed) for each 3000–36,000 km2 area. Therefore, we focus on operational satellite measurements, providing moisture and thermal conditions at the level of vegetation, which is the place of vector and parasite habitat. In addition, successful satellite application to malaria monitoring was achieved following development and application of new, theoretically grounded Vegetation Health (VH) method and VH products through estimation of moisture and thermal conditions of vegetation cover. Keywords  Environment · Malaria · Mosquitoes (Vector) · Parasite · Ecosystem · Climate · Weather · Vegetation Health

© Springer Nature Switzerland AG 2020 F. Kogan, Remote Sensing for Malaria, Springer Remote Sensing/ Photogrammetry, https://doi.org/10.1007/978-3-030-46020-4_3

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3  Environment in Relation to Parasite, Mosquitoes and Affected People

3.1  Specific Features of Parasite and Vector In order to understand how the environment affects malaria and its intensity, it is important to: first, focus on specific environmental requirements of malaria parasite and vector. As has been discussed, the mosquito vector spreads malaria by biting and injecting the Plasmodium parasite into a human’s blood. Chapter 2 discussed many aspects of malaria’s consequences from socioeconomic to individual. Since malaria affects a large swath of the world¸ with nearly one half of world dwellers and resulting in more than one million deaths each year, which include up to 50% of the estimated annual malaria mortality in persons less than 15 years of age, different aspects of the malaria’s environmental features and distribution have been well studied and published in the past several decades (Byron et al. 1991, McMichael et al. 1996, Kaya et al. 2002, Craig et al. 2004, Chandramohan et al. 2002, Bhatt et al. 2015). We outlined some mosquito and parasite features, which are important for understanding the environmental impacts on malaria’s features, such as intensity, area and the number of affected people. Many Anopheles (An) mosquito vectors carry the Plasmodium (P) parasite, which is the most dangerous. Among P parasites, the most widespread are two strongest species, P. falciparum (PF) and P. vivax (PV). From many An. types, the An. Dirus, An. gambiae and An. funestus, are the most efficient vectors transmitting P. falciparum. In forested areas of Southeast Asia, a parasite such as as An. arabiensis (AA) and An. merus (AM) spread malaria (Rahman et  al. 2006, Nagpal and Sharma 1995). In Africa’s and South America’s tropical areas, the most widely spread malaria types are Anopheles arabiensis in Africa and, Anopheles darlingi in South America. There are also other types of malaria but it is not as dangerous and has less distribution (Laporta et al. 2015, Recht et al. 2017). Malaria is transmitted by the infected adult female mosquito that bites to get blood for laying eggs. Physiological timing is important for understanding environmental impacts on mosquitoes’ development. The mosquito hatching period from laying eggs to an adult stage is between 7 and 15 days. An entire cycle, when the AD is able to bite and transmit the parasite is between 15 and 50 days (Pampana 1969, Boe¨te and Koella 2002). Therefore, during the summer (April–October) in the Northern Hemisphere, four to five cycles of the mosquito population are able to appear, bite and transmit malaria. The incubation period for the development of malaria, after the infected mosquito bites, is between 8 and 35 days. Environmental conditions, especially weather, during the indicated periods might drastically change the mosquito population, biting intensity and the number of affected people. Mosquitos’ breeding habitats include puddles on footpaths and turbulence pits at the heads of drainage gullies, which hold water for some time without supplemental rainfall (Rahman et al. 2006). In Bangladesh, for example, the AD-type malaria is mostly restricted to the hilly districts in the east and south. These forested hills are located at the center of the malaria endemic area. In general, the forested and forest-­ fringe areas report more than 90% of total positive malaria cases and more than 70% of total PF cases in all districts of Bangladesh (Elias and Rahman 1987, Ingrid and Van 2004).

3.2 Malaria, Ecosystems and Climate

45

A very important point is that environmental impacts on malaria distribution and its intensity have long-term and short-term aspects. The long-term (multi-year) environmental impacts on malaria is associated with climate and ecosystems, while short-term (less than one year) impact is controlled by weather. Therefore, further discussion focused on these two aspects: (a) climate-ecosystem and (b) weather.

3.2  Malaria, Ecosystems and Climate From the long-term or multi-year standpoint, climate and ecosystems contribute strongly to malaria distribution and intensity around the world. Following these considerations, malaria is typical in areas where a multi-year wet, warm and humid climate has contributed strongly to the development of tropical and sub-tropical type of ecosystems. Figure 3.1 compares world ecosystems, with specific focus on malaria (Fig. 3.1a, AEZ 2018, CIA 1997, Goldsberg 1972) and its distribution from the aspects of WHO (2005). First, it is important to emphasize that following Fig. 3.1a, there are four-type malaria ecosystems from tropical wet to tropical dry. Without a tropical-dry ecosystem, the remaining three types cover quite a large area, which is generally located in the northern and central South America, sub-Sahara Africa (except for desert-type ecosystem, especially in the far south), southeastern Asia, far north of Australia and part of Caribbean basin. These areas principally coincide with the malaria area, outlined on Fig. 3.1b. Some exceptions include central South America (not included in malaria area) and western India (included in malaria area) in Fig. 3.1b. However, considering estimates in Fig. 2.2 (c) and (d), that tropical moist ecosystem in the central South America has favorable climate for malaria; western India, having tropical dry ecosystem is affected by malaria as well. In the indicated warm and moist ecosystems, Anopheles s(An) mosquitoes can survive, multiply and malaria parasites can complete their growth cycle inside the female mosquito (“extrinsic incubation period”). Warm temperature is particularly critical during that period, especially if the temperature drops below 20 °C, the parasite, such as PF, cannot complete its growth cycle inside the An mosquito and the transmitted parasite cannot cause disease. In a very general sense, tropical and sub-­ tropical ecosystems with warm temperatures, excessive rainfall, and high humidity are conducive to mosquito breeding, longevity, active reproduction and parasite sporogony (WHO 2018, 2017, 2017b, 2015, 2013, 2010, 2009, Tanser et al. 2003). Al types of mosquitoes, especially An. gambiae and An.funestus, breed readily in large and small collections of sun-exposed and still water ponds, which exist throughout warm-wet ecosystems, especially in tropical Africa. In addition to ecology, warm, wet and moist climate, typical for tropical and sub-tropical world zones, determine primarily the distribution and activity of An mosquitoes, malaria intensity and the number of affected people (Olson et al. 2009). Besides climate and ecosystems, vector activity and malaria’s transmission intensity depend strongly on short-term environmental conditions of each year (monthly, weekly and even daily), which are related to weather (rainfall,

46

3  Environment in Relation to Parasite, Mosquitoes and Affected People

Fig. 3.1  World (a) Ecosystems (AEZ 2018, Carrasquilla 2001) and (b) Socioeconomic-based malaria-derived area (WHO 2005)

temperature, air humidity etc.) variation, presented below. Weather determines the number of mosquitoes and their ability to multiply, survive, bite and transfer malaria to humans (Tanser et al. 2003). Most research points out to three climate factors that control mosquito activity and their ability to distribute malaria: rainfall, temperature and humidity (Pampana 1969). The optimum temperatures for the most favorable mosquitoes’ activity and malaria development are between 25 and 27 °C (Hay et al. 2002, Bouma 2003). If the daytime temperature exceeds 40  °C, mosquitoes are less active and parasite transmission is very limited. In general, a larger amount of rainfall stimulates mosquito development. However, frequent and intensive rainfall (during for example, monsoon period) might produce stagnation in malaria transmission since intensive precipitation washes out eggs and reduces the chance for development of adult

3.3 Malaria and Weather

47

mosquitoes (Thomson and Connor 2001, Chandramohan et al. 2002, Thomson et al. 2000, 2001, 2006, 2011). There are other specifics of environmental impacts on malaria depending on location. For example, in the environment of Bangladesh, female mosquitoes stay active during the period when precipitation exceeds 50 mm per month. However, a combination of a large rainfall and hot weather during summer (June to August) might reduce mosquito activity (Chilundo et al. 2004, Rahman et al. 2006, 2010). Also, malaria transmission might slow down if humidity drops below 60%. Summarizing, wet, warm and humid climate are the principal determinants of long-term aspects of malaria distribution around the world. Figure 3.2 shows these climate parameters. In addition to ecosystems (Fig.  3.1), principal malaria areas coincide with maximum annual precipitation exceeding 2000 mm in most parts of South America, sub-Sahara Africa (except for the area adjusted to Sahara, and south, close to semi-desert), southeast Asia, northern Australia and Central America (Fig. 3.2a). Although the northern part of Europe and southeast of the North America are areas with similar amounts of annual precipitation, the climate of these is not vulnerable to malaria since temperatures are cooler (Fig. 3.2b). As seen in Fig. 3.2b, global mean annual temperature greater than 22 °C are more affected by malaria if precipitation is in the right range for malaria activity. Therefore, in addition to precipitation (P), climate-controlled malaria distribution should be considered by their combination with mean temperature (T). The P and T combination is well represented by potential evapotranspiration (PET) parameter. Following Fig.  3.2c, malaria is typical for areas with PET above 750 mm per year, which covers the same world area with more than 2000 mm annual rainfall (Fig. 3.2a). The PET>1200 mm/ year shows the area with the strongest malaria activity. Finally, climate-based malaria distribution is well outlined by the values of relative humidity, which should be above 88% (Fig. 3.2d). Although the Northern Hemisphere areas have similarly high humidity, they are not affected by malaria due to extremely low PET (2000 mm, mean annual temperature > 22 ̊ C, PET>1250 mm/ year and humidity >88%.

3.3  Malaria and Weather This portion discusses the second, short-term aspects of malaria distribution, intensity and impacts on the number of affected people. The parasite and mosquito development, vector activity and ability to transmit malaria changes from year to year, depending on the weather conditions. Most research points out to the same three

Fig. 3.2  World (a) mean annual precipitation, (b) mean annual temperature, (c) potential evapotranspiration (PET) and (d) mean annual relative humidity (CM 2018, NOAA 2018, 2017, Willmott and Matsuura 2015, Goldsberg 1972)

48 3  Environment in Relation to Parasite, Mosquitoes and Affected People

3.3 Malaria and Weather

49

Table 3.1  Malaria long-term Ecosystem and Climate characteristics in South America (SA), sub-­ Sahara Africa (s-SAf), Asia (As), Australia (Au), Caribbean (Ca) and Europe (Eu) Malaria Malaria world categories areas Major Norther SA, Central s-SAf, southeast As Moderate Central SA, South-central s-SAf, India &south-central China, Far north Au, Ca Minor East-central Argentina, south Af, west India, central China, north Au, Mexico, far south USA, far south Eu

Ecosystem Tropical Moist &Wet Tropical semi-dry & moist mountains

Precipitation (mm/year) >2000

Temperature annual mean (̊ C) >22

PET (mm/ year) >1250

Humidity annual mean (%) >88

1001–2000

20.1–22

751– 1250

70.1–88

19–20

600– 750

65–70

Tropical dry 500–1000

weather parameters (rainfall, temperature and humidity) the most important for controlling mosquito activity and their ability to transmit malaria (IAMAT 2019, Abiodun et al. 2016, M’Bra et al. 2018, McMichael 2013, Paaijmans et al. 2010, USAID 2007, Kumar et al. 2007, Zhou et al. 2004, Githeko et al. 2000, Nanda et al. 2000, Pampana 1969). Therefore, in the most recent two-three decades, weather parameters have been used as indicators for monitoring malaria epidemics, including features such as timing, duration, area and intensity of outbreaks (Nagpal and Sharma 1995, Smith and McKenzie 2004, Zhou et al. 2004, Allard 1998). I n general, warm and wet surfaces stimulate mosquito development and activities to carrying the disease to people. The optimum seasonal temperatures for malaria outbreak and its intensity are 25–27  °C (Hay et  al. 2002, Bouma 2003). For example, in Columbia (south America), where the mean annual temperature is 26° C, rainfall 6000–8000 mm and relative humidity is 90%, conditions are extremely suitable for spreading malaria (Bouma et al. 1997). The exceptions are the periods with extreme daytime temperature exceeding 35-40 °C, when mosquitoes are less active and parasite transmission is very limited. Large amounts of rainfall stimulates mosquitoes’ activity. However, frequent and intensive rainfall might produce stagnation in malaria transmission since it might reduce the number of adult mosquitoes, killing eggs (Thomson and Connor 2001, Chandramohan et al. 2002). In the environment of Bangladesh, AD females stay active during the period when precipitation exceeds 50 mm per month. However, a combination of large rainfall and hot weather between June to August might reduce mosquito activity (Chilundo et al. 2004, Rahman et al. 2006). Malaria transmission might also slow down when the air is dry (humidity less than 60%).

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3  Environment in Relation to Parasite, Mosquitoes and Affected People

In addition to general weather-malaria interaction, specific environmental features have been investigated during their pre-adult (eggs, larva, pupa) and adult stages of development. Water is a principal mosquitoes’ requirements in pre-adult habitat. Mosquitoes laid their eggs either on the surface of water or on damp soil that will be flooded by water. Most eggs hatch into larvae within 48 hours. Larvae lives in the water, where it feeds (on microorganisms and organic matter) and comes to the surface from time-to-time to breathe. Their speed of development and survival depends on water conditions (quite-turbulent) and air temperature. Rainfall and temperature determine larvae development in the aquatic environment and the survival of adult mosquitoes. For example, a larvae live cycle might continue 14 days at 20 °C but only 10 days at 30 °C (AMCA 2018). In the Ivory Coast (western sub-Sahara Africa), for example, elevated rainfall increases the availability, persistence and dimension of the Anapoles larval habitats (McMichael 2013, McMichael et al. 1996). The surviving larva is transferred quickly to no feed pupa stage, which soon converts to an adult mosquito. The newly emerged adult mosquito rests normally on the surface of the water for a short time to dry, harden the body and spread the wings. This stage continues for two days before it begins to feed on blood and mate. Although rainfall and surface water are important for a mosquito’s survival, temperature plays a leading role in the pre-adult physiological stage, the mosquito’s adult stage biting rate and gonotrophic processes (Paaijmans et al. 2010). Therefore, it is critical to establish a qualitative relationship between the vector abundance during the pre-adult and adult stages and temperature, identifying the peaks of the vector population. In this regard, a very interesting experiment was conducted in South Africa (Dondotha village (28°34′S, 31°56′E) northeast KwaZulu-Natal province), where researchers collected samples for pre-adult (immature) and adult stages of An. arabiensis mosquitoes and tested their livelihood under different temperature conditions between January 2002–December 2004 (Abiodun et al. 2016). Following this investigation, statistical models were developed to equate mosquitoes’ stage dynamics with temperature during the day and at night. Figure 3.3 displays these relationships. It is interesting, that the optimal temperature for producing the largest number of eggs is 25 °C (Fig. 3.3a) and the best time is in the afternoon. Temperatures lower and higher than the optimal are less effective for that stage of development. Among the lowest temperatures, 10 °C is the worst. The best temperature conditions for larva and pupa survival is between 25 and 35 °C, again with the worst conditions being when temperature is less than 10 °C (Fig. 3.3b and c). For adult mosquitoes’ performance, such as searching for host and rest site, similar temperature thresholds (25–35 °C) are considered to be the best (Fig. 3.3 d and e), although the differences in performance between the lowest and the highest temperatures are relatively small (32%) compared to pre-adult stage (nearly 100%). Mosquitoes search for an oviposition site that guarantees optimal egg and larval survival in case of a lengthy drought, freezing in winter, absence of nutrition and other is the best going under the lower temperature (10–15 °C) and can continue the entire day. If the temperature is higher than 15 °C, then the best search is performed during the morning hours (Abiodun et al. 2016).

3.3 Malaria and Weather

51

Fig. 3.3  Sensitivity of pre-adult (eggs, larva, pupa) and adult (host searching, mosquitoes at rest, oviposition site searching) mosquito population dynamics to temperature in South Africa’s Dondotha village

Models presented in Fig. 3.3 describe the dependence of the physiological stages, regulating mosquitoes’ abundance and potential performance, on weather. However, it is difficult to use these criteria for predicting the number of people with malaria disease. Therefore, many studies provide a direct comparison between the number of malaria cases (number of people with disease) and weather parameters. Some of the tests are presented below. One of them was performed in Ivoire Coast’s (Cote d’Ivoire) Korhogo city (Lon 05°38’W & Lat 09°28’N) during a 10-year period, between 2004 through 2013 (M’Bra et  al. 2018). Malaria is endemic in Côte d’Ivoire. In 2015, 330 cases of malaria were registered per 1000 population. Korhogo is the largest city of the country in the north with semi-arid climate. The average annual temperature there is 27  °C and precipitation is 1000–1200  mm (M’Bra et  al. 2018). Since the number of malaria cases were available during 10-year period from 2004 through 2013, they were compared with the total annual precipitation and annual mean temperature (Fig. 3.4). First, what was investigated the main months when malaria is the most widely spread. Following this figure, the seasonal distribution of malaria cases over the decade is coherent with warm months. During May–September malaria incidence showed a negative association with a lower temperature and a positive one with rainfall: the largest number of malaria cases per month (170–200) are associated with the 25 °C monthly temperature and above 15 mm total monthly rainfall (Fig 3.3a and b). These results can be used to develop an early warning system to forecast the period of the highest infection risk. Comparison of total annual malaria cases with time series of annual rainfall and mean temperature during 2004–2013 (Fig. 3.4 (c) and (d)) indicate strong dependence of annual malaria on temperature (Fig.  3.4(d)). The range of temperature dependence is very narrow, only 1  °C (between 27 and 28  °C). However, when mean annual temperature is approaching to 28 °C (drought development) the number of annual malaria cases is reducing to 6500–7000 (years 2009–2010). The

52

3  Environment in Relation to Parasite, Mosquitoes and Affected People

Fig. 3.4  Malaria cases, mean temperature and total precipitation: (a) and (b) monthly and (c) and (d) annual during 2004–2013 in Korhogo city (Cote d’Ivoire)

number of affected people is increasing nearly 34%, to 8500–9500 with mean temperature is slightly cooler, close to 27 °C (years 2005–2007 and 2012–2013). The situation with total annual rainfall impact is more complicated (Fig.  3.4(c)). In 2010, excessive rainfall (1270  mm) coincided with a very low number (6500) malaria cases, however, under the same amount of rainfall in 2012, the number of cases were extreme (9000). These results indicate that in the investigated semi-dry tropical area, it is important to take into consideration first, temperature, second, seasonal distribution of both rainfall and temperature (inside a year) and third how far is weather station from the location where the number of malaria events are registered. Moving from semi-dry ecosystems to a wetter climate and landscape areas, short-period (days, weeks, months, up to one year) precipitation and temperature are still the most important parameters characterizing the number of malaria cases. Rainfall in tropical areas are expanding the number of breeding sites of increasing mosquito populations and their activities. Warmer temperatures shorten the sporogonic cycle of the parasite increasing malaria transmission. Such conditions are reported in Rwanda and Uganda (Collon-Conzalez et  al. 2016). Malaria in these countries creates a big problem for human health. Multi-year statistics showed that the total number of malaria cases during an 11-year period (2001–2011) in Rwanda and 9-year (2002–2010) in Uganda were 50,104,341 and 101,636,920, respectively (Collon-Conzalez et al. 2016). Figure 3.5 compares annual number of malaria cases with precipitation (P) and temperature (T) for a few administrative regions. In Rwanda, the number of annual malaria cases followed well annual P and T dynamics in two areas strongly affected by malaria. Specifically, warmer temperatures and

3.3 Malaria and Weather

53

Fig. 3.5  Comparison the number of malaria cases in Rwanda and Uganda with precipitation and temperature during 2002–2012

a higher rainfall increased the number of people with the disease (Fig.  3.5). In south-central Uganda, a higher rainfall increased the number of malaria cases while a lower rainfall, decreased malaria cases. There are some exceptions to this relationship, due to specifics with applied protections against vector, seasonal distribution of P and T, and the distance between weather stations and malaria-affected areas and others, which should be considered in model development for monitoring malaria (Chandramohan et  al. 2002, Thomson et  al. 2006, 2000, Thomson and Connor 2001, Githeko et al. 2000, Rosenberg and Maheswary 1982, Biondi et al. 2016, Conor et al. 1999, Craig et al. 2004, Faiz et al. 2002, Hay et al. 2001). One serious problem in effective malaria early detection and monitoring based on weather data is the limited number of weather stations and their sparse distribution, especially in tropical ecosystems. Table 3.2 shows the number of weather stations and their potential area coverage (assuming that they are distributed uniformly) in two countries on each continent strongly affected by malaria. Although the countries are different in the occupied area and the number of available weather stations, the general conclusion is that the area covered by one weather station for the selected countries is huge, between 3000 and 36,000 km2. Even the smallest area/per station in Asia’s Nepal (3131 km2), is not sufficient for effective malaria monitoring, since precipitation and temperature measured at a weather station might be absolutely different at a station within up to 3000 km2 radius. This situation is much worse when one weather station is available for the area up to 12-time larger (10,000–36,000 km2). As seen in Table 3.2, the situation is worse in South America’s Bolivia and Venezuela, where one weather station is available for areas between 23,000 and 36,000  km2. Besides, it is well known, that weather stations are not

54

3  Environment in Relation to Parasite, Mosquitoes and Affected People

Table 3.2  Area of country in tropical ecosystem covered by one weather station (if to assume that they are equally distributed (WS 2018) Continent Africa South America Asia

Country Nigeria South Africa Bolivia Venezuela Myanmar (Burma) Nepal

Area (km2) 923,763 1,219,912 1,098,571

Number of weather stations 54 121 47

Area (km2) per station 17,107 9759 23,374

916,445 676,575

25 63

36,658 10,739

147,180

48

3131

distributed uniformly, making malaria monitoring extremely difficult (Ikeda et al. 2017). Therefore, for better monitoring and predicting of malaria area and intensity, some attempts to use high-resolution satellite data were made (Nizamuddin et al. 2013, a, Rahman et al. 2011, 2006, Bartnston et al. 1997, Rogers et al. 2002).

3.4  S  atellites Data – Solution for Malaria Monitoring and Prediction Opposite to a very low-resolution and unevenly distributed weather stations data, environmental satellites provide high-resolution (both spatial and temporal) observations of the earth’s surface and near-surface atmosphere, where conditions are regulated by weather in each climate- ecosystem type of environment. Therefore, environmental satellite data have been investigated since 1970s as a tool for monitoring land cover conditions (Gates 1970, Myers 1970, Tucker 1979, Tucker et al. 1983, 1986, 2004, Tarpley et al. 1984, Kogan 1987, 1995, Kogan and Guo 2015, 2016, Kidwell 1995, 1997 Cracknell 1997, Jacobowitz et  al. 2003, Texier et  al. 2013, Ceccato et al. 2005, Lindsay et al. 2000, Thomson and Connor 2001, Thomson et al. 2000, 2011, Mohapatra et al. 2000). Some practical experience with satellite data applications were reached in the 1970s when very high-resolution (32 m) Landsat satellites began to provide land-­ atmosphere measurements (NASA 2019, USGS 2019). Considerable success in satellite data use for land and atmosphere near the ground monitoring were achieved in the early 1980s with observations from the NOAA operational polar-orbiting satellites (Rouse et al. 1973, Tucker 1979, Kogan 1987, 1989, Cracknell 1997). These satellites began to provide daily observations in 1980 and continue the process even now, with the new generation of NOAA polar-orbiting satellites (currently NOAA-20), scheduled to work through the mid-twenty-first century. Considerable achievements with land and many events (malaria, food security, fire, etc.) monitored from the NOAA operational polar-orbiting satellites have been

3.5 Short summary

55

reached with the development of the Vegetation Health (VH) theory and methodology for applications, data set development and designing products (Kogan 1987,1989, 2018). The VH principle and data were developed from afternoon satellite measurements of land surface reflectance and emission, which were converted to vegetation characteristics, specifically moisture and thermal land surface conditions, applying the principles of three biophysical laws (Low-of-Minimum, Low-of-­ Tolerance and Carrying Capacity). These moisture and thermal characteristics were derived every week for each 1.0, 4.0 and 16.0 km2 of the global land. These VH data, indices and products have been obtained from 1981 through 2012 from the NOAA system, between 2013 and the current year, from the JPSS system and currently, the NOAA/new generation system (NOAA/NESDIS 2019). From 2013, in addition to the indicated resolutions data and products, weekly information is available for each 0.5 km2 global Earth. Since the mid-1980s, VH data has been used for global and regional monitoring vegetation health, moisture and thermal conditions of vegetation, drought, fire, soil saturation and other vegetation components (Kogan 1987, 1989, 1995, 1997, 2000, 2001, 2002, 2018, Kogan et  al. 2019, 2017, 2016, Kogan et  al. 2015a, b, 2014, Kogan et al. 2013a, b). From the 1990s, VH indices (moisture and thermal) were used for modeling crop yield (Kogan et al. 2005, Kogan et al. 2009, Salazar et al. 2007, 2008, Liu et  al. 2009). An important success in modeling and monitoring malaria was achieved when satellite-derived Vegetation health indices were used as predictors of malaria vector activities in spreading malaria in a few countries (Nizamuddin et  al. 2013, Rahman et  al. 2011, 2011b, 2010, 2006, Salazar et  al. 2008, Kaya et al. 2002, Hay et al. 2002).

3.5  Short summary Summarizing, we should emphasize again that annual malaria performance strongly depends on yearly weather and its monthly (weekly) distribution. Countries across tropical regions need weather data to predict malaria area, intensity and the number of people that could potentially be affected (Wild 2018, WB 2017). But weather station data are spread too far apart to provide local moisture and thermal conditions to monitor vector activities in order to predict malaria. For example, Africa has the world’s least developed weather, water, and climate observation network, which is spread far apart (Table 3.2). According to World Bank estimates (WB 2017), less than 300 of African’s weather stations meet the World Meteorological Organization’s (WMO’s) observation standards. Only 10 out of Africa’s 54 countries offer adequate meteorological services information, but the stations are spread too far apart to use this information efficiently (FCFA 2016). Following these considerations, the next two chapters are devoted to the description of operational satellite data, Vegetation Health (VH) methodology and VH products.

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Chapter 4

NOAA Operational Environmental Satellites for Earth Monitoring

Abstract  In the second half of the twentieth century, following demands for more accurate environmental monitoring of the Earth’s two satellite systems, geostationary (GOES) and polar-orbiting (POES) were developed to observe the ocean atmosphere and land for more accurate monitoring, modeling and prediction of environmental impacts on economy and human life. The measurements from the GOES satellite were used for weather monitoring and predictions. POES data were used intensively to estimate land cover change, vegetation health and soil moisture, to detect and monitor drought and vegetation stress, to predict fire and malaria risk, to model crop and pasture production and for other purposes affecting the humanity. Most of these POES products are used successfully for prediction of food shortages and the related food security problems. This chapter describes the POES data collection and processing (especially noise removal) from the two NOAA operational polar-orbiting satellite systems (initial – NOAA/AVHRR and new – SNPP/VIIRS) and data preparation for continued applications and predictions in agriculture, human health, food security, climate and land changes, disasters detection (drought, crop losses etc.). Since the late 20th, these data have been used for malaria monitoring. Keywords  POES NOAA/AVHRR and SNPP/VIIRS · Data collection and processing · Indices

4.1  Introduction Since the end of the World War II in 1945, an intensive economic and cultural development of the world’s countries have started. The initial efforts have shown that the recovery and further development could be done effectively with more accurate environmental monitoring of Earth’s land, ocean and atmosphere. The available weather station network used traditionally was extremely limited and unevenly distributed to provide sufficient environmental information. That stimulated the development of satellite systems to observe the Earth and to use the data for modeling © Springer Nature Switzerland AG 2020 F. Kogan, Remote Sensing for Malaria, Springer Remote Sensing/ Photogrammetry, https://doi.org/10.1007/978-3-030-46020-4_4

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and prediction of the impacts of environment on economy and human life. It took almost two decades for the development of the first continues satellite observations of the Earth. The practical experience with satellite data applications has started in the 1970’s when a very high-resolution (32 meters) Landsat satellites started to provide land-atmosphere-ocean measurements (NASA 2019, USGS 2019). Landsat data have been investigated intensively as a tool for monitoring three Earth systems: land, ocean and atmosphere (Texier et al. 2013, Ceccato et al. 2005, Lindsay et al. 2000, Thomson and Connor 2001, Thomson et al. 2000, 2006, 2011, Mohapatra et  al. 1998, Tucker 1979, Tucker et  al. 1983, 1986, 2004, Cracknell 1997, Kogan and Guo 2016, Kogan 1995a, 1995b, 1997, 2000, 2001, 2002). Considerable success in satellite data use for monitoring land and atmosphere near the ground have been achieved in the early 1980’s with the development of the new NOAA operational polar-orbiting satellites system (Rose et al. 1973, Tucker 1979, Tarpley et al. 1984, Kogan 1987, 1989, 2006, Kogan et al. 2013a, 2013b, Cracknell 1997). The launched NOAA operational long-term satellites have started to provide daily observations and are continuing this process up to now with the new generation of NOAA polar-orbiting satellites (currently NOAA-20), scheduled to work up to nearly the mid-twenty-first century. Initially, the two operational satellite systems have been developed: geostationary (GEO) and polar-orbiting (POES). The GEO satellites were developed for frequent (every 5–15 minutes) monitoring weather over the same limited area (between two poles) of the Earth. Global weather monitoring by GEO satellites was achieved through international cooperation using GEO satellites from several countries. Currently, global coverage of GEO satellites is achieved through several systems developed in the US (GOES and GOES-R) to observe North and South America, in Europe (METEOSAT, MSG) to observe Europe and Africa and in Japan (GMC, MTSAT) to cover Asian and Australian region. There are also geostationary satellites in other countries (Russia’s METEOR) and others. In general, the GEO satellites data are using for weather monitoring and prediction. The POES (polar-orbiting environmental satellites) produce less frequent observations but cover the entire globe in one day. They encircle the Earth in north-south sun synchronous orbit. Each polar-orbiting satellite is orbiting the Earth around the two poles, while rotating Earth is displaying the new area for data collection. Therefore, after 24-hours the entire globe is covered with daily environmental measurements, which are used for monitoring land, atmosphere, ocean and assessment of their impacts on economy and human life. In the most recent two to three decades, POES data were used intensively to monitor earth greenness and temperature, to detect and monitor drought (start, area, intensity, duration, impacts etc.), to measure soil moisture (both shortage and saturation), to estimate fire risk, vegetation stress and other environmental parameters and phenomena. These POES measurements and developed products were especially successful in providing assessments of drought impact on agriculture and prediction of crops and pasture losses, which were applied to estimate food shortages, and analysis of the food security situation (Salazar et al. 2008, Unganai and Kogan 1998, Kogan et al. 2019, Kogan 2018). In the recent two decades, the POES satellite data were used for malaria monitoring

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(Nizamuddin et al. 2012, 2013, 2013a, Rahman et al. 2006, 2010, 2011, 2011b). NOAA/POES satellite is a very complicated business in handling enormous amount of data, their proper processing and turning the data into products to monitor the Earth. Therefore, this Chapter discussion will be focused on the NOAA POES operational satellites’ measurements, noise removal, products’ development and their applications.

4.2  N  OAA Operational Polar-Orbiting Environmental Satellites (POES) Two NOAA POES satellite systems, morning and afternoon, were designed and have been providing 40-year (1980–2019) observations. The satellites have several instruments on board to measure many environmental parameters of the Earth’s ocean, land and atmosphere, which include incoming solar radiation (in a few parts of energy spectrum), temperature, precipitation, clouds, aerosols, and others. For an assessment of environmental impacts on agriculture, food security, fire risk, malaria spread and other purposes, two of the most important instruments have been used. On the initial NOAA system, called NOAA/AVHRR, the Advanced Very High Resolution Radiometer (AVHRR) have been serving the global community from 1981 through 2012 (Cracknell 1997). They are still continuing to provide measurements but with a deteriorated quality. The next generation sensor was the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the new operational system, called Suomi National Polar-Orbiting Operational Environmental Satellite System Preparatory Project (Suomi-NPP or S-NPP), servicing from 2012 through present. In November 2017, the new more advanced Joint Polar Satellite System (JPSS) with VIIRS sensor on board has been developed (JPSS 2014, NOAA 2017), and the first NOAA-20 satellite has been launched, which is now in sevice. This system is scheduled to continue its service almost to the mid-twenty-first century (satellites NOAA-20 through NOAA-24). Prior to the first US operational POES satellites, a few experimental meteorological satellites have been launched to investigate their performance and applications. The first such satellite, called TIROS-1, was launched in 1960. In the next 15  years, TIROS-2 to 10 and NOAA-1 to 5 were launched to test satellites and sensors performance and identify their potential applications. Following these tests, the first operational POES satellite in the US, NOAA-6 with AVHRR instrument on board, was launched on June 27, 1979. That day, the operational POES era has begun (Cracknell 1997, Kogan 1995a, 1997). The AVHRR sensor has five channels to observe the Earth in the visible, near infrared and three infrareds parts of solar spectrum. Since the early 1980s, fourteen polar-orbiting satellites were launched, with the AVHRR instrument on board, to be used for global numerical measurements of environmental parameters, data accumulation, calculation of indices and development of product used for monitor

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environmental parameters and their impact on Earth. Currently, almost 40-year (1981–2019) of NOAA/AVHRR and NOAA/VIIRS operational polar-orbiting satellite data and products characterizing land, at 4 and 16 km2 resolution, are available for the entire land. These data and products are providing numerical assessment of land cover greenness, temperature, vegetation health, droughts, soil moisture, prediction of crop and pasture production, evaluation of vegetation stress (moisture and thermal), food security, fire risk, malaria’s area and intensity, climate trend and some other parameters (Kogan et al. 2019, 2017, Kogan 2018, NOAA/NESDIS 2019).

4.2.1  AVHRR Sensor The AVHRR sensor on NOAA satellite system is a cross-track scanning radiometer (Kidwell 1995, 1997, Cracknell 1997). The AVHRR instrument has been observing the Earth continuously throughout its 37-year history (from 1981 to 2017) in the following wavelengths of the solar spectrum: visible (VIS, 0.58–0.68  μm, channel 1 (Ch1)), near infrared (NIR, 0.725–1.1  μm, channel 2 (Ch2)) and three infrareds (IR, 3.5–3.9 μm, channel 3, 10.3–11.3 μm, channel 4 (Ch4) and 11.5–12.5 μm, channel 5 (Ch5)). The NOAA/AVHRR scans the Earth continuously at high, 1.1-km (Local Area Coverage (LAC)) resolution, which is later composited to medium, 4 km2 (Global Area Coverage (GAC)) resolution of each orbit (Gates 1970, Cracknell 1997). From the 14th NOAA morning and afternoon operational satellites, flying in sun-synchronous orbit and carrying the AVHRR instruments, data sets for vegetation monitoring were developed from seven afternoon satellites: NOAA-7, 9, 11, 14, 16, 18 and 19. They were launched, correspondingly, on June 23, 1981 (local day time at launch 14:30 pm), December 12, 1984 (14:20 pm), September 24, 1988 (13:30 pm), December 30, 1994 (13:30 pm), September 21, 2000 (13:44 pm), May 20, 2005 (13:50 pm) and June 2, 2009 (13:44 pm). These satellites operated during the following years: 1981–1985, 1986–1989, 1989–1994, 1995–2000, 2001–2005, 2005-present and 2009-present, respectively (Kogan et al. 2017, Jin 2004, Kidwell 1997, Cracknell 1997). From September 1994 through January 1995, no afternoon operational observations were produced since NOAA-11 satellite malfunctioned and the new NOAA-13 satellite failed soon after launch. Also, between January– June 2005, NOAA-16 malfunctioned, from time to time, and its data was replaced with NOAA-17 (morning satellite) preliminary calibrated to the NOAA-16 afternoon data. From the indicated satellites, NOAA-7 and 9 carried AVHRR-1 instrument, NOAA-11 and 14 – AVHRR-2 and the rest - AVHRR-3. All of them have identical design but slightly different response functions (Kidwell 1995, 1997, Kogan et al. 2009).

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4.2.2  AVHRR Data for Vegetation Monitoring Several global data sets were developed from the AVHRR records since 1981. Three long-term data sets, oriented towards monitoring vegetation greenness, were NOAA’s Global Vegetation Index (GVI and GVI-2), NASA’s Pathfinder and NASA-­ MarylandUniv’s GIMMS (Tarpley et al. 1984, Kogan 1987, 1989, James and Kalluri 1994, Kidwell 1995, 1997, Tucker et al. 2004, Kirschbaum et al. 2017, PotashCorpo 2013, Pinzon and Tucker 2014). These data were focused on vegetation greenness only, which was characterized by two channels (VIS and NIR). AVHRR-based infrared measurements have been ignored on the “greenness” systems. Meanwhile, thermal measurements are very important for monitoring temperature-based vegetation stress, which is important for characterization of crop health, fire intensity, soil moisture reduction, vector activity and other parameters important for monitoring their impacts on humans. Following these shortcomings, NOAA developed a new data set, entitled the Vegetation Health or VH (LeComte and Kogan 1988, Kogan and Guo 2016, Kogan 1989, 2018). The NOAA operational VH has many advantages over the other nonoperational global data sets (Pathfinder, GIMMS and others). The most important are (a) application of infrared channels in addition to VIS and NIR, (b) comprehensive noise removal, (c) 39-year longevity, (d) global coverage with 0.5, 1 (2013-current), 4 and 16 km2 (39-­year) spatial and one-week temporal resolution, (e) strong theoretical background based on bio-physical laws (Low-of-Minimum, Low-of Tolerance and Principal of Carrying Capacity), (f) availability of numerous indices and products, (g) monitoring climate/land cover change and others. Following these advantages, the global VH was the most accurate in assessment of moisture-thermal drought impacts on crop losses, vegetation stress, fire risk assessment, prediction of vector activity in spreading malaria and climate monitoring (Kogan et  al. 2019, Kirschbaum et al. 2017, Kogan 2018). Summarizing, we should emphasize again that, the current AVHRR-based VH data set is global, the longest (39 years), has the highest spatial (4, 16 km2 (39 year), 0.5, 1 (6-year)) and temporal (one week) resolution, contains originally observed reflectance, emission, and many indices, including those with suppressed noise, presents biophysical climatology of indices, and, what is the most important, contains products used for monitoring the environmental and socioeconomic activities (Kogan et al. 2017, 2019, Kogan 1995a, 1995b, 1997, Rao and Chen 1995, Kogan 2018). This sub-chapter describes the new, considerably improved and currently available (to users since 1981 joint AVHRR-based and VIIRS-based operational global Vegetation Health) data set at 4 and 16 km2 (0.036° and 0.144°) resolution.

4.2.3  Initial Algorithm for Data Collection The initial VH system algorithm starts form 1.1 km2 data extraction from the NOAA AVHRR/CLAVR-x processing system (Jacobowitz et  al. 2003, Heidinger and Pavolonis 2005) and collating the data onto a global 4 km2 VH grid. This grid is

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based on the Plate Carree map projection. The global data spans from 75.024° (north edge) to −55.152° (south edge) in the latitudinal and from −180° (west) to 180° (east) in the longitude directions. This processing supports nominal grid cell size of 4 by 4  km resolution with the 3616∗10,000 grid elements for the entire world. The VH input includes the CLAVR-x navigation (NAV), observation (OBS), and geo-location (GEO) files for each Global Area Coverage (GAC) Level 1b orbit. The VH is using three AVHRR channels VIS (Ch1), NIR (Ch2) and IR-4 (Ch4). One of the important steps in primary data processing is radiometric calibration of VIS, NIR and correction of IR-4. Visible channels’ calibration consists of generally two steps: pre- and post-launch adjustments. Based on Kidwell (1995), the following pre-launch linear formula (A = S∗C + I) is applied, where (A) is albedo, (S) - slope and (I) – intercept. Since the instrument output does not remain the same after launch, post-launch calibration was applied (R  =  S∗(C-cd)) to AVHRR on NOAA-7 to 14 satellites, where C is 10-bit radiance count and cd – dark count) following Rao and Chen (1996, 1999). For NOAA-16 through 19, a dual slope calibration method was applied. For the best thermal characteristics of the environment, Ch4 (IR-4) data (from two IR (4 and 5) channels) were used since they are less affected by moisture in the atmosphere compared to Ch5 (Kidwell 1997, Cracknell 1997). One of the most important conditions, was the collection of thermal channel data from afternoon (not later than 3:00 pm) satellites observations in order to monitor the highest temperature during each day, which is crucial for detecting thermal stress in vegetation and fire risk, estimation of agricultural production loss malaria intensity and area and applying more accurate approach to food security and the number of people with malaria disease. The VIS and NIR channels have been used for monitoring vegetation greenness since the very beginning of the operational satellite era, although there was very limited biophysical explanation of the principles. As it is known from plants biology, due to the availability of the chlorophyll-a and carotenoids (Fig. 4.1a), vegetation absorbs the second highest amount of solar energy in the VIS (580–680 nm) range of solar spectrum for its biological processes. High solar energy absorption stimulates the highest photosynthetic rate and green mass accumulation (Fig. 4.1b). However, it is important to note that the highest solar energy absorption and the highest photosynthetic rate, as seen in Fig. 4.1, are in the 400–550 nm range of solar spectrum (Myers 1970, Gray and McCrary 1981, James and Kalluri 1994). Unfortunately, both operational AVHRR and VIIRS sensors, as well as scientific MODIS sensor, were not designed to produce measurements in that range because the main goals of the sensors during the period of their development were to observe and measure environmental parameters controlling weather (Cracknell 1997). Meanwhile, land and ecosystem scientists found ways of using the selected bands to characterize land cover with specific focus on vegetation. Using Landsat-1 satellite’s VIS and NIR data, scientists examined vegetation green-up during the growing season (Gray and McCrary 1981, Hashemi and Chenani 2004, NCDC 2011, ASP 1975). Following their analysis, the VIS and NIR bands provided useful information about ecosystems, both wild and cultivated. Figure 4.2 shows these differences, comparing the amount of solar radiation reflected by the vegetation in

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Fig. 4.1  Absorption of Chlorophyll-a, -b and Carotenoids (a) and Photosynthesis rate (b) for 400–700 nm wavelength

these two bands. First, what is noticeable from the Figure is that in all ecosystems very little radiation is reflected (0.6), (3) two types of VHI-SSTa teleconnections (positive and negative correlation) are seen for all and strong ENSO cases. Countries and regions with positive correlations include Argentina (north), USA’s California (south and central), Mexico (west), Horn of Africa and Saudi Arabia (central). The strength of the correlation for all 16 ENSO cases changes from 0.29  in Saudi Arabia to 0.74  in Argentina. Ecosystems in these areas experience stress during La Niña (VHI range 30–45, indicating drought impacts) and healthy condition during El Niño (VHI range 53–70). In the countries with the negative VHI-SSTa correlation in Table 7.2 (Brazil-north, Republic of South Africa-central, Australia-east, Canada-central, Republic of Ghana-south, and Borneo-south) vegetation experiences healthy conditions during La Niña (VHI >50) and becomes stressed during El Niño (VHI  0.5 ° C) since 1981. For the majority of the strongest four cases, the values and proportion of stressed-no stressed vegetation are identical to all ENSO cases. However, we should point out that for a few locations the strongest cases show more intensive vegetation stress (lower VHI for Argentina and the Horn of Africa) or healthier vegetation conditions (higher VHI for the Horn of Africa and California). If a very strong ENSO (|SSTa| ≥ 2.0 ° C) events occur in the future, Table 7.2 might be a good source to

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Table 7.2  Vegetation Health Index (VHI) during El Nino and La Nina years and VHI-SSTa monthly (November–February) correlation for 16 (|SSTa|  >  0.5  ° C) and four strongest (|SSTa| ≥ 2.0 ° C) ENSO cases

Region Argentina USA (California) Horn of Africa Mexico Saudi Arabia Brazil South Africa Rep. Australia Canada Ghana Borneo

# ENSO years 16 (All) 4 (Strongest) 16 (All) 4 (Strongest) 16 (All) 4 (Strongest) 16 (All) 4 Strongest) 16 (All) 4 (Strongest) 16 (All) 4 (Strongest) 16 (All) 4 (Strongest) 16 (All) 4 (Strongest) 16 (All) 4 (Strongest) 16 (All) 4 (Strongest) 16 (All) 4 Strongest)

La Nina 45 37 35 27 39 43 45 43 40 38 53 54 63 65 63 64 50 43 59 59 53 49

El Nino 70 71 45 53 47 54 53 52 58 61 41 40 41 45 35 37 38 36 51 38 35 34

VHI-SSTa monthly (November–February) Correlation 0.74 0.86 0.44 0.49 0.47 0.72 0.35 0.42 0.29 0.36 −0.70 −0.81 −0.59 −0.72 −0.52 −0.59 −0.52 −0.59 −0.53 −0.55 −0.61 −0.59

estimate vegetation health for an early prediction of malaria area and intensity, especially if ENSO event can be derived early (before November). The presented analysis allow us to summarize that following Figs. 7.8, 7.9 and Table 7.2. Many countries in the tropics and sub-tropics showed VHI-SSTa teleconnections for all and the strongest ENSO cases based on 4 km2 data. For most of the countries, the correlation was stronger than 0.45 and significant at 5% level. For nearly 50% of pixels inside each area (Fig.  7.9(a) and (b)), the correlation is 0.56–0.59. Among the strong VHI-SSTa teleconnections the correlation is larger than 0.69. Stable areas with positive correlation is typical for central and northern Argentina, southern Brazil, Horn of Africa, eastern Asia, and USA’s far west (mostly California). These areas experience favorable vegetation condition (VHI > 60) during El Niño and stressful (VHI  0.5 ° C). Following Table 7.3, we should conclude that in most countries, VCI and TCI indices correlate with SSTa with the same sign and values. This means that for a positive correlation (Fig. 7.9(a) & (c)), vegetation experiences moisture and thermal stress during El Niño and healthy conditions during La Niña; for negative

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Table 7.3  Pearson correlation coefficients between area-mean monthly VHI (moisture-thermal condition), VCI (moisture condition) & TCI (thermal condition) indices and mean monthly SSTa in Niño-3.4 area during all ENSO events (|SSTa| > 0.5 ° C) in 1981–2016 Region Number 1 2 3 4 5 6 7 8 9 10

Region Name Argentina Horn of Africa Asia northeast USA (California) Brazil South Africa rep. Australia Sub-Sahara Africa Canada Southeast Asia (islands)

VHI 0.74 0.47 0.76 0.44 −0.70 −0.59 −0.52 −0.57 −0.52 −0.81

VCI 0.59 0.68 0.63 0.25 −0.51 −.060 −0.56 −0.39 −0.65 −0.02

TCI 0.80 0.71 0.03 0.59 −0.73 −0.69 −0.59 −0.01 −0.75 −0.80

PCCs >0.7 are significant at 1%, the rest at 5%

correlation (Fig. 7.9(b) & (d)), vegetation conditions are opposite. Meanwhile, in the majority of countries in Table 7.3 (Argentina, Horn of Africa, Brazil, Republic of South Africa, Australia, and Canada) both moisture and thermal indices determine vegetation conditions, although thermal index (TCI), having higher correlation, indicates some advantages before moisture (VCI) index. The thermal index shows also considerable advantages before the moisture in California and Asia islands. In sub-Sahara Africa, the correlation of VCI and TCI with Niño 3.4 SSTa is not strong. The result in Table  7.3 can be used for the development of specific malaria prediction models.

7.7  Conclusion The previous discussions indicated that warm El Nino and cool La Nina create strong and relatively stable weather patterns (dry-hot and wet-cool) around the world during winter and summer seasons (NOAA/Climate.gov 2018; Suplee 1999; Trenberth and Hoar 1997; Ropelewski and Halpert 1996). In addition, the established weather parameters (mostly rainfall and temperature) during ENSO years correlate with intensity of malaria in some areas of a few countries of Southeast Asia, Africa and South America ((Dhiman and Sarkar 2017; Sekelaga Owusu et al. 2017; Hanf et al. 2011; Poveda et al. 2001; Bouma and van der Kaay 1996). Since the number of weather stations in the malaria endemic areas are very limited and malaria research is very localized, we used high spatial (4 km2) and temporal (one week) resolution VH indices for more precise characterization of the environment and ecosystems where vector spreading malaria is dwelling (Nizamuddin et  al. 2013; Bhuiyan et al. 2017; Dhiman et al. 2010; Rahman et al. 2006). Our goals were to estimate if VH-based conditions matched with ENSO-established weather-related

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patterns and if VH can be used for early warning of malaria area and intensity during ENSO years. This Chapter discussed 3–5 months of advanced malaria warning from vegetation conditions, changing by ENSO-developed specific weather pattern. Climate research has shown that the two phases of ENSO (warm, El Nino and cool, La Nina) phenomenon have profound impacts on global weather. Following ENSO, certain annual/seasonal precipitation and temperature patters are developing across the world, especially in tropical and sub-tropical ecosystems. Some areas have dry and hot and others wet and cool weather depending on ENSO phases. Malaria research, done in small areas (hospital, administration division etc.), have also proven that ENSO-developed weather is affecting the pattern of mosquitos’ activity and intensity of malaria transmission. Unfortunately, weather stations in the tropics and sub-­ tropics are located far from each other, making weather data less effective for malaria predictions. Therefore, we used high-resolution (1 and 4 km2) Vegetation Health (VH) data for analysis of vegetation conditions, their impacts on malaria distribution/intensity and providing 3–5 months advance malaria warning. ENSO, is known as oceanic-atmospheric cumulative climate phenomenon that develops in the Pacific Ocean. ENSO arrival and intensity is measured by sea surface temperature anomaly (SSTa) in the central (3.4 Niño area) Tropical Pacific. ENSO has two phases: El Niño, when SSTa is warm (above climatic norm) and La Niña when SSTa is cool. Each phase creates a certain type of weather (dry-warm and wet-cool) depending on intensity of ENSO. In order to understand if there is teleconnection between high-resolution Vegetation Health and SSTa, we developed monthly (November–February) 36-year (1981–2016) VHI (moisture-thermal) time series for each 4 km2 global land pixels and correlated them with monthly Niño-3.4 mean SSTa during the same November–February months. Following our investigation (Kogan and Guo 2016; Kogan 2013), we showed that Southern Africa, northern South America, eastern Australia and southern Southeast Asia had negative VHI-SSTa correlation during boreal winter (Fig. 7.8, red squares); southern South America (mostly Argentina) and the Horn of Africa correlation is positive (Fig. 7.8, blue squares). For the areas with negative VHI-SSTa correlation (PCC  =  0.53–0.74) vegetation is normally stressed during El Nino (VHI  60). For the areas with positive correlation (PCC = 0.61–0.82) vegetation health has opposite conditions: stressed during La Nina (VHI 58). Here, we also find out that boreal summer vegetation health is also reacting to ENSO-based weather changes in the same areas, but with smaller correlation coefficients. These results indicate the areas and intensity of ENSO impacts on VHI are in agreement with previous studies, which showed an existence of teleconnection between ENSO and weather parameters (precipitation and temperature) and NDVI. Summarizing these results, we should emphasize that Vegetation Health Index (VHI) is correlated with ENSO events. There are areas on all continents where vegetation is sensitive to ENSO impacts during boreal winter and summer. The areas of the most intensive VHI-SSTa correlation matches with the areas of strong weather changes from climate research. Moreover, VHI-estimated land surface conditions

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Rajeevan, M., & Mc Phaden, M. J. (2004). Tropical Pacific upper ocean heat content variations and Indian summer monsoon rainfall. Geophysical Research Letters, 31, 18203. Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang (2002). An improved in situ and satellite SST analysis for climate. Journal of Climate V15: 1609–1625. doi:https://doi. org/10.1175/1520-0442(2002)0152.0.CO;2. Reynolds, R.  W., & Smith, T.  M. (1995). A high-resolution Global Sea surface temperature climatology. Journal of Climate, V8, 1571–1583. https://doi.org/10.1175/1520-0442 (1995)0082.0.CO;2. Ropelewski, C. F., & Halpert, M. S. (1987). Global and regional scale precipitation patterns associated with the El Niño/southern oscillation. Monthly Weather Review, 115, 1606–1626. https:// doi.org/10.1175/1520-0493(1987)1152.0.CO;2. Ropelewski, C. F., & Halpert, M. S. (1996). Quantifying southern oscillation-precipitation relationships. Journal of Climate, 9, 1043–1059. https://doi.org/10.1175/1520-0442(1996)0092.0.CO;2. SCCONC (State Climate Office of North Carolina). (2015). Global Patterns - El Niño-Southern Oscillation (ENSO). https://climate.ncsu.edu/climate/patterns/ENSO.html Sekelaga Owusu A., Woyessa, R.  Cousin, T.  Dinku, D.  Korecha, A.  G. Barnston, B.  Lyon, M. Thomson, and A. Kebede (2017). El Niño and Malaria in Eastern Africa: The Ethiopian Experience and Recent Advances in Data and Tools. https://ams.confex.com/ams/97Annual/ webprogram/Paper317466.html Sena, L., Deressa, W., & Al, A. (2015). Correlation of climate variability and malaria: A retrospective comparative study, Southwest Ethiopia. Ethiopian Journal of Health Sciences, 25(2), 129–138. Suplee, C. (1999). El Niño/La Niña. National Geographic, 195, 73–95. Thomson, M.  C., Abayonmi, K., Barnston, A.  G., Levy, M., & Dilley, M. (2003). El Niño and drought in southern Africa. Lancet, 361, 437–438. Thomson, M. C., Indeje, M., Connor, S. J., Dilley, M., & Ward, N. (2003a). Malaria early warning in Kenya and seasonal climate forecasts. Lancet, 362, 580. Thomson, M. C., Mason, S. J., Phindela, T., & Connor, S. J. (2005). Use of rainfall and sea surface temperature monitoring for malaria early warning in Botswana. American Journal of Tropical Medicine and Hygiene, 73, 214–222. Trenberth, K.  E. (1997). Short-term climate variations: Recent accomplishments and issues for future Progress. Bulletin of the American Meteorological Society, 78, 1081–1096. https://doi. org/10.1175/1520-0477(1997)0782.0.CO;2. Trenberth, K. E., & Hoar, T. J. (1997). El Niño and climate change. Geophysical Research Letters, 24, 3057–3060. https://doi.org/10.1029/97GL03092. WMO. (1997). The 1997–1998 El Niño: A Scientific and Technical Retrospective. Bull World Meteorological Organization, Geneva (WMO No 905). WRCC (Western Regional Climate Center). (2015). Will El Niño Make a Difference? http://www. water.ca.gov/waterconditions/docs/Drought_ENSO _handout.pdf

Chapter 8

1981–2019 Vegetation Health Trends Assessing Malaria Conditions During Intensive Global Warming Abstract  Since the mid-eighteenth century, the Earth’s climate has been warming up. In the past 60-year, Earth warmed up intensively, leading to never before experienced environmental, economic and social events. One of the biggest climate warming concerns is how these changes have affected malaria and what to expect in the near and distant future, considering continuation of climate warming and an intensive population growth. In the recent decades, the United Nations’ (WMO, UNEP) climate change actions, IPCC reports and other scientific publications have strongly emphasized that CO2 increase was “very likely” triggering global warming, which has already led to negative consequences for the environment and society. The past 50  years of environmental observations showed global changes in snow and ice areas, sea level, natural disasters, biological systems (plants, birds etc.) and others. Many publications indicated that climate warming has negatively affected crop yield, especially in underdeveloped countries of Africa, Asia and Latin America. Unfortunately, there are only limited number of publications covering malaria response to global warming. Since climate warming is anticipated drought intensification malaria might be reduced in some areas. If climate warming creates favourable conditions (warm and wet) for mosquitoes’ activities, it might increase the number of people affected by malaria. Since some things are not known, therefore it would be interesting to investigate: What to expect in the future? Which regions will be affected by high malaria? Will the number of people affected by malaria be reduced or increased? Which regions/countries would continue to suffer from the disease? It is also important to understand if short-term (17–35 years) different climate tendencies would continue, considering that before 1998 global warming was very intensive, but between 1998–2014 (hiatus time) the global mean temperature anomaly (TA) experienced a flat trend and that in the last four years (2015–2018) the global mean TA was much warmer than normal. This chapter discusses the current climate warming views, emphasizing matching upward trends in global CO2 and a general warming trend in global mean TA. It also indicates that there are some mismatches between TA and CO2 trends and discusses other causes for temperature warm up. Moreover, this chapter is analysing 1981–2019 vegetation health conditions from remote sensing data and how to use then for assessing malaria area and intensity. These observations were used for trend analysis in global, continental and countries malaria endemic area. Nearly 40-year of remote-sensing high-resolution data were used to investigate vegetation greenness from satellite-­ based specially processed NDVI and Brightness temperature. Malaria endemic area © Springer Nature Switzerland AG 2020 F. Kogan, Remote Sensing for Malaria, Springer Remote Sensing/ Photogrammetry, https://doi.org/10.1007/978-3-030-46020-4_8

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was also investigated by the moisture (VCI) thermal (TCI) and Vegetation Health (VHI) indices. These indices were used to analyse stressed and healthy vegetation tendencies as identifiers of favourable and unfavourable conditions for mosquitoes’ activity in spreading malaria. 38-39 years investigations covered the global and continental malaria endemic area, and also areas of the most malaria-affected countries. Based on these trends the current and future malaria tendencies were assessed. Keywords  Climate warming · Malaria · Land cover change · Stressed and healthy vegetation · High malaria (HM) · Low malaria (LM).

8.1  Introduction Since the mid-eighteenth century, Earth climate has been warming up (IPCC 2007, 2014, 2018). From the nineteenth century, this process has intensified, especially since the mid-1970s, when by the turn of the twentieth century, global temperature anomaly increased up to 0.6 °C (WMO 2014, NOAA 2017, IPCC 2014), leading to quite unusual environmental, economic and social events (UNESCO 2018, NASA 2018, 2017, NOAA 2017, 2016, NOAA/NCEI 2017, IPCC 2014). Such climate warming has been frequently reported to speed up ice melting in the northern pole and sea level rise, to produce changes in biological systems (plants, birds, insects etc.), to increase water scarcity, to intensify and expand drought and other events (Chandler 2018, UNESCO 2018, Watts 2018, Serreze 2018, FAO 2018, 2017, Eilpering et al. 2019, Coats 2018, IPCC 2014, Godfray et al. 2017, Bromwich et al. 2013). One of the most frequently cited (by climate publications and media) consequences of the recent 38-year global climate warming was agriculture and food security, due to drought intensification and expansion, leading to a reduction of agricultural production, shortages of food and even hunger in developing countries of Africa, Southeast Asia and South America (Charles et  al. 2018, Seager 2018, Najafi et  al. 2018, FAO 2018, 2017, NOAA 2017, 2016, WMO 2018, 2017, 2016, 2014). Climate experts and media are warning that continuation of climate warming, would further reduce ice area, increase ocean level, and intensify natural disasters, leading to unusual socio-economic consequences. Some of the very important global consequences is anticipation that climate warming would intensify and expand droughts leading to a considerable reduction of crop production, increasing the number of malnutrition and hungry people in developing countries of Africa, Southeast Asia and South America. Climate warming impacts on food security is presently a very important concerns of global community, considering an intensive Earth population growth and a lack of food, especially in the years of intensive

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droughts (Eilpering et al. 2019, Dennis and Mooney 2018, UNESCO 2018, FAO 2018, 2017, WMO 2017, Alexandratos and Bruinsma 2012). Another a very important socioeconomic world problem is climate warming impacts on intensification of vector-borne diseases, especially malaria, considering that malaria spread among nearly three billion people (in malaria endemic area), responsible for above 200 million clinical cases and more than a million deaths (mostly children) each year (WHO 2019, 2018, 2017, 2016, 2015, 2010, 2009, 2005, 2002, 1999). Unfortunately, there are only a few researches using models and data discussing climate warming impacts on malaria (Githenko et  al. 2000, Hay et al. 2001, 2002, Kumar et al. 2007, BAMS 2018). Some of the United Nations reports anticipate that climate warming might increase the area and intensity of malaria, focusing specifically on possibility for expending malaria out of the endemic area (WHO 2019, 2018, 2017, 2015, 2013, 2010, CDC 2011, UNESCO 2018, USAID 2005, UN 2016. Considering an intensive Earth population growth, important economic and social climate-warming concerns currently is how climate warming has changed the environment and land cover and how these changes have affected malaria tendency in the past several decades. It is currently anticipated from a limited amount of research that climate warming will affect malaria endemic countries following expected intensification of droughts, excessive rains and enormous heat (WHO 2019, 2018, BAMS 2018, UNESCO 2018, IPCC5 2014, 2018). These conclusions were based on the limited malaria-environment modelling, a very widely spread weather station data and on a few years of local malaria observations (PMI 2019, 2005, Githenko et al. 2000, Hay et al. 2001, 2002, Kumar et al. 2007, BAMS 2018). These malaria-climate research results might be considerably improved based on several decades of high-resolution satellite data. Following an intensive work done at NOAA observing land surface from operational polar-orbiting satellites and the new satellite-based Vegetation Health (VH) technology (Kogan 2018). Nearly 40-year high-resolution (both spatial and temporal) land cover VH data are available presently to identify environmental changes in malaria endemic area, model VH-based malaria tendency and predict what can be expected in the near future. The VH technology from long-term satellite data will help to determine what type of changes have been developed during intensive global Earth warming and what can be expected in the near future. How climate warming might affect malaria area, specifically, will malaria expand out of tropical and sub-­ tropical areas? Finally, the most important question, what changes can be expected with land cover, natural disasters, especially dry/hot and wet/warm weather, which affect mosquito’s activity in spreading malaria. It is difficult to answer all of these questions since they relate not only to climate change, but also to economics, policy, sociology, science and even traditions. However, malaria in relation to climate change issues will be discussed with the application of 39-year satellite-based Vegetation Health data and products. This Chapter provides analysis of relationship between changing climate and biological and environmental parameters, such as land cover change (greenness and temperature), drought (start/end, intensity, area and duration) dynamics, vegetation health

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conditions, changes in moisture and thermal vegetation stress, tendencies in land surface changes and others. The 39-year weekly data used in this analysis have 4  km2 pixel resolution and can be presented by a pixel, areal mean for latitude-­ longitude coordinate box, administrative region, country, continent, and endemic malaria area. These multi-year VH data and products are ready to be used without additional processing, to assess the existing and near-future malaria dynamics. In order to identify malaria tendencies, it is necessary to have clear understanding of global climate warming trends.

8.2  Earth Climate Warming and Consequences Global temperature measurements showed that in the past 100 years, Earth’s climate has been warming. The average global temperature over the past 100 years (from 1906) increased 0.74 ° C (IPCC4 2007). Following IPCC Fourth and Fifth assessments (IPCC 2007 and IPCC 2014, respectively), in the past 30-50 years, following a climate warming, few very important global environmental events have occurred: ice in the North Polar was melting, the ice area was shrinking and sea level was rising. The recent National Academy of Science report (Nerem et  al. 2018) indicated that melting ice sheets in Greenland and Antarctica are speeding up the already fast paces of sea level rise. Following this rate, the world’s ocean will be at least two feet higher by the end of the current century. In the past 20 years, environmental observations also showed global changes in snow and ice areas, the sea level, biological systems (plants, birds, collar reef etc.) and others (IPCC 2014). On the land, it has been reported that global climate warming is causing spring to begin earlier, prompting insects to move to Texas (USA) sooner and improving the bats food supply. Therefore, bats are migrating to Texas roughly two weeks earlier (mid-­ March instead of late March) than they had moved 22 years ago (Chandler 2018). Besides, the same publication emphasizes an increase in corn and soybean production in the US’s Midwest due to a cooler and wetter summers. One very important issue of climate warming consequences is drought intensification and expansion, water scarcity and the gradual deterioration of agricultural system, which is expected to affect 5 billion people by 2050 (UNESCO 2018, GWCh 2018; Watts 2018). Regarding Earth vegetation, some research has shown an early greening, especially in the northern latitudes (Lucht et al. 2002, Myneni et al. 1997, Myneni and Running 2003, Nemani et al. 2003, Forzieri et al. 2017). These results were obtained from the analysis of 15-17  years of the Normalized Difference Vegetation Index (NDVI), calculated from the Advanced Very High Resolution Radiometer (AVHRR) measurements on board NOAA operational polar-orbiting satellites. Currently, almost 20-year NDVI data have been added to the AVHRR-based NDVI time series, including the new S-NPP and NOAA-20 satellite data from more advanced VIIRS sensor, improving the entire NDVI time series. Besides, the new VH technology has developed and used land cover temperature records. As the result, processing nearly four-decade NDVI and radiation temperature data records were improved

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considerably through comprehensive noise correction, contemporary approaches with data processing and analysis and, what is the most important, satellite data validation against in situ biological and weather observations (Kogan 2018). Moreover, following biophysical and ecosystem laws the new theory of Vegetation Health (VH) algorithm was introduced, permitting to develop VH-based products and new applications for agriculture, forestry, human health, climate forcing, natural disasters and malaria. All of these innovations permitted to develop the new 39-year global Vegetation Health (VH) dataset and products and used them for analysis of tendencies in land cover change in response to climate warming (Kogan 2018, 1983, Kogan et  al. 2010, 2013,  2015, 2016, 2017, 2018, Kogan and Guo 2014, 2017, 2015). In order to understand the impacts of global warming on malaria multi-year variations and tendencies, we first, have to describe many issues related to global warming presented in climate publications. They include the beginning of global warming, level of temperature from the start to the end of available data, how the temperature anomaly is measured, how intensive the global warming tendencies during different periods, what environmental events caused global temperature changes, why there are some jumps in global temperature, how well global warming trend is matching with hemispheric and regional trends of malaria activity. There are some other aspects of global warming and its impacts on malaria.

8.3  Causes of Global Warming Following IPCC (2007) and IPCC (2014) reports, continuous global warming is the result of fuel burning (coal, wood etc.), cement production etc., resulted in greenhouse gas emission, specifically CO2, which intercept infrared (IR) radiation emitted by land and warm up Earth climate. Therefore, in the past 20 years, there has been a strong focus on the need to reduce CO2 emission in order to mitigate climate warming and develop some measures for adaptation. The Kyoto Protocol and the subsequent Adaptation Fund were the first steps to encourage the international community begin working on these goals (UNFCC 2014). The Kyoto Protocol was issued in 1997 and in eight years was ratified by the participants, obliging industrial countries to cut greenhouse gas emission by 5% (compared to 1990 level) by 2008-2012. An intensive global campaign began from the publication of the book “Inconvenient Truth” (Gore 2006) and a film with the same title. Those sources show a diagram with global temperature increase time series and the matching CO2 increase as a prove that global warming is the result of CO2 increase in the atmosphere which is trapping heat. Some of the conclusions were quite scary: “the world at the edge of climatic catastrophe” … “if not stop emitting CO2, we come to the point of no return” (Gore 2006). In addition to CO2 some other greenhouse gases with smaller concentration are СН4, N2O, and SF6. However, according to IPCC (2014) “CO2 is the largest single contributor to radiative forcing over 1750-2011 and its trend since1970”. Therefore, human activity in releasing greenhouse gases,

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specifically CO2, is considered to be a cause of the current global warming (IPCC 2014). Following an intensive greenhouse gases-based United Nations’ actions, in 2015, 195 countries agreed with, the so called “Paris Agreement,” to reduce emission of greenhouse gases into the atmosphere (Gray 2016). According to the Agreement, countries have to cut emission of CO2 (coming from a burn facile fuel) to keep the global temperature anomaly under 1.5 ̊ C (and if possible, below 2.0 ̊ C). The largest world CO2 emitters are currently China, USA, the European Union, India, Russia and Japan. These countries contribute 68% of the total global CO2 emission, having only 51% of global population (Berwyn 2016). From 195 countries, 179 signed the Agreement (UN 2016). Regarding the emission amount, it is known that in the twenty-first century, China increased coal consumption by 19% (Bastasch 2017, Ren et al. 2017, Miller 2017). Almost three quarters of Russia’s territory (east of Ural Mountains) is burning wood and coal to warm houses during very harsh winters. I worked as an agricultural meteorologist in East Siberia for four years and know that there are little chances to change this trend. Following the signed international documents, if CO2 emission exceeds the level, assigned by the Agreement, countries have to pay a fine in the fund distributed to developing nations. Economists calculate that fulfilling the Agreements of CO2 limitation goal and keeping global temperature inside the indicated limits, would cost the world $10 trillion (Ward 2016). The United States of America agreed to cut emission from 28 to 26% by 2025 otherwise the USA has to pay a fine. The US Heritage Foundation calculated that if the US pays its share assigned by the Agreement, then by 2035, losses of Gross Domestic Product (GDP) would account for $2.5 trillion (Groves 2016). During Earth’s history, climate changed many times. As Dr. Ward (2016) presented; “The Earth climate has never been settled.” We know that millions of years ago (before appearance of human beings on the Earth), near-polar regions were heavily forested. For example, the Norwegian Spitsbergen (known as west Spitsbergen in the archipelago) is well known as a coal-mining place since the 19’s century (Harland and Henderson 1976). Availability of coal means that millions of years ago archipelago was quite forested due to warm climate and, following severe climate cooling and ice formation, the remnants of the forest were turned into coal being inside the soil without oxygen over millions of years (Harland and Henderson 1976). It is also known that between 100,000 and 10,000  years ago, the Earth warmed up approximately every 3600 years from the ice age to 10-16 ̊ C; 25 times, these changes from warm to ice age and back has happened (Ward 2016). Around 12,000 years ago, more than a half of the Earth was covered with ice, which melted very quickly with climate warming (Schlossberg 2016). In the 10s century, the Vikings discovered Greenland, which was covered with heavy vegetation due to warm climate but currently is under a thick ice. Little ice age was during 1340-1700 (Haldon et al. 2018), which was changed by intensive warming up at the beginning of the 17  s century (Alley et  al. 2010). That means that Earth has always been affected by strong climate changes, which resulted in some period with complete changes in Earth cover. During these ancient times, forest fires and volcanos were practically the only source of CO2 release in the atmosphere. Therefore, if no

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human-activities triggered CO2 release million and thousands of years ago, but the Earth’s climate fluctuated strongly from warm to cold and back with the corresponding changes of Earth cover, from heavy vegetation to ice sheets and back, then a logical question to ask is what other factors (in addition to volcanoes, fires and currently human activity) are responsible for climate changes. The IPCC Report (IPCC 2014), discussing the “Past and recent drivers of climate change,” correctly focused on the atmosphere, oceans, cryosphere, natural and anthropogenic radiative forcing and human activities. However, it is well known that except for the indicated in the report factors, the climate of the Earth is strongly controlled by solar activity, the distance between the Earth and the Sun, which is cyclical such as changes in the angle of Earth’s rotation axes (to the perpendicular towards the plate of Earth rotation around the Sun), atmospheric pressure, ocean-­ atmosphere thermal balance, and volcanoes (Ward 2016). Some other less frequent factors such as cosmic events (interconnection between Earth with Moon and Sun) and internal Earth forces should also be considered, as contributors. In addition, other climate-affecting factors such as, the quasi-periodic oscillations of the meridional heat transport (MHT) in the North Atlantic are usually regarded as the main mechanism for the formation of low-frequency variations in SST and heat fluxes on the ocean–atmosphere boundary in the North Atlantic (i.e., of the AMO) and others. The importance of the AMO mechanism in changing the climate is confirmed by the fact that approximately16-year phase shift is observed between the low-frequency variations of absolute humidity in the surface layer of the atmosphere (leading to the variations of latent heat fluxes through the sea–air boundary) and SST in the northwest part of the North Atlantic (Polonskii and Voskresenskaya 2004). In addition, such a large-scale atmospheric circulation pattern as the North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), Arctic Oscillation (AO), Atlantic Meridional Overturning Circulation (AMOC) and others affect earth climate (Serreze 2018, Weisberger 2018, Rice 2018, Ward 2016). Moreover, some research showed that quasi-periodical inter-­ decadal warming and cooling of the North Atlantic is of the same order or even exceeds human-induced warming (Kerr 2005, Raa et  al. 2004). The most recent Science article by Samset (2018) focuses on human activities’ emission of aerosols particles, which produced an overall cooling effect on earth, blocking sunlight penetration. Moreover, climate models indicate that the cooling effect might be as large as 0.5 ̊C and should be taken into consideration calculating the balance of CO2 (temperature warming) and aerosol’s (temperature cooling) contribution to Earth warming.

8.4  Global Temperature and CO2 Trends Following sub-chapter 8.3 discussion, climate change is driven by multi-­dimensional factors. However, the explanation of the current climate warming is presently considered to be related to greenhouse gasses, specifically CO2 released in the

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atmosphere by human activities (from fossil fuel burning) (IPCC 2014, IPCC 2007, UNFCC 2014, Gore 2006). Therefore, before coming to the assessment of the current climate warming consequences for malaria, it is important to analyse the global mean temperature anomaly (TA) and CO2 dynamics (from a well-established sources) in detail for a more precise understanding global TA role in assessing its impact on malaria. Following IPCC (2018, 2014, 2007) and other reports (UNESCO 2018, NASA 2018, 2017, NOAA 2017, WMO 2017, 2014, Karl et  al. 2015, Hansen and Sato 2004, Hansen et al. 2010, 2000), continuous one and a half century surface global warming has been going on since 1850’s. Figure 8.1 presents TA and CO2 trends from IPCC (2014) together with average North and South Hemisphere’s TA trends (Hansen et al. 2010). Very many publications (IPCC 2017, IPCC 2014, Gore 2006 and others) indicate a matching one and a half century matching upward trends in global TA (Fig. 8.1a) and CO2 (Fig. 8.1b). Hemispheric TA trends is growing up similarly from 1980’s. Figure 8.1 analysis comparing TA and CO2 trends are based on the recent IPCC report, which calculates TA using 1986-2005 base climatic period (IPCC52014). Following IPCC (2014), during 162 years (1850-2012), global Earth mean TA increased from -0.7 to +0.3 ̊ C (almost 140%) and CO2 has increase from 2 to 40 GtCO2/Yr (190%). Hemispheric temperature anomaly increased since 1980 from -0.3 to +0.7 ̊ C for the Northern Hemisphere and to nearly +0.5 ̊ C for the Southern Hemisphere. The strongest both CO2 and TA increased occurred after 1980. Moreover, as IPCC5 (2014) indicates “… the period from 1983 to 2012 was likely the warmest 30-year period of the last 1,400 years in the Northern Hemisphere”,

Fig. 8.1  Global (a) mean land and ocean temperature anomaly (TA, relative to the 1986-2005 global mean temperature (IPCC 2014, 2007)), (b) atmospheric CO2 emission from burning fossil fuel, cement production and flaring, forestry and other land use (1850-2012), (c) North and South Hemisphere temperature anomaly (1880-2009, Hansen et al. 2010)

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when “the global average combined land and ocean temperature data calculated as linear trend, show a warming of 0.85°C (0.65 to 1.06°C) between 1980-2012.” Following IPCC reports, global TA and CO2 have matching general trends. However, a more precise analysis of these trends (Fig. 8.1(a) & (b)) indicate that at the background of the stably increasing trend in CO2, TA (both global and hemispheric) has experienced several mismatching types of short-term (17–35  year) trends. They are shown with red lines compared to a grey line of the general TA trend from 1850 to 2012 in Fig. 8.1a. This mismatch was emphasised by Dr. Ward (2016). Our analysis is focused on numerical comparison of short-term trends in global TA and CO2. During the first 100 years, from 1850 through 1950, CO2 experienced continues increasing trend while global temperature anomaly (TA) experienced three short-term trends: (1) 1850 through 1873, (2) 1874 through 1910 and (3) 1911 through mid-1940’s. For the case (1), the analysis indicates that, at the general background of 75% CO2 increase (from 2 to 3.5 GtCO2/Yr, Fig. 8.1b) during 23 years from 1850 through 1873, global TA trend was flat (no increase/decrease (TA trend was at the level of around -0.3  °C), mismatching with CO2 increasing trend. For the case (2), mismatch between trends of TA and CO2 was even opposite decreasing for TA (from -0.3 °C to -0.5 °C during 37-year) increasing for CO2 (77% increase from 3.5 to 6.2 GtCO2/Yr). Only for the case (3) both TA and CO2 have similar upward trends. However, TA increase was much more intensive (32%) compared to CO2 (6%). The case (2) mismatching situation repeated again in case (4, Fig. 8.1(a)) during 1944-1970 when CO2 increased 3.8 times (from 6.3 to 24 GtCO2/ Yr, Fig. 8.1b), while TA trend decline from -0.3 to -0.5 °C), during the same 28-year starting from 1944. The case (5) is the only event when during 17-year period both TA and CO2 trends were matching and intensively increasing, for TA from -0.5 to +0.15 ° C and for CO2 from 17 to 28 GtCO2/Yr. Finally, the most interesting mismatching 17-year trends occurred during 1998-2012: flat trend in TA (around +0.2 °C) and strongly increasing CO2 trend (nearly 50% from 28 to 40 GtCO2/Yr. The 15-year (1998-2012) period of flat global TA trend was called by climatologists as a “hiatus” time (Karl et al. 2015). It is interesting, that short-term trends in TA for both Hemispheres (Hansen et al. 2010) are similar to the described global short-­ term trend, although Northern Hemisphere’s TA is showing more precise match with the globe. Unfortunately, IPCC (2014)) climate warming publication has not provided well determined explanations to mismatches between strong CO2 increase trend and the up/down and hiatus trends in global temperature anomaly. Some discussions have been about the 30-year (mid-1940s to mid-1970s) decline in TA trend at the background of a very intensive CO2 increase, considered as a cause of climate warming (IPCC 2007, IPCC 2014, Kennel 2014, Brahic 2007, Kerr 2005, Ned 2010, Berardelli 2010, NOAA/NCDC 2017). As some analysis suggest, the 1945-1975 period of climate cooling, followed by elevated industrial and volcanic aerosols in the atmosphere during the post-World War II period (Kennel 2014, Brahic 2007, Kerr 2005). Following the indicated climate cooling, by the mid-1970s, the world was very concerned about a 30-year global temperature reduction, specifically, was it possible

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for this reduction to lead to an ice age and what impact it may have on human activities. I remember that time very well since I worked as a senior agricultural meteorologist at the USSR Hydrometeorological Centre, the main forecasting weather, ocean, hydrology and agrometeorology organization. I was responsible for weather-based modelling and forecasting of USSR’s annual grain production. Grain in the USSR was the most important agricultural product, used for estimation of food security. The grain production forecast has been issued regularly for the central government only in May and June. The USSR had been generally very concerned about the amount of grain produced by Soviet agriculture annually, since that amount was much below what was needed for food and feed. According to non-official estimates, Soviet agriculture had to produce every year one million tons of grain per each person. Between 1946 and 1980 (the year of my emigration from the USSR), that goal had never been achieved. The maximum grain collected during that period was 208 million metric tons (MMT) in 1978 (Kogan 1983) for the total USSR population of about 260 million people (Anderson and Silver 1990). In 1972, the USSR was affected by the strongest drought (since 1946), when only 160 MMT of grain was collected (my prediction issued in early May 1972 was 162 MMT). Following a lack of grain in general (compared to USSR population), extremely low grain production following the 1972 drought and the cooling global temperature trend, the USSR Government asked the Hydrometcenter to provide a long-term forecast, what changes in Soviet grain production might be expected due climate cooling. We had performed a lot of modelling, calculations and estimated changes in the amount of Soviet grain production due to the reduction of grain crops area in the northern European USSR, potential improvement in grain production in Kazakhstan and others following continuation of global cooling. Fortunately, no measures had been taken by the USSR Government, since in mid-1970s, global warming has started again. The previously discussed the 162-year climate analysis was based on TA data presented in the IPCC (2018, 2014, 2007). There are other publications with the TA data. However, we should warn that in many cases it is hard to compare the TA data sets, presented in publications, since they were developed using different basic climatic periods for calculation of TA (for example, 1950-1990 or 1961-2000 or 1986-2005 or others). The World Meteorological Organization (WMO 2015) developed global mean TA data set from 1950, using data set developed by IPCC and other sources (IPCC 2014, Karl et al. 2015, NOAA 2017, NASA 2017, USGCRP 2017). The data improved calculation of global temperature anomaly using the most recent, 1980-2010 global temperature climatology and extending TA up to 2014. Therefore, further analysis of global warming impacts on malaria and VHI-estimated moisture and thermal environmental conditions from 1981 will be done from the WMO (2018a, 2017a, 2016, 2015) data set. Figure 8.2b compares IPCC and WMO global TA from 1950, confirming the IPCC and WMO data are matching quite well. The TA trends from 1981 through 2014 are matching quite well. Analysis of global mean Earth TA since 1981  in Fig.  8.2(b) clearly indicates two trend periods with different TA dynamics: (1)

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Fig. 8.2  Annual global mean Earth TA during (a) 1850-2012 (relative to 1986-2005 global mean temperature’s climatology (IPCC 2014, colour indicates different data sets)) and (b) 1950-2014 (relative to 1981-2010 global mean temperature’s climatology (WMO 2014)); red vertical lines indicate the period from 1981, when Vegetation Health data has started

strong TA growth (upward trend) during 17-year from 1981 through 1997, when TA increased from approximately -0.32 to +0.20 °C and (2) flat trend in TA (around +0.2 °C) during 17 years, between 1998 and 2014. Climatologists called the second 17-year period of flat trend “hiatus” time (Santer et  al 2017, Karl et  al. 2015, Kennel 2014). These two periods of (1) intensive TA growth (1981-1997) and (2) flat trend in TA between 1998 and 2014 are the most important for analysis of global climate warming impacts on Vegetation Health and prediction of malaria variation and intensity. In summary, the IPCC reports (IPCC 2007, IPCC 2014) and other scientific publications (UCS 2017, UNESCO 2018, WMO 2017a, Brahic 2007, Kerr 2005, Berardelli 2010, NOAA/NCDC 2017) have strongly emphasized that the CO2 increase has very likely induced global warming with some consequences for the environment and society. This conclusion was initially based on strongly matching upward general TA and CO2 trends from1850 through 2012 (Fig. 8.1(a) grey line and Fig. 8.1(b), Gore 2006). However, detail analysis of the entire 162-year trend indicate that CO2 has stable increasing trend while global temperature anomaly experienced six 17–35-year up and down trends four of which completely mismatched with CO2 continuously increasing trend. Specifically, they mismatched: from 1850 through 1880s (TA has flat trend, while CO2 was increasing), from 1880s to 1910 (TA was decreasing, while CO2 increasing), from1910 to mid-1940s (TA was decreasing, while CO2 increasing), from mid-1940s to mid-1970s (TA was decreasing, while CO2 was increasing strongly) and from 1998 to 2014 (hiatus time TA has flat trend, while CO2 was increasing strongly. A reasonable question to ask is why, at the background of continuing initially slow and during industrial time

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strong CO2 increase the global mean TA changed its trend six times? And only during two periods the TA trends were matching, while during four periods were mismatching with CO2 trends.

8.5  N  ew Ideas About the Causes of Global Warming During 1981-2014 In the recent two decades, there have been many different ideas about causes of global warming in addition to CO2-triggered generally increasing TA trend. They included changes in solar activity, El Nino Southern Oscillation (ENSO), large-­ scale atmospheric circulation patterns changes (NAO, AO etc.) and others (Kennel 2014, Brahic 2007, Kerr 2005, Berardelli 2010, NOAA/NCDC 2017, Freedman A. 2017, Serreze 2018, Polonskii and Voskresenskaya 2004, Hansen et  al. 2000, 2010, Lucht et al. 2002, Raa et al. 2004, Ren et al. 2017, Chandler 2018). One of the new ideas came from the Book of Dr. Ward (2016). The Book has not only discussed the new cause for global warming, but also investigated the physical principle of greenhouse gases-based global warming, its history, development, duration and impacts on society. Following Dr. Ward’s (2016) investigation, the greenhouse warming idea has appeared in 1859 from physical experiments by J. Tyndall, who showed that greenhouse gases (GHG) absorb some infrared (IR) radiation. Later, spectral physicists have learned in laboratory experiments that IR radiation is absorbed into oscillations of the bonds, holding each molecule of GHG together. However, it has been unknown how the oscillatory bond energy is transferred to kinetic energy, which is heating the atmosphere and land. It was assumed that the energy transfer is going through numerous collisions of GHG molecules, resulting in broken chemical bonds holding molecules together. In 1900, K. Angsröm, a well-known physicist, showed in physical experiments that the IR warming effect is minimal. Meanwhile, the recent 20-40  years, climate models, built on GHG theory, has shown reasonable results of the Earth’s warming until 1997. But during 17-year period, between 1998 and 2014, global TA (around 0.2 °C) experienced flat trend while CO2 was increasing strongly (from 30 to 40 GtCO2/Yr, or 33% (IPCC 2014, 2018)). This TA and CO2 mismatch was also confirmed by Dr. Christy in his testimony before the U.S. House Committee on Science, Space & Technology in March 2017. He demonstrated that the rates of warming Tropical Mid-Tropospheric temperature depicted by the 102 climate models for the period 1979-2016 was significantly different from the observations (Christy 2017). Other scientific publication blamed declining solar irradiance during the two last 11-year  cycles and also ENSO (Coddington et  al. 2016, Kennel 2014, Berardelli 2010) for TA hiatus during 1998–2014. Dr. Ward (2016) explained that the upward global temperature trend from the early 1970 was due to chlorine-induced ozone depletion in the atmosphere and a flat TA trend during 1998–2014, due to a slow ozone restoration. The principle of this

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process has the following explanation. Ozone (O3) is a colourless gas, in the stratosphere is a shield absorbing ultraviolet type B (UV-B) radiation protecting living things on the Earth. The process of ozone depletion began in the 1960s following human activities in using widely chlorofluorocarbon gases (CFC) in refrigerants, paints, perfumes, lubricants, cooking oil and other. By 1974, scientific research has shown that when CFC gases roused to the lower stratosphere, they are broken down by UV-B radiation, resulting in a release of chlorine, which destroys the ozone. It was estimated that one chlorine atom might destroy up to 100,000 molecules of ozone through a catalytic reaction (Ward 2016, 2016a). The process of ozone destruction was very extreme in polar regions, resulted in development of the so-­ called ozone hole over Antarctic in 1985, where ozone was depleted in half. It was slightly less depleted in the Arctic, up to 15% in mid-latitudes and very little in the tropics (Bromwich et al. 2013). Ultraviolet radiations are high energy electromagnetic waves emitted by the Sun which, if enters the Earth’s atmosphere, can lead to a number of health-related issues for all living organisms and also various environmental issues including global warming (Ankit 2015). Following the chlorine-based destruction of the ozone shield, Earth began to warm up. It was clear that the global society must stop releasing CFC gases into the atmosphere. Following this goal, the United Nations Montreal Protocol limited manufacturing of CFC gases. As the result, by 1993, chlorine increase in the atmosphere stopped, by 1995, ozone

Fig. 8.3  1945-2014 global mean temperature anomaly (TA, Degree C), carbon dioxide (CO2, in Parts per million), ozone (O3, in Dobson units) and chlorine in (Part per billion) from CFC gases

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depletion stopped and by 1998, the global temperature increase stopped as well (Ward 2016). Following this idea and a few research (Ward 2016, 2016a, Staehelin et al. 1998, Solomon 1999, NOAA/NCDC 2016, Levitus et  al. 2012, NOAA 2016, Farman et al. 1985, Bromwich et al. 2013) a diagram was developed showing this process. Figure 8.3 shows 1945-2014 trends in global temperature anomaly and CO2 in the atmosphere, O3 and chlorine accumulation in the stratosphere. Ozone dynamics (black line) clearly indicates its depletion along with accumulation of chlorine in the stratosphere (green line). This diagram supports the previous paragraph’s discussion on the physical principal of ozone depletion. It shows that ozone depletion has initiated as soon as chlorine began to increase in the early 1970s. At the same time, TA began to increase strongly (red bars). CFC release in the atmosphere reduced considerably in 1993, following intensive international efforts to limit using CFC gases. From that time, the amount of chlorine started to reduce as well and the most important that ozone depletion practically stopped in the next 17-year (between 1998 and 2014). Trend analysis in Fig. 8.3 indicates that during 1975-1997, the strongest upward trend was observed for three parameters: increase in TA and CO2 and depletion in O3. During that 23-year period, the temperature anomaly increased from 0 to +0.5˚̊C with matching CO2 growth from 330 to 350 parts per million (PPM) and ozone decrease from approximately 332 to 313 Dobson Units (DU). Since both O3 depletion and CO2 increase had approximately, the same rate (around 6%) during 1945-1995, it is hard to understand, which of these parameters triggered Earth’s warming (strong TA increase). The answer came from an analysis of these three parameters dynamics after 1997. The most interesting differences in matching O3 and CO2 trends with TA changes began in 1998 and continued through 2014. The amount of CO2 in the atmosphere during those 17 years continued to increase intensively (from 375 to 400 PPM, or 7%), while TA did not change (flat trend), remaining at the level of around +0.22˚̊C during hiatus time (Karl et al. 2015). The CO2 and O3 trends’ mismatch indicated that carbon dioxide increase has not stimulated rising global temperature during 1998–2014. Here where ozone comes as the potential source of global temperature stabilization during the 17-year, since ozone restoration in the lower stratosphere recreated the natural screen, protecting the Earth from dangerous UV-B radiation’s penetration to the earth surface. It is known that ultraviolet radiations are high energy electromagnetic waves emitted by the Sun, which, if it enters the Earth’s atmosphere, can lead to a number of health-related issues for all living organisms and also intensifies global warming (UCS 2017, Ankit 2015). The diagram in Fig. 8.3 clearly shows a very good match between TA trend and O3 depletion trend from the mid-1970s through 2014. Two cycles in both data are perfectly seen: strong increase in TA and O3 during 1975-1997 (23-year) and, what is the most important, matching almost flat trend in both parameters during 17-year from 1998 through 2014. Therefore, following Fig. 8.3 and physics of ozone concentration as Earth protective shield (Ward 2016, 2016a, Ankit 2015), strong depletion of ozone layer was accepted as additional to CO2 cause of climate warming

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during 1975-1997 and the only cause of no warming during hiatus time from 1998 through 2014.

8.6  Strong Global Warming During 2015–2018 Following nearly 40-year mismatch between trends in global TA and CO2 the only point of concern, is the causes of considerable global warming in the 4-year (2015, 2016, 2017 and 2018) after hiatus time (NOAA 2016, UCAR 2018, NOAA/NCDC 2016, NOAA/NCDC 2017, NOAA/NCEI 2017). During 2015-2018, Earth temperature unexpectedly increased strongly. Figure  8.4 displays global temperature anomaly since 1980, shown in Fig.  8.1 but extended for the years 2015 through 2018 (WMO 2018a, Spencer 2019, BAMS 2019, 2018, NOAA/NCDC 2017, NOAA/NCEI 2017). Calculations showed that four-year (2015-2018) mean global TA (calculated relative to 1980-2010 climatology) unexpectedly reached 0.42 °C, which was 0.22  °C above the 17-year (1998–2014) flat global mean TA trend (around 0.22 °C above 1980-2010 climatology) during hiatus time. Detail analysis of these 4-year data (Fig. 8.4), indicate that in 2015, global mean TA jumped up to 0.45 °C, in 2016, the TA increased even higher, to 0.56 °C, and after that dropped down to 0.46 °C in 2017 (WMO 2018a) and further down to 0.3 °C in 2018 (Spencer 2019, BAMS 2019, 2018, WMO 2018a), quickly approaching to 1998–2014, hiatus time’s TA trend level. During 2015-2018, global TA was the highest since 1880 (BAMS 2019, 2018, NOAA/NCDC 2017, NOAA/NCEI 2017). Some scientific publications blame strong El Nino of 2015-16 for such a warm up, others blame solar activity, ocean-­ atmosphere interaction etc. (ClimateBet 2018, Haldon et  al. 2018, UCS 2017, Samset 2018, NOAA/NCEI 2017, NOAA/NCDC 2017, Kogan et  al. 2015a, 2015b, Kogan and Guo 2017). The most interesting fact is that the highest global TA between 2015-2018 period occurred after 17  years (1998–2014) of stable (no increase) global TA during climate hiatus time. Another interesting fact is that

Fig. 8.4  Global mean land temperature anomaly (relative to the 1986-2005 mean) during 1850-2012 (IPCC 2014), and temperature anomaly (adjusted to the IPCC 2014 climatology) during 2013-2018 (red bars)

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NOAA reports indicate only the values of global TA and its distribution over the Earth, but do not indicate the causes of such warming, which is very important for understanding if TA increase is going to continue. If it is, then a new multi-year global warming trend should be expected after a hiatus time of flat global TA trend. If global temperature is going to reduce after 2018, then hiatus-type stable global TA trend would continue. Climate publications indicated several causes for global mean TA increase, which we investigated including the latest 4-year. First of all, it is greenhouse gas emissions (mainly CO2), since most of climate publications are strongly supporting contribution of industrial CO2 release as the main cause of trapping earth-emitted infrared radiation, resulting in strong increase of Earth warming over the recent 3-4 decades (NOAA 2018, WMO 2018, Blander et al. 2018, Williams and Roussenov 2017, NASA 2017, USGCRP 2017, IPCC 2014). However, CO2 measurements (NASA 2018) indicated that between 2014 and 2015, CO2 has increased only 1% (from 397 to 400 Dobson Units), while global TA (relative to 1980-2010 climatology) has more than doubled from 0.22 to 0.46 °C (WMO 2018a) between these two years and almost tripled between 2014 and 2016 (0.56 °C). Therefore, CO2 contribution to doubling global mean TA between 2014 and 2015 (including the next two years) cannot be considered as one of the causes of strong 2015-2018 TA increase. Another source of strong global TA increase in 2015 and especially 2016 (compared to 2014) was El Niño-Southern Oscillation (ENSO (WMO 2018, Blander et al. 2018, USGCRP 2017)). During November 2015-April 2016, extreme El Niño occurred, when ocean in the area of ENSO’s 3.4 Tropical Pacific (the most influential ENSO area) was 3.0 °C warmer than normal (Kogan and Wei 2017), which was the strongest ENSO in the past several decades. Release of such extreme ocean heat intensified overall global warming (Wendel 2018). A very warm 2015-16 El Niño contribution to global warming was also intensified by two other preceding events of a warm ocean: (a) the so-called “Blob” and (b) long-term accumulated ocean heat (Cornwall 2019, Thomas et al. 2018, Cheng et al. 2018). The “Blob” started in late 2013 (Cornwall 2019), when a large area of unusually warm water formed in a Gulf of Alaska has spread south so fast that by mid-2015, the “Blob” doubled in size, covered nearly 4∗106  km2 of Pacific Ocean between Alaska and California, had surface temperature 2.5 °C above normal and even crashed some of marine ecosystems (appearance of toxic algae, small fish dying etc.) (Cornwall 2019). Regarding the (b) event, global ocean has been generally warming up for a few decades (Cheng et al. 2018). The rate of the warming for the upper 2000-m has accelerated from 0.55 in 1991 to 0.68 W per m2 by 2000. In addition, the area of warm ocean has increased strongly between 1982 and 2016 (from 27 to 62% for severe and from 68 to 93% for moderate heat (Cornwall W. 2019)). Moreover, the number of marine heat waves almost doubled during these years (Thomas et  al. 2018). Therefore, release of heat accumulated in the ocean over the recent decades, heat from Blob during 2013-2015 and especially 2015-16 El Niño-released heat have contributed to the recent 4-year of Earth warming. One more source of global warming during 2015-2018 was an unexpected and persistent increase in global emission of ozone-depleting chlorofluorocarbon (CFC),

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specifically CFC-11 (Blander eta al 2018). The recent measurements indicated that CFC-11 was emitted from Southeast Asia (most probable from China) during 2014-2016 (Montzka et  al. 2018, WMO 2018a, NASA 2018a, Bastasch 2017). Penetrating to the lower stratosphere, CFC-11 contributes to ozone destruction, reducing Earth protection from an increased penetration of solar ultraviolet B radiation (UV-B) to the earth surface and intensification of global warming (WMO 2018a, UCS 2017, Ward 2016). Previously, such situation of strong earth temperature increase, occurred from the mid-1970s, when CFC gases, manufactured worldwide since 1960, penetrated to the lower stratosphere, were broken by UV-B radiation, resulted in releasing chlorine, which destroyed a large amount of lower-­ stratospheric ozone (Ward 2016). Additional source of ozone depletion during 2015-2017 and increasing global temperature might be also eruption of Bardarbunga volcano from September 2014 through February 2015 in the central Iceland. This effusive-type volcano has spread mostly basaltic lava over the area of 85 km2, which emitted ozone-depleting chlorine and bromine (Ward 2016). When these two gases reached the lower stratosphere in the second half of 2015, they provided additional contribution to destroying earth-protective ozone layer, increasing UV-B radiation reaching the earth surface, causing some warming. (UCS 2017, Ward 2016, Solomon 1999).

8.7  3 8-Year Land Cover Changes in Malaria-Endemic Area During Global Warming Following a previous discussion, it is important to reiterate that: (a) between 1981-1997, there was a match in the trend of global TA and CO2 increase and O3 depletion trend; (b) between 1998–2014, global TA, had a flat trend, mismatching with a strong CO2 increase and matching with a depletion of O3 flat trend; (c) the world was very warm between 2015-2018, without a definite explanation as to the cause (in climate publications), except for a strong El Nino in 2015-16, an intensive water warming south of Alaska (the “Blob”) in 2014-15 and possible solar activity and a volcano. Therefore, there is only 4 years (2015-2018) of strong TA increase (mean value 0.42 °C) after 17 years of a much lower TA trend (with mean value 0.22  °C) that has not been explained and with no indication of expected future global TA trends. Therefore, any further discussions related to environmental conditions during a strong global warming will also include an investigation into land cover changes, specifically, vegetation greenness and radiative temperature during the three periods 1981-1997 (strong global warming), 1998–2014 (flat global TA trend during hiatus time) and 2015-2018 (very strong unexpected increase in global TA). The 38-year (1981-2018) Vegetation Health data (NOAA 2019) and method (Kogan 2018) were used for this analysis. Land cover changes in malaria-endemic area will be investigated by vegetation greenness and radiative temperature of

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vegetation cover. These parameters will be analysed by the NO noise Normalized Difference Vegetation Index, called Smoothed NDVI (SMN) and Brightness Temperature (BT), called Smoothed temperature (SMT, Kogan 2018). An almost four-decade trend of these parameters will be investigated by an assessment of a 17-year statistical trend between 1981-1997 (following global TA upward trend) and 1998–2014 (following hiatus time flat global TA trend). The analysis will cover the entire global malaria endemic area and three continents. Land cover greenness and temperature trends were estimated by a linear regression of a multi-year weekly index value (K) against weeks-years. An intensity of the trend was estimated by statistical analysis of linear trend through (a) slope (S) and (b) trends’ relative differences (RD). The slope (y) was estimated by a linear equation’s trend (8.1). The RD was calculated for the corresponding parameters’ values taking the difference between the trend end (tj) end trend beginning (ti) relative to the ti (Eqs. 8.2).

( a ) Trendk = xk + yk ∗ Week & Yeark

(8.1)



( b ) RDk = 100 ∗ ( tkj − tki ) / tki

(8.2)

where K is the investigated index (SMN, SNT, VCI, TCI, VHI), x is intercept, y is slope, RD – relative difference, tkj – values at the trend end, tki – values at the trend beginning. Vegetation greenness and surface temperature are two important parameters characterizing mosquito dwelling conditions and impact on their activities. Some publications emphasize that climate warming stimulates vegetation greening and warmer temperature of vegetation surface in wet and humid areas (BAMS 2019, 2018, 2017, 2016, Jia et  al. 2009, Pouliot et  al. 2009, Young and Harris 2005, Myneni et al. 1997). Greener and warmer (not hot) vegetation is supposed to provide better conditions for mosquito survival and activity and vice versa for less green and cooler vegetation surface in malaria endemic area. For example, if vegetation greenness is increasing in response to global warming, mosquito activity in spreading malaria should intensify. However, if temperature in malaria endemic area increases too much (strong thermal drought) due to global warming, then environmental conditions for mosquitoes might deteriorate. Therefore, further discussion will present data of satellite -based greenness (estimated from no noise NDVI (SMN)) and radiative temperature trends using no noise Brightness Temperature (SMT) in malaria endemic area. Figure 8.5(a) and (b) presents greenness (G) and temperature (T) time series in the entire malaria endemic area for the periods of global TA trends (c) and Table 8.1 presents numerical values of a trend’s relative difference (RD) following Eqs. 8.2 The purpose was to investigate (1) G and T trend direction and intensity, and (2) if greenness and temperature trends in malaria endemic areas follow global temperature anomaly trends (intensive increase in 1981-1997, flat TA trend in 1998–2014 hiatus time and global TA jump up in 2015-2018). It should be emphasised that

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Fig. 8.5  Vegetation greenness and temperature with trends in the entire malaria endemic area (shown on the map) during three periods: (a) 1981-1997 and 1998–2014, (b) the entire 38-year (1981-2018), (c) global TA and trends during intensive global warming in 1981-1997 and during hiatus time in 1998–2014 Table 8.1  Relative difference (Eq.  8.2) between the end and the beginning of trend values (Eq. 8.1) in malaria endemic areas (global and three continents) Greenness (SMN) % Change % Change from 1998 from 1981 to 2014 to 1997 3 0

Malaria Region All endemic SAmerica 3 Africa -1 SE Asia 3

1 1 6

% Change from 1981 to 2018 5

Temperature (SMT) % Change % Change from 1981 from 1998 to 2014 to 1997 5 5

% Change from 1981 to 2018 5

3 0 8

5 4 4

6 5 4

8 5 4

since the 2015-2018 period was short, its contribution to G and T changes were tested on the entire 1981-2018 time series to see if an intensive global TA jump up in the last four-years (2015-2018) led to G and T in the entire malaria endemic area increase at the end of time series. Since malaria endemic area covers three continents, which have differences in environmental conditions and malaria spread and intensity, the next goal was to investigate (a) if G and T trends for malaria endemic areas of each continent have followed global warming trends in the three periods (intensive global warming in 1981-1997, flat trend in 1998–2014 and strong TA jump up in 2015-2018) and (b) if the continents (South America, Africa and Southeast Asia) are different in G and T.  Figure  8.6 displays G and T time series and trends for the entire 38-year (1981-2018) of the three continents’ endemic malaria area and Tables 8.1 provides numerical approximation of trends RD (Eq. 8.2) for the three periods. South America (SA)  Most malaria cases in South America are from the Amazon rain forest area in northern countries imposing a considerable burden on local popu-

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Fig. 8.6  Greenness (SMN) and temperature (SMT) time series with estimated trends between 1981-2018 in endemic malaria areas of the three continents of South America, Africa and Southeast Asia (shown on the maps)

lations (Jia et al. 2009, Pouliot et al. 2009). Although economic, social and political situations strongly affect malaria activity in South America, many researchers ­indicated that climate and weather conditions provide considerable contribution to the annual spread and intensity of malaria. Here, we analyse if climate warming changes land cover, specifically greenness and temperature affecting malaria distribution. The 38-year G and T data were obtained by averaging satellite-based 4 km2 weekly vegetation measurements over the area shown on the figure’s map. Following Fig. 8.6, the 38-year G and T values were around 0.35 (SMN) for greenness and 21-22 °C (SMT) for temperature. Following Table 8.1, the 38-year G and T trends were growing slightly up with relative difference 3% for greenness and 6% for temperature. These values, by 17-year periods (Table 8.1), showed a similar to 38-year trend intensity, specifically, 3 and 1% growth for G and 5 and 8% increase for T (in both periods of climate warming 1981-1997 and 1998–2014, respectively). Following these calculations, G and T trends in endemic malaria of South America mismatched with global TA warming during 1981-1997, almost matched during a hiatus of a flat trend and no impact on the entire 1981-2018 trend following strong global TA increase during 2015-2018. In general, G and T trend in South America’s area of malaria is matching with trends in the entire malaria endemic area, Africa  Malaria burden was the heaviest in the African, providing around 90 percent of the world malarias cases. Moreover, malaria is one of the leading causes of death in Africa. For children under 5 years of age, malaria accounts for 61% of all deaths (WHO 2019). Although from 2010 through 2018, the number of malaria cases per 1000 population in Africa decreased from 260 to 215, the 10 the most affected countries in Africa reported increases in cases of malaria in 2017 compared with 2016. Of these, Nigeria, Madagascar and the Democratic Republic of the Congo had the highest estimated increases, all greater than half a million cases. The six highest burdened countries in the WHO African region have been Nigeria, Democratic Republic of the Congo, United Republic of Tanzania, Uganda, Mozambique and Cote d’Ivoire. These six countries account for an estimated 103 million, or 47% of all malaria cases (WB 2018). Following figure Fig.  8.6, the 38-year  G and T values in Africa were around 0.30 (SMN) for greenness and 23-24 °C (SMT) for temperature. Based on Table 8.1, the G time series trend was

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nearly flat for the entire 38-year and for the two 17-year periods (RD is between -1 and + 1). The 38-year temperature dynamics had a slight upward trend (RD = 4-5) for all three periods. Thus, similar to South America, the 38-year global warming has not changed G and T in Africa’s malaria endemic area. Southeast Asia  is home to about 2.2 billion people, potentially at risk for contracting malaria. This equates to approximately 67% of the world population at risk of malaria, largely because six of the most populous countries in the world (India, China, Indonesia, Bangladesh, Vietnam and the Philippines) are located in this region (WHO 2010a). Moreover, in such countries as Bangladesh, India, Indonesia, Myanmar and Vietnam, about 91 percent of the population lives in areas of high malaria transmission. For example, in 2008, India, Myanmar and Indonesia accounted for approximately 94% of the reported malaria cases in the region, with India bearing the highest burden at 65% (WHO 2010a). Malaria in Southeast Asia exhibits special epidemiological characteristics such as forest malaria and malaria due to migration across international borders (Bharati and Ganguly 2013). Following malaria’s socioeconomic specifics, land cover changes play a very important role for assessment of future malaria activity in Southeast Asia. Figure 8.6 provides a 38-year time series of vegetation G and T in malaria endemic area of Southeast Asia. The levels of G and T in Southeast Asia during the entire period were 0.27.0.28 (SMN) and 18-19 °C (SMT), respectively. Changes (RD) in G from the beginning to the end of shown periods were 3-8%, which is slightly higher than in the other two continents. Temperature trends of the investigated periods in Southeast Asia were increasing slightly with the RD of 4%. Summarising the results presented in Figs. 8.5, 8.6 and Table 8.1, it is important to emphasise that (a) G and T trends for the entire malaria area and also for the three continents were growing slightly (RD 1-8%) during the investigated three periods (1981-1997, 1998–2014 and 1981-2018); (b) this increase was negligible compared to global TA intensive growth in 1981-1997 and 2015-2018. These two summaries are very important because the area of analysis is huge, n7early 20 million square kilometres, representing around 15% of Earth’s ice-free land cover (Global Forest Atlas 2018, Mayaux et  al. 2005). These are very good examples indicating that global warming is not affecting vegetation greenness and surface temperature on a huge tropical area that is severely exposed to malaria. Another example of limited impact of global warming on drought trend, both global and regional, was discussed in Kogan (2018). Following the presented 38-year G and T trends in the malaria endemic areas of the world, it is expected that current G and T in that area will continue for the next few years, even if global TA will continue the tendencies established during 1981-1997 and 1998–2014.

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8.8  1 9812018 Vegetation Health Trends Assessing Long-Term Malaria Intensity and Areas Sub-section 8.7 presented analysis of long-term land cover changes, indicating that a 38-year G and T experienced very negligible growth in malaria endemic areas following established long-term climate and ecosystem resources. Therefore, 1981-2018 malaria activity would not be affected from changes in vegetation greenness and temperature. Considerable changes in malaria distribution and intensity are coming from annual weather changes. Chapter 6 discussed how to control annual malaria area and intensity using satellite-based Vegetation Health (VH) indices. As was indicated earlier, there are three VH indices characterising vegetation moisture (VCI), thermal (TCI) and combined moisture-thermal (VHI) conditions (Kogan 2018). Weekly 4-km2 resolution VCI, TCI and VHI values provide numerical assessment of weather impacts on vegetation and consequently on mosquitoes’ activity in spreading malaria. Therefore, the next analysis investigates if weather impacts on vegetation conditions are changing due to intensive climate warming during 1981-2018. Long-term vegetation condition changes were evaluated by VH time series, estimating moisture (VCI) and thermal (TCI) and moisture-thermal combined vegetation condition trends. Short Methodology  The VH indices are changing from zero, which assess weather-affected exceptional vegetation stress, to 100 – extremely healthy vegetation. The indices values between zero and 100 characterise the entire range of weather conditions and corresponding mosquitoes’ low/high activities and consequently an intensity of malaria spread. Since VH indices are appropriate indicators of vegetation condition changes due to weather variations, healthy vegetation (moist from VCI and warm from TCI) determines intensive vector activity in spreading malaria. If vegetation is stressed due to drought (dry and hot weather), mosquitos are less reproductive and active in spreading the disease. It was established that indices values between 60 and 100 characterise vegetation as healthy and more intensive mosquitos’ reproduction and higher activity in spreading malaria, with the corresponding higher number of malaria cases. This situation is identified as high malaria (HM). Index values below 40, characterize vegetation as stressed and have a reduced number of mosquitoes and generally, less intensive mosquito activity in spreading malaria, with the corresponding lower number of people who contract malaria. This situation was identified as low malaria (LM). Three levels of each HM and LM were developed based on a correlation between the number of malaria cases and values of VH indices. For HM the VH levels’ range 60-100 was determined as moderate HM, the range 75-100 was determined as very HM and the range 85-100 was extra HM (the largest number of malaria cases). Similarly, for LM, the levels’ range between zero and 30 was determined as moderate LM, between zero and 20  – strong LM and below 11  – very strong LM (the smallest number of malaria cases).

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Moisture-Thermal Vegetation Condition (VHI) for Global Malaria Endemic Area  First, the entire malaria endemic area was investigated. The analysis was based on the Vegetation Health Index (VHI), which estimates combined moisture-­ thermal conditions. From the VHI values, three levels of high (HM) and three levels of level low (LM) malaria intensities are estimated. Figure 8.7 displays VHI trends assessing changes in the HM and LM percent affected area for three levels of intensity during the three periods of global climate warming. During an intensive global warming between 1981 and 1997, moisture-thermal (VHI) conditions in the entire malaria endemic area declined slightly (RD is 1-2%) for both LM and HM. Around 10% of an endemic area was affected with moderate intensity malaria for both cases (yellow for LM and light blue for HM). Environmental conditions for extreme malaria intensities affected 3 and 1% of light and very light LM, respectively and 4 and 1% of very and extra HM, respectively. Therefore, intensive global warming during 1981-1997, has not affected changes in LM and HM, assessed by VHI (Fig. 8.7(a)). During the hiatus time (1998–2014) of stably flat global TA, an area of stressed vegetation experienced a slight upward areal trend for all intensities (from 5% at the trend beginning to 11% at the trend end for moderate stress, from 2.5 to 4% for severe stress and from 1 to 2% for extreme stress, Fig. 8.7(b)). These trends indicate a gradual reduction of the malaria area for all intensities and potential reduction of malaria cases. Opposite to stressed, healthy vegetation experienced a slight declining trend (from 14 to 9% for moderate (light blue), from 4 to 3% for

Fig. 8.7  Moisture-thermal (VHI) time series, characterizing vegetation condition (stressed and healthy) and HM or LM malaria area and intensity during three period of climate warming trends (1981-1997, 1998-2014 and 1981-2018)

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8  1981–2019 Vegetation Health Trends Assessing Malaria Conditions…

very healthy (dark blue) and from 2 to 1% for extra healthy (red)). This declining trend confirmed that HM activity is declining, due to dryer conditions. Since global warming intensified strongly during 2015-2018, further analysis investigates vegetation and malaria trends during the entire period from 1981 through 2018, shown in Fig. 8.7(c). Both healthy and stressed vegetation had a very slight declining VHI trend with RD (between beginning and the end) nearly 1%. The moderate area of HM and LM are growing up by 13 and 10% of RD. The other intensities for both HM and LM are covering areas of 4% and 1%. Following, Fig. 8.7(c), LM and HM trends identified from a combination of moisture and thermal condition index (VHI) have not changed during the period of intensive climate warming form 1981 through 2018. Moisture (VCI) and Thermal (TCI) Vegetation Conditions for Malaria Endemic Area of the Three Continents  The long-term VHI index estimates dynamics of vegetation conditions and malaria from combined moisture-thermal conditions. Unfortunately, during extreme weather events over a few years (high/low temperatures or moisture deficit/excess), temperature and moisture combination might not provide exact evaluation of malaria conditions. Besides, since the three continents affected by malaria have very different climates, ecosystems and weather conditions, vegetation-malaria assessment produced over the entire endemic area might provide not very precise malaria assessments from VHI.  Therefore, further vegetation-­malaria analysis are performed for each continent with separate assessments of moisture (VCI) and thermal (TCI) vegetation conditions. Figure 8.8 (A, B, and C) displays a 38-year time series of moisture (VCI) and thermal (TCI) vegetation conditions for continental malaria. We are pursuing two goals (a) contribution of long-term moisture and thermal conditions in changes of malaria area and intensity and (b) if global warming stimulated changes in malaria activity. It is noteworthy to remember that the analysis will be done for two malaria conditions: low malaria (LM) and high malaria (HM). The LM indicates that vegetation conditions for mosquito activity in malaria transmission are deteriorating (malaria becomes less intensive, affects smaller area and the number of people with the disease are reducing). The HM indicates that vegetation moisture and thermal conditions become favourable for malaria intensification, expansion and the number of people with the disease are increasing. South America’s Malaria Endemic Area  Thermal (TCI) and moisture (VCI) vegetation condition trends in South America are shown on Fig.  8.8(Aa & b). Tendencies of these two indices assess area size and intensity trends for high (HM) and low malaria conditions during the 38-year period. Analysis of LM area and intensity (a) indicate that thermal vegetation trends are growing for two periods coinciding with a global warming trend in 1981-1997, but mismatched with flat global TA for the entire 17-year of hiatus time (1998–2014). Therefore, it is possible that intensive global warming during 1981-1997 stimulated increase TCI upward trend in South America. Following the increasing TCI trend, especially during 1981-2018, when an area with hotter vegetation surface doubled for all three

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Fig. 8.8.  Moisture (VCI) and thermal (TCI) vegetation condition trends for the malaria endemic area of South America (A), Africa (B) and Southeast Asia (C) during the three periods of global warming (intensive during 1981-1997, stably warm during 1998-2014 and 1981-2018 to evaluate the impact of strong temperature jump up during 2015-2018)

v­ egetation and malaria intensities: for moderate vegetation stress (VS) and moderate LM (RD = 124%), severe VS and light LM (RD = 159%) and extreme VS and very light LM (RD = 183%, Table 8.2). This indicates that based on temperature conditions analysis there is a very strong tendency towards malaria reduction (reduced areas and intensity) and potential tendency for decreasing the number of people with the disease in South America. Moisture index (VCI) trends for LM indicates small reduction of areas with moisture conditions (RD = -17 to -33%). Thus, reduction of moisture for all three intensities (VCI) made additional contributions to thermal condition to reduce malaria intensity and area in South America. Regarding HM, which is based on upper level (60-100) of VCI and TCI conditions ((Fig. 8.8(Ab) and Table 8.3), vegetation thermal condition area is reducing for all intensities, specifically during 1981-2018 (RD = -49 to -58%). That means that TCI-based HM conditions deteriorate in South America, slightly reducing temperature impacts on mosquitoes’ activity for spreading malaria. Meanwhile, VCI-based vegetation conditions trend is growing improving HM conditions for vector to spread malaria, since area is increasing for all intensities from highest (VCI 85-100) to lowest (VCI 60-100) gradations. Thus, VCI and TCI condition compensate their opposite contributions remaining HM activity at approximately the same long-­ term level. Africa’s Malaria Endemic Area  Figure 8.8(Ba & b) displays thermal (TCI) and moisture (VCI) vegetation condition indices’ trends for the entire endemic area of Africa for assessment of LM and HM dynamics during the discussed three periods

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Table 8.2  Low Malaria (LM): Relative difference (RD%) between the trend end and beginning for area and intensity (0-30 moderate, 0-20 light, 0-10 very light LM) Continent Conditions S. America Thermal (TCI)

Moisture (VCI)

Africa

Thermal (TCI)

Moisture (VCI)

SE Asia

Thermal (TCI)

Moisture (VCI)

% Changes from Intensity 1981 to 1997 0-30 84

% Changes from 1998 to 2014 60

% Changes from 1981 to 2018 124

0-20 0-10 0-30

83 80 -5

40 50 -5

159 183 -32

0-20 0-10 0-30

-8 -10 50

-7 -3 125

-17 -33 100

0-20 0-10 0-30

40 35 -17

150 160 -9

90 85 -11

0-20 0-10 0-30

-15 -20 39

-20 -50 96

-30 -53 43

0-20 0-10 0-30

24 39 -4

100 77 -40

41` 55 -41

0-20 0-10

-10 -8

-53 -50

-60 -62

of global warming. Thermal analysis for LM (Fig. 8.8(Ba) and Tables 8.2) indicates that temperature-assessed (TCI) malaria area and intensity (moderate, light, very light) have growing trends for all three periods with a smallest RD = 50-35% for 1981-1997, with middle RD  =  100-85% for 1981-2018 and the highest RD = 125-160% for 1998–2014. First, we should indicate that TCI-based growth matched with global TA increase for the first and third periods. However, an intensive thermal growth for the middle period did not match with flat 17-year TA trend during a hiatus time (1998–2014). Therefore, it is possible to accept that intensive growth of global TA from 1981 through 1997 stimulated an increase in the area and intensity of very light LM conditions during this period. Additionally, a contribution to very light LM conditions came from moisture index (VCI), which has slightly reduced trends for all three periods (RD = -5 to -10%, -3 to -7% and -17 to -33% for 1981-1997, 1998–2014 and 1981-2018, respectively, Table  8.2). Following this analysis, it is possible to expect that thermal (TCI) and moisture (VCI) conditions would stimulate light and very light LM reduction in malaria intensity and affected area.

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Table 8.3  High Malaria (HM): Relative difference (RD%) between the trend end and beginning for area and intensity (60-100 moderate, 75-100 light, 85-100 very light HM) Continent Conditions S. America Thermal (TCI)

Moisture (VCI)

Africa

Thermal (TCI)

Moisture (VCI)

SE Asia

Thermal (TCI)

Moisture (VCI)

% Changes from Intensity 1981 to 1997 60-100 -26

% Changes from 1998 to 2014 -39

% Changes from 1981 to 2018 -49

75-100 85-100 60-100

-43 -53 35

-55 -50 10

-58 -50 45

75-100 85-100 60-100

23 39 45

5 9 -50

72 43 -33

75-100 85-100 60-100

42 50 60

-73 -43 10

-40 -50 50

75-100 85-100 60-100

30 40 -13

12 13 -40

46 40 -23

75-100 85-100 60-100

-18 -20 36

-59 -58 40

-30 -40 110

75-100 85-100

31 33

60 28

150 140

For HM determined by TCI > 60 and VCI > 60, trends for TCI has declined and for VCI has increased during all three periods producing opposite impacts on conditions for vector activity. That means that declining trends for thermal (TCI) conditions deteriorate HM conditions, helping to decrease malaria. But increasing trends for moisture (VCI) conditions improving HM activity. As the result, TCI and VCI conditions compensate their contribution to HM activity. Combining results of malaria activity would depend on an intensity of increasing-declining trends. Following the TCI-VCI dynamics during 1981-2018, RD for TCI three intensities was -33, -40, -50 and for VCI was 50, 46 and 40. From these results. it is possible to conclude that moderate HM malaria would slightly intensified and extra HM tendency would slightly reduce. These tendencies would continue for the next few years. Finally, regarding the recent global warming impacts on temperature trends and malaria activity in Africa we should emphasise again that (a) TCI increasing trends for LM (high temperatures) matched with global TA increase during 1981-1997 and 1981-2018, while (b) TCI trends for HM (lower temperatures) have been opposite (decreasing) to global TA trend. Since the same area during the same period has two

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opposite thermal dynamics, (a) and (b), we have to conclude that the recent 40-year global warming has not affected thermal conditions and malaria activity in Africa. Southeast Asia’s Malaria Endemic Area  Figure 8.8(Ca & b) displays thermal (TCI) and moisture (VCI) vegetation condition indices’ trends in Southeast Asia for estimation of LM and HM dynamics in each of the three periods of global warming trends. Thermal analysis for LM (Fig. 8.8(Ca) and Tables 8.2) indicates that TCI-­ based trends are growing for the discussed three periods with a highest intensity during 1998–2014 (RD = 77-100%) and with moderate intensity for the two other periods (RD = 24-39% for 1981-2018 and RD = 41-55% for 1998–2014 (Table 8.2)). Following TCI’s strong upward trends, it is possible to conclude that thermal-based drought conditions have been intensifying in the past 40-year stimulating reduced mosquitoes’ activities in spreading malaria. Reduced mosquito activity are supported by a lack of long-term moisture, derived from a declining VCI trends (Fig. 8.8Ca). The declined intensity of a 38-year moisture trend was assessed at -40 to -60 of moderate to severe RD (Table 8.2). From such a combination of upward thermal conditions (TCI) trends, leading to drought intensification, and decreasing moisture (VCI) availability trends, contributing to drought intensity during 1981-2018, vector activity conditions in spreading malaria would deteriorate and malaria area and intensity for all determined levels of LM (moderate, light, very light) would be reduced. Regarding the causes of TCI strong trends increase, data in Fig. 8.8Ca and Table 8.2 indicate that intensive global warming during 1981-1998 and 1981-2018 might stimulate temperature increase in southeast Asia endemic malaria area. However, it is important to indicate that a strong TCI increase from 1998 through 2014 has not matched with a flat global TA trend during the same period (hiatus time). Thermal (TCI) and moisture (VCI) trends characterising high malaria (HM) area and intensity levels, shown in Fig.  8.8Cb and Table  8.3, indicate that during the three periods, TCI is slightly declining (RD changed from -13 to -20% of RD during 1981-1997, from -40 to -59% (RD) for 1998–2014 and from -23 to -40% (RD) for 1981-2018) and VCI is strongly increasing, especially for 1981-2018 (RD changed between 110 and 150%). Declining trends of thermal index (TCI) indicates deterioration of HM conditions stimulating some malaria decrease. But intensive VCI increasing trends for moderate, healthy and very healthy moisture conditions indicate an increase in malaria area and intensity for moderate, very healthy and extra healthy HM. An increasing moisture during 38-year is helping mosquitoes’ multiplication and intensification of their activity in spreading malaria, increasing the number of malaria cases. Since moisture (VCI) trends, characterising all three levels of malaria area and intensity, have stronger growth (RD = 110-150%) compare to TCI malty-year decline (RD changes from -23 to -40%), the combined impacts of thermal and moisture conditions during 38-year on HM will be positive, indicating improvement of conditions for mosquitoes’ reproduction and intensification of malaria distribution and increasing the number of people with the diseases. Finally, we should emphasise that all three continents showed similar 38-year moisture (VCI) and thermal (TCI) trends, characterising changes in high (HM) and

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low (LM) malaria conditions. They are helping to assess long-term mosquitoes’ activity in spreading malaria. In case of LM, which is developed during droughts, thermal (TCI) conditions in malaria endemic area are intensifying and expending over time, while moisture (VCI) conditions are deteriorating since the trend is decreasing (Figs. 8.8 A, B, C & Tables 8.2 & 8.3). The combined impacts of moisture and thermal conditions intensify drought and increase its area, reducing vector activity. For HM, TCI trend is decreasing, while VCI is increasing. This means that temperatures are slightly cooling and moisture is increasing strongly over the 38  years, improving conditions for malaria increase. We should notice that the endemic malaria area of each continent is large and some parts of the areas might be not affected by malaria. Therefore, we are continuing to examine dynamics of malaria conditions on each continent by some of the most malaria affected countries. Moisture (VCI) and Thermal (TCI) Vegetaton Condition Ttrends During 1981–2019 in Malaria-Affected Countries  Each of the three discussed continents has several countries located in malaria endemic areas. Some of the countries are more affected by malaria and some less. We selected two the most malaria-affected countries in each continent. Previously, we presented analysis of the entire global endemic malaria area and each of the three continents. Their trends had been investigated by the three period of global warming, 1981-1997, 1998–2014 and 1981-2018. Since the presented above global and continental analysis showed quite identical results, countries investigation will cover only the entire 39-year period of available data (1981–2019). The selected countries included Brazil and Colombia from South America, Nigeria and Tanzania from Africa, India and Pakistan from Southeast Asia. Brazil and Colombia from South America  Brazil and Colombia are the major contributors of malaria cases in the region, accounting for 67% of the total malaria cases (Chaparro et  al. 2013). Another three country of South America with less malaria cases but still considered as malaria-affected, are Haiti, Peru and Venezuela. In 2009, these five countries reported 91% of total malaria South America’s cases (Feged-Rivadeneira et  al. 2018). During 2000-2010, Peru and Ecuador in South America have presented a significant and stable decrease in malaria incidence. However, in Brazil and Colombia, during the same period, morbidity has remained high (Chaparro et al. 2013). The Brazilian Amazon region was found that malarial infection is more frequently observed in areas with greater forest cover and close to gold mining (Feged-Rivadeneira et al. 2018). Figure 8.9Aa presents Brazil’s thermal (TCI) and moisture (VCI) 1981–2019 trends for estimation of 39-year low malaria (LM) and high malaria (HM) conditions’ dynamics. Analysing first LM, it is seen in the figure that the thermal trends are increasing strongly for all three LM intensities. For moderate level of LM (TCI from 0 to 30 characterizes moderate drought level) relative drought area increased by 75% (RD) between 1981 and 2019. For stronger droughts: severe (TCI is from 0 to 20) and extreme (TCI is from 0 to 10) drought area increased much stronger, 120 and 200%, respectively. Relative to the entire Brazil, the three levels of LM,

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Fig. 8.9  Moisture (VCI) and thermal (TCI) vegetation condition trends for severelybmalaria-­ affected countries in endemic areas of South America (A1 Brazil &A2 Colombia), Africa (B1 Nigeria & B2 Mozambique) and Southeast Asia (C) during 1981–2019

drought-affected areas have increased between 1981 and 2019 from 28 to 49% of the country (moderate intensity), from 15 to 27% (severe intensity) and from 7 to 18% (extreme intensity), respectively. These strong thermal-based (TCI) trends increase in drought area and intensity have created unfavourable conditions for mosquitoes’ reproduction and activities in spreading malaria over 39-year. These conditions have deteriorated more since moisture (VCI) trends for LM (Fig. 8.9b,

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Table  8.4) is decreasing by -30, -40 and -50% for moderate, severe and extreme conditions, respectively. For HM (Fig. 8.9Ab), moisture (VCI) trends are increasing, while thermal (TCI) trends are decreasing during the last 39 years. Since VCI and TCI trends have opposite directions and correspondingly opposite impacts on malaria, it is necessary to estimate changes in areas and intensities of moisture and thermal conditions. Following the figure and table, 39- year moisture upward trends are stronger than downward thermal. During 1981–2019, the moisture area for moderately healthy vegetation, increased from 26 to 50% (RD is 92%), for very healthy vegetation the increase was from 12 to 26% (RD is 92%) and for the strongest, extremely healthy vegetation, the increase was from 6 to 12% (RD is 100%). Such moisture conditions stimulated improvement in vector intensive activity for HM. Reduced 39-year TCI trends indicated slight temperature reduction for all HM areas and intensities. Compared to the indicated moisture condition increase (92, 92 and 100%), 39-year thermal-based (TCI) relative area decrease was -30, -40 and -50%, which was 2-3 times lower (by absolute values of area and intensity). Following intensive moisture increase and slight thermal trend decrease, the total conditions will intensify malaria. Malaria in Colombia was characterized by a different intensity, connectivity and segregation in each region. About 85% of the Colombian rural territory is at an altitude less than 1600  m above sea level, which presents climatic, geographic and epidemiological conditions suitable for malaria transmission. Moderate deforestation fosters malarial infection. Colombia’s gold exploitation areas are also strongly affected by malaria. It is estimated that approximately a quarter (~11 million people) of its current national population (estimated at 45 million people), live permanently at risk for infection. For example, in 2010, 117,108 total malaria cases were recorded. The most affected departments were Antioquia (39.7%), Chocó (19.2%) and Cordoba (17.2%) (Feged-Rivadeneira et al. 2018, Chaparro et al. 2013). Figure 8.9B displays a 39-year moisture and thermal condition trends and Table 8.4, provides estimates of these VCI and TCI nearly four decades changes for the three intensities’ levels of LM and HM during 1981–2019. Thermal (TCI) condition trends for LM (Fig.  8.9Ba, left) is growing, which indicates that by 2019, droughts have become more intensive and occupy a larger area. Following the figure and table, three TCI intensities (moderate, 30-0, severe, 20-0 and extreme, 10-0) increased drought area to the end of 39-year period by 50, 25 and 67%, respectively. From Fig. 8.9Ba right, moisture-based vegetation conditions are slightly reduced with RD of 15-36%. This indicates a moisture reduction by 2019  in Colombia’s endemic area. Following drought intensification from both thermal (strong) and moisture (slight) conditions in LM case, conditions for mosquito reproduction and level of activity, would deteriorate leading to a decrease of malaria cases. In cases of HM (favourable conditions for vector activity), thermal (TCI) and moisture (VCI) vegetation conditions represented by their weekly dynamics and statistical trends estimation for 39-year in Colombia are shown in Fig. 8.9Bb. The thermal trends are slightly declining for all intensities (moderate, very healthy and extra healthy) from 6 to 25%, respectively (Table 8.4). Slightly cooler temperatures

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Table 8.4  Relative difference (RD%) of TCI and VCI changes (area and intensity) from 1981 through 2019; they characterise changes in conditions of LM (moderate 30 to 0, light 20-0 and very light 10-0) and HM (60-100 moderate, 75-100 light, 85-100 very light HM)

Country Brazil

Thermal (TCI) Moisture (VCI) TCI

VCI

Colombia TCI

VCI

Nigeria

TCI

VCI

Tanzania

TCI

VCI

India

TCI

VCI

Pakistan

TCI

VCI

Intensity LM 30-0 20-0 10-0 60-100 75-100 85-100 30-0 20-0 10-0 60-100 75-100 85-100 30-0 20-0 10-0 30-0 20-0 10-0 30-0 20-0 10-0 30-0 20-0 10-0 30-0 20-0 10-0 30-0 20-0 10-0 30-0 20-0 10-0 30-0 20-0 10-0

% Changes from 1981 to 2019 75 120 200 -30 -44 -50 50 26 67 -15 -28 -36 86 39 86 -15 -22 -42 300 480 700 7 -13 -23 60 40 50 -56 -56 -64 60 50 33 -23 -28 -40

Intensity HM 30-0 20-0 10-0 60-100 75-100 85-100 30-0 20-0 10-0 60-100 75-100 85-100 60-100 75-100 85-100 60-100 75-100 85-100 60-100 75-100 85-100 60-100 75-100 85-100 60-100 75-100 85-100 60-100 75-100 85-100 60-100 75-100 85-100 60-100 75-100 85-100

% Changes from 1981 to 2019 -30 -40 -50 92 92 100 -6 -10 -25 39 43 28 -28 -43 -50 60 82 80 -58 -61 -62 6 -11 -25 -8 -17 -17 172 225 300 -43 -52 -52 138 186 233

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normally improve weather conditions for mosquito activity (Bhuiyan et al. 2017, Feged-Rivadeneira et  al. 2018, Githenko et  al. 2000). Moisture (VCI) condition trends are increasing for all intensities. By the end of 2019, the moisture condition area increased by 39% for moderate HM, 43% for very HM and 28% for extra HM (Table 8.4). Following favourable moisture resources and slight temperature reduction during 1981–2019 conditions mosquitoes’ activities in spreading malaria improve for HM. Nigeria and Tanzania from Africa  The six highest malaria burden countries in the WHO African region (in the number of diseases cases) are Nigeria, Democratic Republic of the Congo, United Republic of Tanzania, Uganda, Mozambique and Cote d’Ivoire. These six countries account for an estimated 103 million (or 47%) of malaria cases in Africa (WHO 2015, 2013). We selected two the most malaria-­ affected countries, Nigeria and Tanzania for analysis of 39-year thermal and moisture vegetation conditions and potential changes in HM and LM distribution and intensity. Nigeria has a population of about 123.9 million people. A large percentage of its population lives in extreme poverty, in rural areas, without access to potable water, without appropriate healthcare and adequate protection from malaria (Carrington 2001). Most of the country is low land area with favourable climate and weather conditions for spreading malaria by mosquitoes every year. Therefore, analysis of 39-year changes in moisture and thermal conditions in Nigeria is very important for evaluation if long-term climate resource, assessed by vegetation moisture (VCI) and thermal (TCI) 39-year trends would indicate some intensification or reduction of LM and HM activity. Figure 8.9C displays moisture (VCI), thermal (TCI) weekly time series, and statistically estimated trends from 1981 through 2019 for LM, which is controlled by droughts and HM assessed by favourable moisture-thermal conditions. Following Fig. 8.9Ca, for LM thermal condition trends are increasing (left diagram), while moisture trends (right diagram) are lightly decreasing from 1981 through 2019. What does these opposite tendencies indicate for LM assessment and how intensive these trends in their impacts? As seen in the left image, moderate intensity (30-0) thermal trend area in Nigeria changed from 21% of the country in 1981 to 39% in 2019, increasing 86% (Table 8.4) over 39-year (for severe and extreme intensities the increase was 39 and 86%, respectively). For LM, such increases indicate intensification of unfavourable thermal conditions for mosquitoes, especially in case of droughts, which occurred quite often in Nigeria (Carrington 2001, Nizamuddin et al. 2013, PMI 2005, 2018). Since moisture condition trends (Fig. 8.9Ca right figure) were decreasing slightly, water supply for LM has been decreasing in intensity of thermally produced unfavourable conditions for vector activity in spreading malaria. For HM (Fig. 8.9Cb and Table 8.4), increasing moisture (VCI) trend (right figure) indicates that the area of favourable moisture conditions during 1981–2019 was increasing by 55% (area is changing from 30 to 40% of the country for moderate intensity) and by 65 and 64% for very healthy and extra healthy intensities, respectively. These assessments of moisture conditions indicate that HM can be classified

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as having better moisture conditions for all intensities (moderate HM, very HM and extra HM). Following an increase in HM moisture area and intensity during 1981–2019, conditions for mosquitoes’ reproduction and activities in spreading malaria become more favourable. As seen in Fig.  8.9Cb left, thermal condition trends are slightly decreasing by the 2019, indicating temperature reduction. Meanwhile this temperature decrease is negligible for reduction of mosquitoes’ activities. Therefore, total impacts of moisture and thermal conditions change during 39-year would stimulate favourable conditions for intensifying malaria in Nigeria. Malaria is considered one of the deadliest diseases in Tanzania, with many reported cases. Each year, 10 to 12 million people contract malaria in Tanzania and 80,000 die from the disease; most of them children. Malaria transmission takes place all year round with a seasonal peak extending from December to April. Rainfall and temperature can be considered the major natural risk factors affecting mosquito the life cycle and breeding habits. Malaria is an enormous economic burden in Tanzania. Analysis at the individual household level found that Tanzania households incurred catastrophic payments for malaria episodes compared to South Africa, (Zacarias and Andersson 2011, Rahman et al. 2006, 2010, 2011. Castillo-­ Riquelme et al. 2008). In general, weather supports vector activity for malaria distribution. However, in some years, moisture-thermal drought conditions are developing reducing mosquitoes’ activity and the number of malaria cases. Therefore, since in the past half a century climate was warming, it is important to investigate changes in vegetation health in order to determine if drought is intensifying. Figure 8.9D displays a 39-year trend of vegetation moisture (VCI) and thermal (TCI) conditions for LM and HM. Left diagram of Fig. 8.9 Da is assessing drought activity in LM case from thermal conditions. The diagram indicates a considerable area increase with hotter temperatures in Tanzania. For example, in case of moderate (30-0) LM conditions (Table 8.4), the area of hotter conditions increased from 12% of the country in 1981 to 45% in 2019 (275% growth over 39 years). If hotter thermal conditions become severe and extreme, the area increased from 4 to 29% (625% growth) and from 2 to 13% (550% growth), respectively. Since droughts in Tanzania have always been an important component of the country’s climate, such strong changes in thermal conditions indicate that thermal drought intensified and expanded by 2019, deteriorating considerably mosquito activity in spreading malaria. A very interesting result was obtained for LM moisture conditions (right diagram for case (a)). For moderate moisture conditions (30-0), the trend is slightly increasing (around 7%) intensifying moisture supply. However, for stronger conditions, moisture trends decreasing from 1981 to 2019. Thus, for severe moisture conditions, the area decreased from 17.4 to 15.8% (-9% reduction) of Tanzania and for extreme conditions, the area decreased from 10.9 to 8.4% (-23% reduction). High malaria (HM) conditions were present in Tanzania to assess favourable conditions for vector activity in distributing malaria. As seen in Fig. 8.9Db, thermal trends (left diagram) is declining, indicating a decrease in temperature area and intensity. For moderate level (30-0) of vegetation health, characterizing moderate

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level of HM, the temperature area dropped from 36% of the entire Tanzania in 1981 to 15% in 2019, which indicates a 58% reduction (Table 8.4). For other two stronger thermal vegetation conditions intensities (20-0 and 10-0), which characterize very HM and extra HM, respectively, areas with reduced thermal conditions decreased from 18 to 7% (RD -61%) and from 8 to 3% (RD -62%, Table 8.4) by 2019. Such changes in thermal conditions might deteriorate malaria activity. Regarding moisture conditions (Fig.  8.9Db right), 39-year changes in area and intensity have a negligible increase (6%) for moderate intensity but a slight reduction for very healthy and extremely healthy vegetation conditions, which are translated to very and extra HM. Following these results, HM conditions will be slightly reduced for high vector reproduction and activity. India and Pakistan from Southeast Asia  For approximately 1.4 billion people living in 11 countries of Southeast Asia, 1.2 billion are exposed to the risk of malaria. According to FAO (2018) estimates, Southeast Asia has nearly 15 million malaria cases and 20,000 deaths annually, of which India has about two million confirmed malaria cases and 1000 deaths (Kumar et al. 2007). India is working to fight malaria, however the disease continues to be one of India’s leading public health problem. In addition to social and economic causes of malaria in India, the annual number of malaria cases depends on climate and weather. In drought years, the number of malaria cases decreases, while moist and warm weather stimulates mosquito activity in spreading malaria. Since global climate in the recent 50-70 years warmed up strongly, it is important to investigate if warmer weather simulated drought intensification and expansion reducing malaria activity. Figure 8.9E presents changes in thermal and moisture vegetation conditions trends from 1981 through 2019. These changes are evaluated by area and intensity. In case of LM, associated with unfavourable conditions for vector activity, thermal conditions during the 39-year increase of area and intensity of stress vegetation, indicates that drought area and intensity are deteriorating mosquito activity. The Fig. 8.9Ea (left) indicates that for moderate (30-0) vegetation stress (corresponding to moderate LM) the area increased from 25 to 40% of the country (with RD or intensity of increase 60%). For stressed (20-0) and extreme (10-0) stress from 15 to 21% (RD 40%) and from 8 to 12% of India (RD 50% Table 8.4). Such increase of vegetation stress (or moderate to very light LM conditions) indicates that drought intensified and expanded over India making conditions for vector activities unfavourable. These conditions were offset by moisture conditions (VCI), since VCI trends for LM (Fig.  8.9Ab, Table  8.4) is decreasing. The area of moisture stress reduced between 1981–2019 by -30, -40 and -50% for moderate, severe and extreme conditions, respectively. Since moisture and thermal conditions were almost equal in intensity of a 39-year change but opposite in the direction of trend, the total impact of climate change on malaria conditions has not changed. For HM levels, moisture (VCI) conditions for healthy vegetation experienced a very intensive upward trend (Fig. 8.9Eb right and Table 8.4). During 1981–2019, moderate vegetation health (corresponding to moderate HM) area increased considerably, from 18 to 48% of the country (RD 172%). For stronger HM intensities

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(very HM and extra HM) the areas of better moisture conditions changed from 7 to 27% (RD 225%) and from 3 to 13% (RD 300%), respectively. A strong increase in area and intensity, improved conditions for mosquito activity in spreading malaria. Thermal conditions of healthy vegetation have practically not changed during the 39-year period (right diagram). For moderately healthy vegetation (corresponding to moderately HM), thermal area changed from 24 to 22% of the entire country, decreasing 8% (RD). For very and extra healthy vegetation, (very HM and extra HM, respectively) thermal condition area dropped from 12 to 10% (RD 17%) and from 6 to 5% (RD 17%) in India, respectively. Such negligible thermal conditions change impacted very little the m vector activity. Thus, combined conditions for vector activity in case of HM turned favorable for spreading malaria in India. Pakistan has a population of 180 million inhabitants of which 95% are at risk for malaria. In Pakistan, more than 300,000 cases of malaria are reported every year. The country has a long latitudinal extent stretching from the Arabian Sea in the south to the Himalayan Mountains in the north. It is located in the sub-tropics and partially in temperate region. The biological activity and geographic distribution of the malarial parasite and its vector are sensitive to climatic influences, especially temperature and precipitation (Farooqi et al. 2005, PMI 2005). Assessment of the potential impact of global climate change on the incidence of malaria suggests a widespread increase of risk due to expansion of the areas suitable for malaria transmission (WMO 2018, IPCC 2007, 2014). Since malaria is an important issue of population health and climate warming is reported to have stimulated an increase in malaria transmission in Pakistan, we are investigating 1981–2019 moisture and thermal condition trends through satellite based on assessment of vegetation health for further evaluation of LM and HM activities. Figure 8.9F shows the dynamics of moisture and thermal conditions of stressed and healthy vegetation, which characterizes vector activity in Pakistan in case of low and high malaria (LM and HM) situations. In case of LM (Fig. 8.9Fa), which characterizes thermal and moisture conditions of stress vegetation, they experienced a 39-year trend of different directions and intensity. As seen in the right figure, stressed vegetation (or LM) areas of thermal conditions are growing from 25% of the entire country in 1981 to 40% in 2019 for moderately stressed vegetation. For severe and extreme vegetation stress, the area changed over 39-years from 16% to 24% and from 9 to 12%. The intensity (RD) of these changes was 60, 50 and 33% (Table 8.4). This indicates that thermal drought in case of LM is intensifying and expending, deteriorating mosquito activity. Moisture condition trends assessing moderate, severe and extreme vegetation stressed area is declining from 26 to 20%, from 18 to 13% and from 5 to 3% of Pakistan area with intensities (RD) -23, -28 and -40%. Although moisture conditions slightly offset opposite thermal conditions, but since thermal impact was stronger, the total conditions for vector activity were unfavourable. For HM (Fig. 8.9Fb and Table 8.4), moisture (VCI) area and intensity for Healthy Vegetation is increasing from 16 to 38% for Pakistan for Moderately Healthy Vegetation (corresponding to Moderately HM), from 7 to 20% of Very Healthy Vegetation (Very HM) and from 3 to 10% of Extremely Healthy Vegetation (Extra

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HM). Thirty-nine years of RD for three intensities increased 138, 186 and 233%, which is considerable intensification of moisture conditions for increasing vector activity. The left diagram of the same figure is showing thermal conditions trends for Healthy Vegetation. Opposite to moisture, thermal condition trends are slightly decreasing. For Moderately, Very and Extra Healthy Vegetation (corresponding to Moderately, Very and Extra HM) the areas of thermal conditions decreased between 1981 and 2019 from 26 to 20%, from 18 to 13% and from 6 to 3% of the country. The 39-year intensity of thermal trends decrease was -23, -28 and -50%. Such small thermal trends reduction would not affect vector activity in spreading malaria. In summary, it is important to emphasize that we investigated a 39-year thermal and moisture trend of stressed and healthy vegetation as an indicator of malaria conditions in extreme cases (low malaria cases and very many malaria cases). When vegetation is stressed during drought, vector activity in spreading malaria is reduced considerably. Healthy vegetation developed in favourable environmental conditions, intensify malaria activity. These extreme malaria conditions were named LM and HM, respectively. Trends of healthy and stressed vegetation determine general direction of HM and LM. Following trend analysis in all countries, it was identified that for LM, thermal trends are increasing in both area and intensity. This indicates that thermal conditions for malaria spreading are deteriorated due to thermal drought intensification and expansion. Such conditions are generally unfavourable for mosquito activity to spread malaria. Moisture trends for LM is decreasing, indicating a moisture deficit contributing to further deterioration of LM conditions. HM conditions are opposite to LM.  Moisture trends for HM are increasing while thermal trends are decreasing. This indicates that environmental conditions for mosquitoes’ activity are improving over time.

8.9  Conclusion The goal of this chapter was to investigate how global warming in the past four decades (since 1981) affected global and regional malaria. It is known, that moisture and temperature are the leading environmental components affecting malaria’s parasite development and mosquito activity in transmitting the disease. The main concerns are if these changes worsened or improve, it will lead to an increase or decrease in the number of people affected by the disease. This goal was also dictated by claims from many scientific and media publications that a warmer world has intensified and expanded droughts and deteriorated moisture conditions (UNESCO 2018, Rice 2018, Watts 2018, AG 2017, Forzieri et al. 2017, Freedman 2017, ClimateBet 2018, IPCC4 2007, IPCC5 2014, NOAA/NCDC 2016, 2017, Schlossberg 2016 etc.). Therefore, we also investigated these changes because drought intensification might reduce malaria. In order to determine global warming impacts on malaria, we needed to assess also the stability of warming and changes in malaria conditions. Having a 38-year well validated satellite-based Vegetation Health data, characterizing vegetation greenness and surface temperature, moisture/thermal conditions,

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vegetation stress, drought area and intensity, the exact environmental parameters affecting malaria distribution and intensity. Therefore, the goal was to investigate if these parameters followed global warming trends. Following these goals, we have also analysed scientific literature on the intensity of global warming and its causes. Many publications and international actions (Myneni 1997, IPCC5 2014, IPCC4 2007, UNESCO 2018, UNFCCC 2015, 2014, WEF 2016, WMO 2016 etc.) agreed that the world is warming and that the cause of this event is CO2 increase, since CO2 traps infrared radiation emitted by the Earth. Following IPCC (2014) reports and other publications, CO2 long-term upward trend generally matches a similar global TA trend. Therefore, CO2 was considered the major cause of a warmer Earth (Gore 2006). Our detail analysis of global temperature anomaly indicated that there is a general uprising trend in global TA since 1850; six such 17–35-year trends. During the entire 150-year period, two trends were declining, two were flat and two were increasing. A reasonable question to ask is why. Initially low, during the industrial revolution, did a strong CO2 increase change the global mean TA trend six times? Moreover, why, after a period of intensive global warming from the early 1970s through 1997, during a 17-year (1998–2014) period after 1997 (named “hiatus” time (Karl et al. 2015, Kennel 2014)), global TA had a flat trend, while CO2 continued strong increase? A few other interesting facts regarding global warming. Following a stably flat global TA trend from 1998 through 2014, during 2015-2018, global TA was the highest in the past 150 years. Some scientific publications blamed a very strong El Nino of 2015-16 for the warming. Others blamed solar activities, ozone (O3) depletion, etc. However, there is no definite explanation as to why and how long this will continue. Following these contradicting facts, 38-year of VH data were used to verify if global warming tendencies have matched with tendencies in vegetation greenness and temperature, moisture-thermal stress, drought and others. Three periods of different global warming trends were selected: (a) very intensive TA trend’s increase during 1981-1997, (b) flat TA trend during hiatus time (1998–2014) and (c) an intensive TA jump up from 2015 through 2018. Since 4-year period (c) is too short for trend estimate, we estimate the impacts of 2015-2018 TA on land surface change by the analysis of the 1981-2018 trend. Greenness and temperature trends’ analysis for malaria endemic area of the world and three continents indicated that G and T were increased slightly, with RD =1-8%, compared to a very intensive global TA growth in 1981-1997 and 2015-2018. This indicate that global warming has not affected vegetation G and T on a huge tropical area (20 million km2) severely exposed to malaria. Similar results show no or limited impact of global warming on drought trend, both globally and regionally, which were obtained earlier (Kogan 2018). Following the 38-year  G and T changes in the malaria endemic areas of the world, it is expected that current G and T trends would continue in the next few years, even if global TA will continue its tendency as established during 1981-1997 and 1998–2014. It is known, that long-term malaria is strongly affected by economic, political, social and climate factors. Annual (seasonal, monthly, weekly) malaria area and

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intensity depends much on weather conditions. Malaria is normally reduced by droughts and increases from warm and wet weather. These conditions are evaluated by the Vegetation Health indices, which assess moisture (VCI), thermal (TCI) and combined moisture-thermal (VHI) conditions of vegetation. It was shown in Chap. 6, that the number of malaria cases correlates strongly with VCI and TCI. Therefore, VCI and TCI values were used to develop a 39-year time series assessing drought area for the three strongest area. The same assessment (moderate, severe and extreme) was developed for favorable conditions. Drought from TCI and VCI time series would assess low malaria (LM) due to low moisture and high temperature. Time series for favorable conditions would assess high malaria (HM) identifying if moisture and temperature would deteriorate conditions. The entire malaria endemic area was assessed with combined moisture-thermal index (VHI). The results of analysis indicate that during an intensive global warming between 1981 and 1997, moisture-thermal (VHI) conditions in the entire malaria endemic area declined slightly (RD is 1-2%) for both LM and HM. During the hiatus time (1998–2014) of stably flat global TA, areas of stressed vegetation experienced slight upward trends for all intensities (from 5% of the global malaria endemic area at the trend beginning to 11% at the trend end for moderate stress, from 2.5 to 4% for severe stress and from 1 to 2% for extreme stress. These trend increase would indicate a gradual reduction of malaria area for all intensities. Opposite to stressed, healthy vegetation experienced slight declining trends (from 14 to 9% for moderate (light blue), from 4 to 3% for very healthy (dark blue) and from 2 to 1% for extra healthy (red)). This declining trend confirmed that HM activity is declining, due to dryer conditions. For the period from 1981 to 2018, which included a strongest TA increase between 2015-2018, both healthy and stressed vegetation had very slightly declining VHI trends with RD (between trend’s beginning and the end) nearly 1%. Malaria endemic areas for three continents (South America, Africa and Southeast Asia) were investigated with moisture (VCI) and thermal (TCI) indices. All continents showed similar 38-year moisture (VCI) and thermal (TCI) trends, characterising changes in high (HM) and low (LM) malaria conditions. In case of LM, characterizing droughts, thermal (TCI) trend is growing, while moisture (VCI) trend is decreasing. This indicates that drought is intensifying and malaria transmission is reducing. For HM, the TCI trend is decreasing, while VCI is increasing. This means that temperatures have slightly cooled and moisture slightly increased over 38 years, improving malaria activity. In addition to continents, two of the most malaria-affected countries on each continent were investigated. The analysis was done for the 1981–2019 (39-year). Following a trend analysis in each country, it was identified that for LM, thermal trends are increasing (for both area and intensity) indicating that thermal conditions for malaria spreading deteriorated due to thermal drought intensification and expansion. Such conditions are generally unfavorable for mosquito activity to spread malaria. Moisture trends for LM is decreasing, indicating a moisture deficit contributing to further deterioration of LM conditions. HM conditions are opposite to LM.  Moisture trends for HM situation are increasing while thermal trends are

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decreasing. This indicates that environmental conditions for mosquitoes’ activity are improving over time.

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Chapter 9

Main Points to Think About

Abstract  Malaria is very dangerous considering the number of affected and dead people and a large number of poor countries with intensive population growth. Malaria is also complicated since the new satellite-based Vegetation Health technique for malaria prediction requires development of malaria-VH model for several dozens of countries, unknown impacts of global warming on malaria spreading and intensity, unknown future climate trend and others, this chapter is focusing again on main points of the problems in a short format. There are many challenges to fight malaria. The most important is that malaria still continues to be the major public health problem. Climate and weather are the most important environmental problems affecting malaria intensity and distribution. It was shown that malaria conditions can be predicted based on the new satellite-based Vegetation Health technology (1–2 months in advance) and based on ENSO and VH (3–5 months in advance). A very serious discussion was going about global warming and impacts on vegetation (vegetation greenness, temperature, moisture and thermal conditions) and malaria distribution and intensity. An important attention was devoted to malaria-VH modeling and assessment of long-term tendencies of LM and HM conditions. Keywords  Malaria challenges · Climate · Weather · Vegetation health conditions today and in the future

9.1  Why Malaria? Malaria is the deadliest parasitic human infections, accounting for millions of clinical attacks worldwide annually. Malaria is endemic to 109 countries around the world, and is responsible for over 200 million clinical cases and more than a million deaths each year. (WHO 2019, 2018a, b, 2017a, b, 2016, 2015, 2008, 2005, Nizamuddin et al. 2013, a, Rahman et al. 2011, a, 2010, 2006, Montanari et al. 2001, Faiz et  al. 2002). Based on WHO estimates the number of global malaria cases would be around 219–223 million in 2018 and 2019, with 435 thousand expected death (WHO 2019, 2018a). Death? Malaria has caused almost one fifths of © Springer Nature Switzerland AG 2020 F. Kogan, Remote Sensing for Malaria, Springer Remote Sensing/ Photogrammetry, https://doi.org/10.1007/978-3-030-46020-4_9

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estimated annual worldwide deaths. Between 1.5 and three million people die annually, accounting for 4–5% of global fatalities (WHO 2018a, b, 2017a, b). Children are the most vulnerable, since up to 50% of the estimated annual malaria mortality fall on persons less than 15 years of age (CDC 2018). Nearly one million of pregnant women die annually from malaria.

9.2  Affected Regions Malaria occurs mostly in tropical and subtropical areas of the world, where nearly half of the world population (3.2 billion people) resides (CDC 2018). Each year, nearly 40% of the world’s population, living mostly in the poorest countries of Africa, Southeast Asia and South America, are at risk of malaria. The Africa is contributing 60% to the global malaria cases, with 18% children dying under the age of five (USAID 2007)). From 1216 billion people living in Africa, 66% are at risk of malaria each year. For Asia’s 4.463 billion total population, 49% of the people live under threat from this disease, most of them are living in 11 highly populated countries. Every year, nearly 2.5 million of malaria cases are reported in these countries, 75–85% of which are reported from India. In South America, most malaria cases come from the Amazon rain forest areas in northern countries (WHO 2018a, CDC 2018).

9.3  Cost and Funding Malaria is an extremely costly disease. According to US estimates, global malaria accounts for 25–35% spending of all outpatient visits, 20–45% of hospital admissions and 15–35% of hospital deaths (WHO 2018a, 2017b, 1999, MMV 2017, CDC 2018, Sachs and Malaney 2002). The global financial input to malaria control since 2012 averaged US$ 250  million per year. The main sources of funding are the Global Fund to fight AIDS, Tuberculosis and Malaria, the United States President’s Malaria Initiative and the World Bank’s Booster Program (WHO 2008b). Annual malaria cost in Africa is estimated at $12 billion, accounting for 40% of Africa’s spending for all health cases (CDC 2018, MMV 2017). In addition to human burden and death, malaria also imposes drastic economic losses (Nagpal and Sharma 1995). Total funding for malaria control and elimination reached an estimated US$ 2.7 billion in 2016; contributions from governments of endemic countries is up to US$ 800 million, accounting for 31% of all health funding (WHO 2018a, b, CDC 2018, UCAR 2018, MMV 2017).

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9.4  Fighting Malaria-fighting includes malaria control and prevention, treating sick people, drug distribution, sleeping under a mosquito net treated with insecticides that kill mosquitoes or stop them from biting, spraying insecticides inside dwellings that leave a residue on walls, special protection for pregnant women using insecticide treated nets (ITNs) and intermittent preventive treatment with antimalarial drugs given as part of normal care, rapid treatment with effective antimalarial drugs for anyone suspected of having malaria can save lives, plus improved early warning, detection and response to malaria epidemics to avert catastrophe (WHO 2017a, b, 2015, 2005, 2002, Bhatt et al. 2015).

9.5  Challenges Although a very considerable success has been achieved over the recent 40–50 years in drug production to treat malaria, success of vaccine and insecticides to treat mosquito habitats, distribution of nets, stable funding for malaria prevention and eradication etc., there are today many challenges facing malaria impacts on humans and malaria still continues to be the major public health problem, especially in poor countries of sub-Sahara Africa, Southeast Asia and South America (Raghavendra et al. 2011). Todays’ challenges are still existing since:: (a) Malaria can be transmitted to people of all ages; (b) Children under age 5, pregnant women, hungry people are the most vulnerable; (c) Increasing mosquitos’ resistance to insecticides and parasite resistance to drugs; (d) Changes in mosquitos’ behavior due to continuous treatments; (e) Ecology changes due to human activities; (f) Chemicals limitation for vector treatment; (g) Intensive population growth; (h) Economic, political and social problems; (i) Environment: Strong climate and weather impacts.

9.6  Environment and Malaria Climate and weather are some of the most important environmental problems affecting malaria intensity and spread. The mosquito vector, which transmit malaria, is the main contributor to malaria burden and correspondingly to the number of malaria cases (CDC 2018, Shiv et al. 2010, WHO 2005). Vector activity and ability to transmit malaria changes from year to year depending on weather conditions. Principally, a mosquito vector is extremely active in wet and warm conditions. If the conditions are hot and dry, the vector activity reduces considerably, leading to a smaller number of people infected with malaria. Therefore, weather parameters (precipitation, temperature, etc.), collected by meteorological stations, have been used traditionally as the indicators for monitoring malaria epidemic (Thomson et al.

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2006, 2000, Thomson and Connor 2001). Long-term records of precipitation and temperature from weather stations are also used for analysis of strong climate warming impacts on malaria trends in the recent 4–5 decades. Unfortunately, number of weather station is limited, especially in tropics, and they do not have uniform spatial distribution creating problems for using weather and climate data for effective malaria monitoring. Therefore, operational environmental satellite data were investigated and the new technology was developed as a tool for malaria monitoring (Nizamuddin et al. 2013, a, Rahman et al. 2011, a, 2010, 2006, Remme et al. 2001).

9.7  Operational Environmental Satellites In the second half of the twentieth century, following demands for more accurate environmental monitoring of the Earth, two satellite systems, geostationary (GOES) and polar-orbiting (POES) were developed to observe the ocean atmosphere and land for more accurate monitoring, modeling and prediction of environmental impacts on economy, land-ocean cover and human life. The POES satellite systems equipped with two sensors AVHRR since 1981 and VIIRS since 2012. collected data applied for analysis of climate, weather and land cover changes, monitoring crops, human health, food security, disasters (drought etc.), vegetation stress and others (Kogan et al. 2017). Since the late 20th, these data have been used for malaria monitoring.

9.8  Vegetation Health Method Considerable success in application of POES data has been achieved with development of Vegetation Health (VH) method. The VH method was developed for space-­ based monitoring moisture, thermal and total moisture-thermal conditions of vegetation. The VH consists of three indices Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI). The VCI, TCI and VHI derived moisture, thermal and moisture-thermal conditions of vegetation, respectively. These indices have strong theoretical background since were developed following three biophysical laws: Law of Minimum, law of Tolerance and Law of Carrying Capacity (Kogan 1995, 1997, 2000, 2001, 2002). The VH indices data has high spatial (0.5, 1 and 4  km2 land surface) and temporal (one week) resolution for 39-year (1981–2019). They estimate moisture and thermal vegetation conditions and, what is the most important, products (drought, moisture/ thermal stress, healthy vegetation, fire risk, soil moisture, malaria, crop production etc.) used for monitoring the environment and socioeconomic activities. These indices is currently used for malaria monitoring and assessment of climate warming impacts on malaria.

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9.9  Malaria-VH Models It is known that moist and warm weather stimulates mosquitoes’ activity in spreading malaria, while dry and hot weather suppresses vector activity, reducing malaria transmission. Therefore, scientists received good results correlated precipitation and temperature with the number of malaria cases received from the hospital (or small region) located not far from weather station. However, for the entire country or large region, malaria monitoring has not been successful since the weather station network is very limited and stations are spread far apart from each other. Therefore, we used high-resolution VH indices for malaria-VH modeling. Since vegetation is the place of mosquitoes and parasite habitat, VH-based vegetation moisture and thermal conditions provided an excellent indication of annual mosquitoes’ activity in spreading malaria. Malaria-VH models were developed for a few countries and regions in Southeast Asia the models were developed for the entire Bangladesh (large-area model), three administrative divisions of Bangladesh (Chittagong, Dhaka and Sylhet, which represented mid-size area) and Bandarban district from the Chittagong division (small area). Validation indicated that independently predicted and observed malaria cases correlated with R2  =  0.78–0.81. In India, malaria-VH models were developed for two administration divisions Tripura and Orissa. The results of validation were similar to Bangladesh divisions. (Nizamuddin et al. 2013, a, Russel et al. 1963, Rahman et al. 2011, a, 2010, 2006).

9.10  ENSO – Climate Event ENSO, an abbreviation for the El Niño–Southern Oscillation, is an oceanic-­ atmospheric climate phenomenon that develops over the Pacific Ocean. ENSO produces periodic variations of sea surface temperatures (SST), air pressure and winds, affecting the world climate and weather. Being a climate-forcing event, ENSO disturbs annual/seasonal weather worldwide. ENSO stimulate normally development wet/cool and dry/hot weather, which creates certain type of vegetation health (L'Heureux 2014). ENSO has two phases: El Niño - warm phase (SST above normal) and La Nina – cool phase (SST below normal), (NOAA/NWS 2019). A criteria of ENSO intensity is SSTa during November–January in Nino 3.4 area of the Pacific Ocean. When ENSO starts, it is expected that in the next 3–5 months some world regions, especially in tropics and subtropics, will have certain type of weather, and Vegetation Health. Therefore, we used SSTa as a predictor of vegetation health and its impact on malaria development (Kogan and Guo 2016).

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9.11  VH-Malaria Prediction From ENSO Seasonal malaria-VH models, developed in Chap. 6, provide 1–2 months prediction of vector activity in spreading malaria. Such prediction is important for an early preparation to fight with malaria (distribution of nets, drug preparation, application of insecticides etc.) and predict the number of people with the disease. In Chap. 7, we tested ENSO signal for 3–5 months advanced prediction of vector activity in spreading malaria. We first, identified global area, where vegetation health is strongly affected by ENSO and second, estimated vector activity in spreading malaria from VH values. Correlating SSTa with VH indices for each 4 km2 earth surface, we derived area and intensity of vegetation conditions and the corresponding potential number of malaria-affected people. Following Kogan and Guo (2016), and Kogan (2011), these results were successful in some countries of sub-Sahara Africa, northern South America, eastern Southeast Asia, especially India.

9.12  Global Warming The IPCC reports (IPCC 2007, 2014, 2018). and other scientific publications (UN 2016, NOAA/NWS 2019, UNESCO 2018, NOAA/NCDC 2017, 2016, NOAA/ NCEI 2017, USGCRP 2017, Gore 2006 etc.). have presented results of the global temperature anomaly (TA) that since the mid-eighteenth century, the Earth’s climate has been warming up. In the past 60-year, Earth warmed up intensively, leading to never before experienced environmental, economic and social events. Many scientific publications indicate that in the past 50 years of environmental observations showed global changes in snow and ice areas, sea level, natural disasters, biological systems (plants, birds etc.) and others. Many publications indicated that climate warming has negatively affected crop yield, especially in underdeveloped countries of Africa, Asia and Latin America. One of the biggest climate warming concerns is how these changes have affected malaria and what to expect in the near and distant future, considering continuation of climate warming and an intensive population growth.

9.13  Climate Warming From CO2? The IPCC reports (IPCC 2007, 2014, 2018) and other publications (for example, Gore 2006) have strongly emphasized that the CO2 increase has induced global warming with some consequences for the environment and society. This conclusion was based on strongly matching generally upward global TA and CO2 trends from 1850 through 2014. However, detail analysis of the entire 164-year trends indicated that CO2 has stable increasing trend, while global temperature anomaly

9.15 Very Warm 4 Years

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experienced six 17–35-year up and down trends. Four of these TA trends completely mismatched with CO2 continuously increasing trend. Specifically, they mismatched: from 1850 through 1880s (TA has flat trend, while CO2 was increasing), from 1880s to 1910 (TA was decreasing, while CO2 increasing), from1910 to mid1940s (TA was decreasing, while CO2 increasing), from mid-1940s to mid-1970s (TA was decreasing, while CO2 was increasing strongly) and from 1998 to 2014 (hiatus time) TA has flat trend, while CO2 was increasing strongly. A reasonable question to ask is why, at the background of continuous CO2 increase the global mean TA has changed its trend six times. Only during two periods the TA trends were matching, while during four periods were mismatching with CO2 trends.

9.14  May Be From Ozone? One of the other ideas of global warming was ozone (O3) depletion in the lower stratosphere. Following Ward (2016), during 1981–1997, there was a 17-year match in the increasing trends of global TA, CO2 and O3 depletion, while during the next 17-year from 1998 through 2014, global TA and O3 had matching flat trend (no increase/no decrease), mismatching with CO2 strongly increasing trend. As Dr. Ward (2016) explained that the intensive global warming during the early 1970–1997 was due to chlorine-induced ozone depletion in the atmosphere and a flat TA trend during 1998–2014, due to a slow ozone restoration. The explanation is that ozone (O3) in the stratosphere is shielding Earth surface from UV-B radiation. The process of ozone depletion began in the 1960s following human activities in using widely chlorofluorocarbon gases (CFC). By 1974, scientific research has shown that when CFC gases roused to the lower stratosphere, they are broken down by UV-B radiation, resulting in a release of chlorine, which destroys the ozone.

9.15  Very Warm 4 Years In 2015, global TA suddenly increased considerably and the world was very warm during 4-year (2015–2018). The global TA increased almost two times from 0.22 °C at the end of 1998–2014 trend to 0.42  °C in 2015. The CO2 from 2014 to 2015 increased only 1% (from 397 to 400 Dobson Units). Therefore, CO2 contribution to doubling global mean TA between 2014 and 2015 cannot be considered as one of the causes of strong 2015–2018 TA increase. What was the cause? Most probably, this TA increase was due to strong El Nino of 2015–16, intensive water warming south of Alaska (the “Blob”) in 2014–15, possible solar activity and volcano eruption (Kogan 2018). Unfortunately, only 4-year (2015–2018) of strong TA increase after the two 17-year TA stable trends can’t explain what to expect with future global TA. Therefore, further discussion related to global warming will be focused on vegetation (greenness, temperature, health etc.) during the three periods of global

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TA trends: 1981–1997 (strong global warming), 1998–2014 (flat global TA trend during hiatus time) and 2015–2018 (very strong unexpected increase in global TA). Since 4-year period is too short for trend estimation, the investigation will cover the entire 1981–2018 and even 2019 trend’s periods.

9.16  Vegetation Greenness (G) and Temperature (T) If vegetation is green and temperature is warm mosquitoes are extremely active to spread malaria. Therefore, it is important to know if global warming has supported these conditions in malaria endemic area. From 39-year VH timed series, it was found that G and T trends for the global malaria area and the three continents were (a) growing slightly (relative difference (RD) between the trend’s end and beginning is only 1–8%). during the investigated three periods (1981–1997, 1998–2014 and 1981–2018); (b) this increase was negligible compared to global TA intensive growth in 1981–1997 and 2015–2018. These two summaries are very important because the area of analysis is huge, nearly 20 million square kilometers, representing around 15% of Earth’s ice-free land cover. Since the intensive global warming has increased only slightly vegetation G and T, vector activity would continue to support vector activity in a huge tropical malaria endemic area in the next few years.

9.17  What Is Low and High Malaria? Since G and T trends indicated general support for mosquitoes’ activity, we tested extreme malaria conditions. The VH indices are changing from zero, indicating extreme vegetation stress, to 100 - very healthy vegetation. Extremely unfavorable conditions are developing when VH is below 30 (mostly due to drought). Favorable conditions are developing if VH is larger than 60. These conditions are identified as low malaria (LM) and high malaria (HM). IF vegetation is stressed (VH  60), the HM is developing, when vector becomes more aggressive in spreading malaria, increasing the number of people with the diseases. Three levels of HM and LM were developed based on our drought investigations Tree vector intensities were developed for HM and LM. For HM, the VH levels’ range 60–100 was determined as moderate HM, the range 75–100  - very HM and the range 85–100 - extra HM (the largest number of malaria cases). For LM, the ranges were 0–30 - moderate LM, 0–20 – strong LM and 0–10s – very strong LM (the smallest number of malaria cases).

9.19  Continental Malaria From VCI and TCI

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9.18  G  lobal Malaria From Moisture-Thermal Vegetation Health Global malaria endemic area was investigated with VHI (moisture-thermal index). Mean total malaria endemic area weekly VHI time series were produced for three periods of global TA trends (1981–1997, 1998–2014 and 1981–2018). The VHI trends were developed only for three levels of HM (moderately healthy (mh), very healthy (vh) and extra healthy (eh)) and LM (moderately stressed (ms), severely stressed (ss) and extremely stressed (es)). During an intensive global warming in 1981–1997, moisture-thermal (VHI) trend declined slightly for both LM and HM and for all intensities, (RD is between −1 and − 4%). Therefore, intensive global TA increase during 1981–1997 has not affected some reduction of LM and HM, assessed by VHI. During the hiatus time (1998–2014) of stably flat global TA, an area of LM’s stressed vegetation experienced a slight upward trends for all intensities (from 5% of endemic area at the trend beginning to 11% at the trend end for moderate stress, from 2.5 to 4% for severe stress and from 1 to 2% for extreme stress). These VHI trends indicate a gradual increase of the malaria area for all intensities (due to droughts intensification and expansion) and potential reduction of malaria cases for LM. Opposite of LM, HM (healthy vegetation) is characterized by a slight declining VHI trends (from 14 to 9% of endemic area for moderate, from 4 to 3% for very healthy and from 2 to 1% for extra healthy). This declining trend confirmed that HM activity is declining (with deterioration of vector activity), due to dryer conditions. The final analysis investigates vegetation trends during the entire period, 1981–2018. Both healthy and stressed vegetation had a very slight declining VHI trend with RD (between beginning and the end) nearly 1%. The moderate area of HM and LM are growing up by 13 and 10% of RD. The other intensities for both HM and LM are covering areas of 4% and 1%. The LM and HM trends identified from (combination of moisture and thermal condition index (VHI) have not changed during the period of intensive climate warming form 1981 through 2018.

9.19  Continental Malaria From VCI and TCI Malaria in South America, Africa and Southeast Asia was investigated with 38-year of moisture (VCI) and thermal (TCI) vegetation conditions indices for LM and HM. Two goals were set up: (a) contribution of long-term moisture (VCI) and thermal (TCI) vegetation conditions in changes of malaria area and intensity and (b) if global warming during the three periods stimulated changes in malaria activity. Analysis of LM area and intensity indicate that thermal vegetation trends are growing for two periods coinciding with a global warming trend in 1981–1997, but mismatching with flat global TA for the hiatus time (1998–2014). Therefore, it is possible that intensive global warming during 1981–1997 stimulated increase TCI

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upward trend in South America. Following the increasing TCI trend, especially during 1981–2018, when an area with hotter vegetation surface doubled for all three vegetation and malaria intensities: for moderate vegetation stress (VS) and moderate LM (RD = 124%), severe VS and light LM (RD = 159%) and extreme VS and very light LM (RD = 183%, Table 8.2). This indicates that based on temperature conditions analysis there is a very strong tendency towards malaria reduction (reduced areas and intensity) and potential tendency for decreasing the number of people with the disease in South America. Moisture index (VCI) trends for LM indicates small reduction of areas with moisture conditions (RD = −17 to −33%). Thus, reduction of moisture for all three intensities (VCI) made additional contributions to thermal condition to reduce LM malaria intensity and area in South America.

9.20  V  CI & TCI Trends in the Most Malaria Affected Countries The 39-year VCI and TCI trends were estimated as an indicator of conditions in extreme malaria cases (LM and HM). Six the most malaria –affected countries were analyzed: Brazil, Colombia (South America), Nigeria, Tanzania (Africa), India, Pakistan (Southeast Asia). The investigation indicated that for LM, thermal trends are increasing in all countries, gradually deteriorating vector activities due to thermal drought intensification and expansion. Moisture trend are decreasing intensifying negative impacts of thermal conditions. HM conditions are opposite of LM.  Moisture trends for HM are increasing while thermal trends are decreasing slightly. This indicates that environmental conditions for mosquitoes’ activity are improving over time for HM. Besides impact assessment on vector, it is important to emphasize association between thermal (TCI) and global TA trends’ increase for LM and disassociation for HM. This indicate that global warming impacts differently on the same event, considering extreme values (LM and HM).

9.21  What to Expect in the Future? Malaria Prediction  In spite of intensive global and countries activity and some success in fighting with malaria, its current trends remained relatively stable due to economic, social political conditions. Specifically, the problems are due to limited funding, mosquitoes’ resistance to treatment, drug limitation, increasing population, climate change, weather disasters etc. Therefore, an advanced prediction of malaria area and intensity is an important component of fighting with malaria. It was shown that Malaria has advanced (1–2 months) prediction from models developed from more than 14-year malaria and satellite-based data. VH data from 1981 to 2019 (and soon 2020) are available now from WEB site (https://www.star.nesdis.

9.21  What to Expect in the Future?

275

noaa.gov/smcd/emb/vci/VH/vh_browse.php). Therefore, it is important to get more than 14-year malaria data (the number of malaria cases) in any region, requiring to have prediction model. In the years of ENSO, an advanced prediction might be increased to 3–5 months. Climate Warming  1. Current climate is warming. 2. There are arguments if it is warming from CO2 or from O3 or from other sources (solar activity etc.). 3. In the past two decades, climate models have not predicted right future climate, 4. Since during 2015–2018, global TA has doubled compared to the previous 17-year’ TA trend, and from 2017 the TA was going down, it is hard to predict further climate behavior. 4. What is possible to do is to develop long-term trends from some events such as malaria, crop yield, stressed/healthy vegetation, droughts etc. and also assess their future potential performance and if their trends match with climate trend. VH Trend to Assess Malaria  Vegetation in tropical and subtropical areas is a home of mosquitoes dwelling. Vector does not like stressed vegetation (mostly from drought). Vector is reproducing quickly and become active in spreading malaria if vegetation is healthy being green and warm. We produced 38-year satellite-based greenness (G) and temperature (T) time series and estimated trends, which showed very slight upward trend. Such G and T trends would continue to increase in the next few years, creating generally favorable conditions for mosquitoes. We are in dough that strong increase in global TA produce such negligible impacts on G and T. Moisture and Thermal Trends for Extreme Malaria Conditions  Since the conditions for vector became generally favorable with slight increase in greenness and temperature, we investigated trends for extreme malaria conditions, unfavorable (in case of drought), when the number of malaria cases is reduced and favorable, when the number of malaria cases is increasing. These two types were named low malaria (LM), assessing extreme reduction of cases and high malaria (HM) assessing increasing malaria cases. The investigation covered malaria endemic area of six countries: Brazil and Colombia from South America, Nigeria and Tanzania from Africa, India and Pakistan form Southeast Asia The trends were assessed for the three periods of global warming (1981–1997 intensive TA increase, 1998–2014 flat TA trend “hiatus” time and the entire 39-year (1981–2019)).In case of LM, all countries showed, thermal (TCI) trends increased and moisture (VCI) trends slightly decreased during all three periods. This indicates that conditions for vector are deteriorated due to drought intensification. For HM, TCI trends are declining slightly, while VCI trends are increasing strongly. Following these tendencies conditions for vector become more favorable with potential for increasing the number of malaria cases. Future  What to expect in the future? Historical data showed that in spite of continuing funding, health care improvement and many successful measures to fight with parasite and vector, malaria will remain a very important problem affecting large number of people. Therefore, in addition to these measures, prediction of

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malaria start, development, intensity, area coverage etc. from satellite-based Vegetation Health data will provide 1–2  months (in case of ENSO, 3–5  months) advance predictions for planning the measures of fight with malaria. Weekly VH data are currently available for every 4 km2 of land surface from 1981 through 2019 and will be available for the fur future. If annual malaria cases are available for the period >14-year, malaria-VH models might be developed very quickly. Global climate warming produced no impacts on greenness and temperature of the malaria endemic area. But global warming induced temperature increase for LM, intensifying droughts. Today  Currently, we are providing weekly assessments of HM and LM for every 4  km2 of Africa’s endemic area. https://www.star.nesdis.noaa.gov/smcd/emb/vci/ VH/vh_browse.php. Figure 9.1(a) displays Malaria Risk Index (MRI) for Africa from moisture-­ thermal index (VHI), indicating that the entire sub-Sahara Africa (between latitudes 15° and 0° north) has perfect conditions (HM) for enhanced vector activity in spreading malaria. It can be expected larger than normal number of people with malaria in that area., especially, in eastern Ethiopia, central and southern Sudan, central Chad, southern Nigeria, Niger, Ivory Coast, Ghana, Togo, Benin and southern Upper Volta. South of 14° south, most of African countries are characterized by LM, when conditions are not favorable for vector activity. Figure 9.1(b) assess separately moisture (VCI) and thermal (TCI) conditions for LM and HM. They indicate that HM was stimulated by moisture conditions (VCI), which are the strongest in the discussed sub-Sahara. However, these moisture conditions will be offset from thermal conditions. Therefore, vector activity is expected to be not extremely healthy in the western part. An extremely healthy conditions will be in the eastern part of that territory because both thermal and moisture conditions are extremely favorable for mosquitoes’ activity. South of 14° south, LM is developed because both moisture (VCI) and thermal (TCI) conditions are not favorable for vector to spread malaria. Therefore, the number of people with the disease will be smaller. We are currently developing MRI, assessing HM and LM for every 4 km2 of malaria endemic area of South America and Southeast Asia. It will be delivered to https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vh_browse.php in 2020.

9.21 What to Expect in the Future?

277

Fig. 9.1  Malaria risk area and intensity in Africa, December 9, 2019 (a) from moisture-thermal index (VHI), (b) moisture index (VCI) and thermal index (TCI)

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Index

A Advanced Technology Microwave Sounder (ATMS), 89 Advanced Very High Resolution Radiometer (AVHRR), 8, 65, 107, 146, 147, 156, 164, 165, 177, 222 channels, 65 infrared measurements, 67 sensor, 66 vegetation monitoring, 67 Agricultural fields, 106 Annual malaria anomaly, 137 Anopheles (An) mosquitoes An. gambiae, 20 aquatic habitat, 21 eggs in water, 21 infective bite, 21 malaria vectors, 20 P parasites, 44 warm and moist ecosystems, 45 Anopheles arabiensis, 44 Anopheles balabacensis, 4 Anopheles culicifacies, 4 Anopheles fluviatilis, 4 Anopheles gambiae, 22 Antimalarial drugs, 5 Atlantic Multidecadal Oscillation (AMO), 225 B Bangladesh large area, 145–147 malaria endemic regions, 143 mid-size area, 148–152 remote sensing technology, 145

small area, 152–163 Biomass, 106 “Blob”, 234 Breeding habitats, 44 Brightness temperature (BT), 71 and NDVI, 74–77, 79, 81, 83 restoration, 78 VH technology, 71 volcano-based aerosol, 83 C Chlorofluorocarbon gases (CFC), 231, 232, 234, 235 Climate, 22, 25 characteristics, in South America, 49 factors, 46 parameters, 47 tropical and sub-tropical ecosystems, 45 vegetation, 105 warm temperature, 45 wet, warm and humid, 47 Climate-based malaria distribution, 47 Climate-ecosystem, 106 Climate signal, 105 Climate warming, 35 agriculture and food security, 220 bats food supply, 222 climate experts and media, 220 drought intensification and expansion, 222 economic and social concerns, 221 food security, 220 global temperature anomaly, 220 ice in the North Polar, 222 ice melting, 220 vector-borne diseases, 221

© Springer Nature Switzerland AG 2020 F. Kogan, Remote Sensing for Malaria, Springer Remote Sensing/ Photogrammetry, https://doi.org/10.1007/978-3-030-46020-4

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Index

282 Climate warming (cont.) VH technology, 221 water scarcity, 222 Climate weather, 7, 8 Climatology, 114 Clouds and Earth’s Radiant Energy System (CERES), 89 CO2 emitters, 224 Costs, 17 D Dichloro-diphenyl-trichloroethane chemical compound (DDT), 29, 31, 34 Direct costs, 17 Drought, 220, 221, 228, 240, 246–249, 251–257 E Earth climate, 34 Earth’s warming, 230, 232 Ecology, 22 Economic activity, 18 Ecosystem, 46 characteristics, in South America, 49 classes, 105 maximum annual precipitation, 47 productivity, 105 sub-Sahara Africa, 45 tropical and sub-tropical type, 45 tropical-dry, 45 warm and moist, 45 Elimination, 31 El Niño-Southern Oscillation (ENSO), 230, 234 climate event, 269 cycles impacts malaria, 192 functioning of ecosystems, 191 global weather pattern, 196–199 indices, 193–203 principles, 192–193 VH, 200–207 VH-SSTa (see VH-SSTa teleconnection) weather-malaria relationship, 199–200 Entomologic inoculation rate (EIR), 20 Environment, 3 Bangladesh, 47, 49 impacts on malaria’s features, 44, 45 satellites, 54 Environmental burden, 25 Environmental conditions, 44 Environmental factors, 25 Environmental laws, 109 Environmental satellite data, 54

Eradication, 31–32 Evergreen broadleaf forest, 105 F Food supply and demands, 7 G Geostationary operational environmental satellites (GOES), 64, 82 Global climate warming, 276 Global malaria, 23 Global temperature anomaly, 220 CO2 emission, 224 and CO2 trends, 225–229 measurements, 222 Global warming AMO mechanism, 225 availability of coal, 224 causes, 230 cement production, 223 CO2 emission, 224 coal consumption, 224 during 2015-2018, 233, 234 fuel burning, 223 GHG, 223, 230 global temperature trend, 230 Kyoto Protocol, 223 land cover changes, 235 large-scale atmospheric circulation pattern, 225 little ice age, 224 solar activity, 225 Greenhouse gases (GHG), 223, 225, 230 Greenhouse warming, 230 H Healthy vegetation, 108 Hemispheric temperature anomaly, 226 High malaria (HM), 240, 272 affected countries, vegetation conditions trends Brazil and Colombia, 247, 249, 250 India and Pakistan, 253–255 Nigeria and Tanzania, 251–253 VCI and TCI vegetation conditions Africa’s, 243, 245 South America’s, 242, 243, 245 Southeast Asia’s, 246 VHI, global malaria endemic area, 241, 242

Index Humans Anopheles mosquitoes, 21 malaria cause, 18–19 cycle and symptoms, 21–22 distribution, 22–24 Plasmodium (P), 20–21 I Indices biophysical climatology, 67 NDVI and BT, 78, 94 Northern Hemisphere seasonal cycle, 74 vegetation monitoring, 82 VH, 90, 93 Indoor residual spraying (IRS), 29 Indoor spraying, 31 Insecticide treated nets (ITNs), 5, 29 IR-based brightness temperature (BT), 141 J Joint Polar Satellite System (JPSS), 8 K Kyoto Protocol, 223 L Land cover changes Africa, 238 greenness and temperature trends, 236 in malaria-endemic area, 235 South America (SA), 237 Southeast Asia, 239 surface temperature, 236, 237 vegetation greenness, 236, 237 Landsat data, 64 Landsat satellites, 64 Larvae, 50 Larval mosquito control, 30 Law-of-minimum (LOM), 109 Law-of-tolerance (LOT), 109 Liebig’s LOM, 109 Long-lasting insecticide-treated nets (LLINs), 29 Low malaria (LM), 240, 272 affected countries, vegetation conditions trends Brazil and Colombia, 247–250 India and Pakistan, 253–255

283 Nigeria and Tanzania, 251, 252 VCI and TCI vegetation conditions Africa’s, 243, 244 South America’s, 242 Southeast Asia’s, 246, 247 VHI, global malaria endemic area, 241, 242 M Malaria burden, 3–5, 7, 25–28 cases, 16 cause, 18, 19 challenges, 33, 34, 267 children and infants, 17 climate, 267, 268, 270, 273–275 control, 4, 5 costs, 4, 17 curable and preventable, 5 deaths, 5, 7, 16 endemic, 2 environmental impacts, 47 epidemics, 24 funding, 16 global burden, 3 global warming, 270 mortality, 4, 17 parasites, 7 P.f. and P.v, 4 prevention, 5, 19, 28, 156 progression, 6 reduction, 31 risk, 3, 16 socioeconomic challenges, 5 transmission, 24, 29 unfavorable weather, 3 vaccine treatment, 30 VCI and TCI, 273–274, 276 vegetation greenness and temperature, 272 weather, 35, 267, 269, 274 Malaria-fighting fund, 5 Malaria Risk Index (MRI), 182, 184 Malaria-VH models Africa, 178–185 South America, 185–186 Southeast Asia Bangladesh (see Bangladesh) Orissa state, India, 173–177 South Korea, 177 Tripura state, India, 163–173 Mass drug administration (MDA), 30 Medical care, 19

Index

284 Meridional heat transport (MHT), 225 Moisture (VCI) and thermal (TCI) indices, 146, 147, 164–166, 171, 174–177, 179–183, 186 Moisture-based NDVI, 136 Moisture-thermal vegetation health, 273 Mosquito breeding, 24 Mosquitoes Anopheles, 20, 21 bites, 19, 31 breeding habitats, 44 climate factors, 46 DDT, 29 EIR, 20 elimination, 28 hatching period, 21 infected adult female, 44 larvae, 50 larval mosquito control, 30 longevity, 20 physiological stages, 51 Plasmodium (P), 18, 20 rainfall and surface water, 50 RBM, 28 requirements, 24 resistance to insecticides, 34 search, 50 vectors, 44, 47 Mosquito vector, 7 N National malaria control programs, 24 Near infrared (NIR) application, 67 AVHRR sensors, 81 bands, 68, 69 and BT, 94 and IR channels, 81 SRF, 80, 93 vegetation density, 72 and VIS channels, 68 Neurologic defects, 22 NOAA/AVHRR operational satellite data, 79 NOAA operational polar-orbiting satellites, 54, 64 Noise problem, 105 Noise sources, 73 Normalized Difference Vegetation Index (NDVI), 141 AVHRR sensors, 81 BI, 71 and BT, 74–76, 81

and chlorophyll content, 71 chlorophyll monitoring, 72 description, 71 EDF normalization, 83, 84 EDF technique, 83 Equatorial Africa’s, 78 latitudinal, 83, 84 noise reduction, 75 property, 72 restoration, 78 SEVIRI’s analysis, 79 SMN and SMT, 87 South America’s and Africa’s, 85 values, 72 vegetative ecosystems, 72 water near the shore, 71 weekly composite technique, 73 O Oceanic-atmospheric climate phenomenon, 269 Oceanic-atmospheric cumulative climate phenomenon, 192, 214 Operational environmental satellite data, 7–8 Operational satellites, 8, 64 Ozone (O3) and chlorine accumulation, 232 and CO2 trends, 232 depletion, 231, 232 description, 231 P Parasites, 7 An mosquitoes, 20, 45 antimalarial medicines, 34 malaria prevention, 30 and mosquito development, 47 person’s blood, 18 Plasmodium falciparum, 22 Plasmodium parasite, 18 P. vivax and P. ovale, 21 sporogonic cycle, 52 transmission, 21 and vector, 44 Paris Agreement, 224 Partial immunity, 31 PCR approach, 170, 172 Physiological timing, 44 Plasmodium (P) P. falciparum (P.f.), 4, 20–22, 30, 32, 163 P. knowlesi (P.k.), 20

Index P. malariae (P.m.), 20–22 P. ovale (P.o.), 20, 21 P. vivax (P.v.), 4, 20–22, 26 Polar-orbiting environmental satellites (POES) AVHRR, 65 (see also Advanced Very High Resolution Radiometer (AVHRR)) daily environmental measurements, 64 instruments, 65 malaria monitoring, 64 north-south sun synchronous orbit, 64 TIROS-1, 65 Population treatments, 30 Poverty, 17 Principal component analysis (PCA), 164 Principle of Carrying Capacity (PCC), 109, 110

285 short-term trends, 227 Temperature condition index (TCI), 141, 268 and crop yield, 124 environmental drought-type combination, 116 malaria-affected countries, vegetaton condition trends Brazil and Colombia, 247, 249, 250 India and Pakistan, 253, 254 Nigeria and Tanzania, 251, 252 moisture-thermal stress, 116 vegetation conditions, malaria endemic area Africa, 243, 245 South America, 242, 243 Southeast Asia, 246, 248 vegetation health assessments, 120 Thermal stress, 117

R Roll back malaria (RBM), 28 S Satellite-based NDVI, 105 Satellite data, 105, 223 Satellite systems, 63 Sea surface temperature anomaly (SSTa), 194, 195, 201, 202, 214 Shefield’s LOT, 109 Smoothed BT (SMT) VH algorithm, 110–113, 115, 116 Smoothed NDVI (SMN) and SMT, 109 VH algorithm, 110–113, 115, 116 Solar radiation, 70 Southern Oscillation Index (SOI), 195 Spectral response function (SRF), 80 Spraying insecticides, 5 Stressed and healthy vegetation, 241, 254, 255 Suomi National Polar-Orbiting Operational Environmental Satellite System Preparatory Project (Suomi-NPP/ S-NPP), 65 Surface temperature, 234, 236, 239, 255 Synthetic metal-based complexes, 31 T Temperature anomaly (TA) CO2 continuously increasing trend, 229 and CO2 dynamics, 226 global warming, 223

U Ultraviolet type B (UV-B) radiation, 231, 232, 235 Urban environments, 30 US Drought Monitor (USDM), 123, 124 V Vector control, 28–32 Vegetation, 104 climate, 105 environmental systems, 106 land and biosphere, 106 method, described, 107 weather, 105 Vegetation changes, 106 Vegetation condition index (VCI), 268 environmental drought-type combination, 116 malaria-affected countries, vegetaton condition trends Brazil and Colombia, 247, 249, 250 India and Pakistan, 253, 254 Nigeria and Tanzania, 251, 252 vegetation conditions, malaria endemic area Africa, 243, 245 South America, 242, 243 Southeast Asia, 246, 248 Vegetation emission, 108 Vegetation greenness, 67, 235–237, 239, 240, 255, 256

286 Vegetation growth, 109 Vegetation health (VH) advantages, 67 agricultural fields, 106 assessments, 8, 116 AVHRR-based VH data set, 67 climate, 105 data and products, 130 ecosystem classes, 105 global and regional monitoring, 55 history, 107 humans, 106 indices, 55, 90, 93, 140–142, 192, 201, 213 malaria multi-year time series, 138–140 methodology, 8 model (see Malaria-VH models) moisture-thermal indices, 107, 115, 117 NDVI, 108 numerical method, 107 operational satellites, 129 precipitation and air temperature, 107 primary data processing, 68 principle and data, 55 system algorithm, 67 TCI (see Temperature condition index (TCI)) 37-year VH data, 109, 111–113, 115 USDM, 123 validation, 121, 122 VCI (see Vegetation condition index (VCI)) VHI (see Vegetation health index (VHI)) VIIRS and AVHRR, 108 weather, 105, 135 yield anomaly (dY) deviation, 125–127 Vegetation health index (VHI), 205, 214, 268 and crop yield, 124 drought area and intensity, 123 drought-related vegetation stress, 119 for global malaria endemic area, 241, 242 moisture-thermal joint vegetation stress, 118

Index and soil moisture deficit, 122 spring wheat yield anomaly, 128 Ukraine’s winter wheat yield, 124 VCI and TCI, 115 winter wheat area, Ukraine, 118 VH-SSTa teleconnection, 207–213 Visible (VIS) band, 69 Landsat-1 satellite’s VIS, 68 NDVI, 71, 72 and NIR channels, 68 radiometric calibration, 68 reflectance, 69 SRF, 80, 93 wheat and bare soil reflectance, 70 Visible Infrared Imaging Radiometer Suite (VIIRS), 65 for vegetation monitoring, 89–91 W Warm temperatures, 45, 52 Water, 50 Weather, 34, 46 ecosystem-climate-weather, 18 Korhogo, 51 local weather conditions, 22 parameters, 49, 104 rainfall, 49, 50, 52 sensitivity, pre-adult and adult, 50, 51 short-term component, vegetation, 105 short-term conditions, 25 stations, 53, 54 surface water, 50 temperature, 50 warm and wet surfaces, 49 water, 50 weather-malaria interaction, 50 Weather extremes, 35 Weather parameters, 7 Weather stations, 7, 36, 53