Abstract

Malaria is endemic problem in the low and middle income countries, especially, sub-Saharan Africa, is caused by Plasmodium falciparum contributed on the major parts, and Plasmodium vivax parasites in the minor parts claim for millions of morbidity and mortality on the global level. Mainly due to the climate change, monsoon failure, declining agriculture crop production, population movements on poverty, mushroom growth of unplanned urbanization, landscape and land cover changes. Multispectral (MSS) satellite data and Synthetic Aperture Radar (SAR) imagery has been used for the replacement of conventional survey methods for the assessment of the problems. Remote sensing of environmental information has been used to study the variations of climate conditions; land use/ land cover changes and its impact on natural environmental transitions, assess breeding potentiality, and forecast malaria for the past 4 decades. It provides the reliable, picturesque, repetitive, precise, speed, and low cost comparatively. Remote sensing technology has been applied as alternative tool, a scientific method to develop spatial models for forecast malaria for lager areas; regional, national, and global scale. Malaria is prolonged public health challenging problem in Africa continent, tropical countries, and sub-tropical regions for several decades, it claims 2 million death tolls every year, especially, in the sub-Saharan Africa regions excessively tremendous problem, despite, all kinds control measures. The perceptions of spatial model for malaria prediction/ forecast malaria epidemics have been attracted by many researchers for past 4 decades. Therefore, present study is aimed to review relevant studies of the use of multispectral satellite data, and synthetic aperture radar imagery to analyze recurrent weather environment (temperature, precipitation, relative humidity, and saturation deficiency), land surface temperature (LST), sea surface temperature (SST), vector breeding potentiality, deforestation; land use/ land cover changes for forecast malaria.

1. Introduction

Tropical and sub-tropical world has rigorously been affected by the vector borne infectious diseases throughout the historical records ¹, particularly, malaria epidemics on the consequences of natural and manmade environmental transition, land use / land cover changes, and recurrent weather condition ^{2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}. Malaria epidemic has been declined throughout the tropical regions in Sub-Saharan Africa, South America, South-East Asia including India for several decades ^{1, 17}, however, malaria is major problem in Sub-Saharan Africa nations, and other third world countries ¹, despite, use of mosquito bed nets, and other prevention measures added much awareness among the communities ¹. The purpose of this study to reveal the major determinants of regular malaria outbreaks or endemic situation in the regions including the agricultural land use, a permanent / intermittent of water logging land covers, seasonal migrations/movements to both rural and urban for livelihood. Present review works, mainly focus on forecast malaria early in advance and management of the epidemic situation in the endemic regions for sustainable health, particularly, in the third world Africa nations, and other tropical countries. Both endemic and epidemics spread into newer areas determined by the complex of phenomenon including changes in environmental features, socio-economic vulnerability, climate determinants, physiographic landscapes, etc., however, the environmental transitions caused by land use/land cover changes3,4, and weather conditions ^{5, 6, 7} are playing important role in the changing scenario of endemic and epidemic diffusion. Multispectral (MSS) satellite electromagnetic spectrum data with different ranges of spatial and temporal resolutions pertaining to land use/land cover changes, determine the land resources in the regions, and synthetic aperture radar (SAR) imagery provides the weather data ^{10, 11, 12, 13, 14, 15, 16}. Remote sensing and GIS has been used to survey, determine vector breeding potentiality, vector surveillance, and forecast vector borne disease epidemics for the past 4 decades ^{9, 10, 11, 12, 13, 14, 15, 16}. The objective of present study, review the available source of remote sensing data and its application to public, health medical entomology, with keen interest in malaria control and management. Satellite data could provide the information on a range of spectrums, band width, efficacy of onboard sensors of the available land resource satellites, and meteorological satellites, which has facilitate to study land and water resources, and to calculate accurate atmospheric weather condition ^{9, 10, 11, 12, 13, 14, 15, 16}.

Earth orbiting land resource satellites imagery for mapping environmental variables: Vectors prevalence is definitely controlled by the environmental variables, viz., landscape topography, altitude, slope, soil types, soil moisture, vegetation, lakes, rivers, canals, streams, ponds, pools, dams, agriculture practice, domestic animals, human populations, settlement types and patterns, etc., ¹⁸ Malaria is classified into four types based on the environmental features and phenomenon, such as; i) Highland malaria, ii) Lowland/plain malaria, iii) Urban malaria, and iv) Coastal malaria. Multispectral satellite data were used to study the environmental variables for the past several decades ^{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18}. The remote sensing data has been used to obtain information relevant to the environmental features for mapping each and every variable accurately, and study the distinctive nature of temporal characteristic and spatial pattern of environmental risk factors dynamic phenomenon. Mostly, the confirmed malaria epidemic cases are registered in the highlands extensively ^{6, 12}, and moderately in the other environment because of the unlikelihood of environmental features and climate variations are unstable, including the Indian sub-continent ^{6, 12}. It doesn’t mean that we are blaming the environmental variables, and climate parameters for the malaria epidemics, but, at the same time, it can be changed the situation radically, and therefore, a time series model could be served the purpose to monitor environmental changes and climate as well. Definitely, large scale environmental transitions, land use/land cover changes, recurrent weather conditions, and manmade developmental activities such as; mega water resource projects, urban development, increasing industries and factories, change in agriculture practice (from dry land agriculture to wetland irrigation agriculture), and floating population movements for occupation are playing important role in the human host-parasite-vector interaction, development of parasites, mosquito vector fecundity, sustain or promote malaria epidemics spatial diffusion, shifting of epidemic transmission, and decline or increase of infections state in the community. The study revealed that developmental activities are altered the natural environment in short duration and has effect on the human-host-vector-pathogen interaction, and the consequences, the epidemiology of mosquito borne malaria epidemic scenarios have been changed accordingly. Generally, the tropical and sub-tropical regions have the suitable environment, climate conditions, shifting of agriculture practice, etc., are fuelling for the vector-parasites thrive ^{3, 6, 9, 16}. Researchers have been choosing the available remote sensing data for the study of malaria epidemic disease risk factors and ranked them to mapping risk prone areas, and to make a continues surveillance in the endemic region.

Data derived from Landsat TM, IRS, Spot, ERS, Envisat (ESA), Quickbird, IKONOS, World View series, Tropical Rainfall Measurement Mission, TRMM), NOAA, COSMO-Skymed, MODIS /TERRA, RISAT, INSAT, Oceansat, MOS, SCATSAT, satellite data has positively been used to analyse vegetation, water bodies, monitor air temperature and humidity criterion ¹⁸. Use of MSS and SAR data have been used for mapping of vector breeding potential environment, to examine the vector ecology, spatial topology between vectors and climate parameters, tropical and subtropical precipitation and the associated environmental studies, spatial prediction of vector borne diseases risk prone areas, and visualize maps to model malaria risk and its spatial-temporal seasonal variation (Table 1). The malaria risk index rule based maps were used in map overlay analysis to predict spatial stretch of malaria epidemic risk, and to generate cartographic visualize weighted final categorized malaria risk map. The available multispectral earth orbiting satellite data could be served the purpose to study environmental variability. These environmental variables could be monitored and changes must be observed regularly using remote sensing satellite data, thus to pool key data in the GIS expert engine to develop a spatial model to predict the malaria epidemics at least 3-4 weeks early in advance.

2. Land Resource Satellites Imagery for Mapping Land Use / Land Cover Changes

More than 3,000 sun synchronous orbit earth resource satellites are currently operating for earth observation operated by 40 countries. Earth resource satellites provide the information on land use/land cover (LULC) changes systematically with specified interval regular basis, linking these changes with malaria mosquito breeding potentials are significant leads to critical approach for both vector control, and disease prevention. Earth Observation satellites and its applications to survey and determine vector breeding potentiality for forecast malaria in association with land use/ land cover changes is fundamental. Huge data sources obtained from different electromagnetic spectrum of various satellites are pursuing the new phase for the development of key elements for assessing the vector potential areas associated with land use/ land cover changes. LULC changes are caused by many factors including the man-made and natural, these changes are brought huge impact on vector mosquitoes profusion due to the multiplier effects of industrial developmental projects, climate change, rainfall uncertainty, increasing sea level by global warming, urban development and rejuvenation, transport network developments, deforestation, water resource development projects, tourism development, huge population movements from rural to urban for job seeking, etc., At present, 1950 earth resource satellites are operated in sensing the information relevant to surface features of different land use / land cover categories are directly linked with malaria mosquitogenic conditions. Deriving the large amount of data in different temporal and spatial resolution from different multispectral electromagnetic spectrum viz., visible, infrared, and microwave, are found highly significance for linking malaria breeding potentiality and relating disease endemics as well as malaria epidemics spreads.

3. Remote Sensing to Malaria Vector Environment

Malaria outbreaks both longitudinal and vertical magnitude trends have been declined for the past decades, however, the epidemics have been increasingly extended in the third world developing and underdeveloped countries, particularly, it has been challenging problem in the sub-Saharan Africa, Middle and South America, South East Asia, and still it is continued in the major part of East, Northeast India for several decades. Malaria epidemics 229 million cases are registered in the world, and it claims 409, 000 deaths in 2019 (WHO, 2020). Sub-Sahara Africa nations alone contributed 93 % of all deaths, and below 5 years old children group have registered 61% of death caused by malaria (WHO, 2020). Multispectral satellite data has been used for mapping the malaria vector breeding habitats. Spatial distribution and seasonal variation of malaria vector fecundity are completely controlled by the climate determinants. Synthetic Aperture Radar (SAR) imagery has been used to develop a spatial model for prediction of malaria epidemics in a particular region with risk of susceptible community, and forecast malaria outbreaks much early in advance precisely.

High resolution multispectral satellite data are readily available to process the past and present circumstances of the vector breeding ecology, using visible spectral data viz., 0.0.45 µm-0.52 µm (Blue), 0.52 µm-0.60 µm (Green), 0.63 µm -0.69 µm (Red), and Infrared imagery (NIR-Near Infrared, MIR-Middle Infrared, TIR-Thermal Infrared) obtained from 0.76 µm -0.90 µm (NIR), 0.90 µm - 1.5 µm (MIR) 1.55 µm -1.75 µm (Infrared), Thermal Infrared bands 10.41 µm -12.5 µm, and 2.08 µm -2.35 µm. Climate variations in the land surface temperature (LST) and sea surface temperatures (SST) are determinants of malaria vectors and disease transmission. Land surface temperature (LST), and Sea surface temperature (SST) have been analyzed using the synthetic Aperture Radar (SAR), Advanced Very High Resolution Radiometer (AVHRR), and Microwave Radiometer (MWR). Microwave remote sensing data range from 1cm to 1m were analyzed to environmental monitoring through obtain the information on soil moisture, soil types, water holding capacity of vegetation land cover categories, evapotranspiration from soil and plants, hydrological information, and wet land water logged areas. Knowledge and key elements obtained from the satellite data was used to delineate, analyze and built spatial model to appreciate spatial relationship between the profusion of Anopheles vector species and malaria outbreaks (Table 1). There has been many researchers included the bio-geo environmental variables and climate determinants in the spatial models to predict the malaria epidemics much earlier in advance at least a month before. Climate determinant variables have been used to develop a forecasting system that can be assisted to delineate the geographical boundary of malaria epidemics early in advance, particularly in the Africa continent, and other parts of tropical nations including India where the malaria is severe endemic disease. Climate model was developed based on weather parameters for forecasting malaria outbreaks few months early in advance by the British researchers at Botswana (Africa), and another study shows that malaria outbreaks in the farming community where directly linked with mosquitogenic condition from hoof prints to large swamps in association with seasonal huge population movement for farming activities, and are reliably determined by recurrent weather determinants at Tanzania (Africa) ⁴, similarly, malaria endemic problem persist in the tropical regions of South East Asia, South America, East and North East India, due to mainly climate factors, and land use/land covers, and wet crops irrigation farming activities, was yield good results accurately, and has statistically significance.

4. Meteorological Satellites Data to Analyze Weather Environment

Meteorological satellites are polar orbiting or geostationary. Seven major groups of unions /organization and/or nations are operating polar orbiting or geostationary satellites for meteorological purposes. Both polar orbiting and geosynchronous satellites are having visible and infrared sensors ^{10, 18}. National Oceanic and Atmospheric Administration (NOAA) series of polar orbiting meteorological satellites, and the Geostationary Operational Environmental Satellite (GOES) series geostationary satellites are operated by the United States America (USA), Metop series satellites operated by the European Union of meteorological satellites (EUMETSAT), Meteor, and RESURS series of satellites operated by Russia, FY-3A, 3B and 3C series by China, and INSAT geostationary satellites owned and operated by India for meteorological purposes (Table 1) ¹⁸. Japan Meteorological Agency (JMA) has the Himawari geostationary satellites, operated for the purpose of weather forecasting, tropical cyclone tracking, and meteorology research ¹⁸. Meteorological agencies of several nations in the East Asia, Southeast Asia, Australia, and New Zealand are using the satellite data for their weather monitoring and forecasting operations (Table 1) ¹⁸. Both polar orbiting satellites and geostationary satellites are operated for monitoring recurrent weather conditions (Table 1). The defence meteorological satellites operated by the United Sates are giving the best results of weather prediction for the North America, South America, and Africa (Atlantic Ocean and Pacific Ocean). Canada uses the GOES system for their meteorological services for weather monitoring, forecasting, tracking storms, and to environmental monitoring of land surface, atmospheric conditions, ocean resource evaluation, cyclone, weather prediction, and climate dynamics. Geostationary satellites are typically used to estimate weather conditions daily; using cloud cover density, cloud height, and aerosol calculations, are obtained and calculated every half hour during the day (Table 1) ¹⁸. Meteorological satellite data are used to appraise even minor variation in the daily weather dynamics, climate pattern, maximum likelihood of seasonal changes, and amount of rainfall, intensity, duration, and thus, malaria mosquito breeding potentiality could be intended ¹⁰.

5. Climate Determinants and Malaria Transmission

In an urban environment, malaria breeding habitats, vector density, and climate were fuelling for malaria outbreaks, the risk prone areas could be delineated precisely using the multispectral electromagnetic spectrum visible and infrared satellite data. Land use/land cover categories are directly connected with malaria epidemic cases in both rural wet irrigation agriculture farming land, and urban environment ^{3, 6, 7}. Among the 400 Anopheles mosquito species, 30 Anopheles vector mosquito species are playing important role as vector in diffusion of malaria¹. Malaria transmission risk has been shifted from epidemic situation to become endemic and vice versa in Africa continent on the consequences of climate change and its impact on vector fecundity, and climate suitability for parasite development ². Most of the malaria epidemic cases were registered in sub-Sahara Africa, including Ethiopia, and the longitudinal trend of malaria cases are irregular pattern of transmission in the pretentious vulnerable community, which is highly determined by the influence of environmental inconsistency, and frequent change of weather determinants ^{12, 14}. As far as the Sub-Sahara Africa concerned, malaria vector Anopheles species including An. gambiae complex (A. arabiensis, A. bwambae, A. melas, A. merus A. gambiae s.s., and A.quadriannulatus) and A. funestusabundance are directly associated with seasonal variations climate factors, viz; temperature, humidity, and amount of rainfall ¹⁴. The information pertain to Land surface temperature (LST), sea surface temperature (SST), normalized differential vegetation indices (NDVI), and evapotranspiration (ET) were derived from the MSS visible, infrared, and SAR microwave remote sensing data. The derived information on LST, SST, NDVI, and ET has been used to analyze the impact of seasonal variation, and environmental variability on spatial and temporal aspects of malaria epidemic pattern in the both endemic and non-endemic regions ¹⁰, and the use of remote sensing for time series analyzes for environmental covariant geo-statistical model had best fit and accurate spatial prediction over the temporal pattern of malaria epidemics ¹². Urban growth, deforestation, agriculture land use changes have been bringing regional micro-climatic changes as well as urban heat island, evapotranspiration (i.e. Evaporation accounts for the movement of water to the air from sources such as the soil, canopy interception), and water bodies, change in atmospheric lower layer troposphere temperature (<15km height from the earth surface) makes important effect on temperature, humidity, and precipitation of weather conditions, effect on human- host-vector-pathogen interaction, and as a result, change in spatial and temporal epidemiology of malaria epidemic transmission, has direct relationship with seasonal variations of the monsoon ^{2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16}, and statistically significance ⁶ with p value <0.001.

6. Remote Sensing and GIS in Spatial Model for Forecasting Malaria

The rationale for persistent of malaria endemic situation is not yet revealed, however, the asymmetrical changing ecology of the prime focus of the vector borne disease epidemics have been attracted keen attention including malaria, as it may extent in the newer localities where it was never reported earlier, might have been influenced by the environmental transitions and ecological changes. The conventional manual processes for identification, survey and mapping of Anopheles vector population involves huge money, time consuming, and involves huge intellectual manpower working days, and hence, it is very difficult to conduct a survey to collect vectors in the environmental reservoir. Machine learning algorithms based mathematical models within the Artificial Intelligence (AI) could be used to mapping the probability of areas at risk of malaria transmission, and the risk level leads to manage and control the intricate situation. The occurrence of malaria vectors is determined by the geo-environmental variables and seasonal changes. Artificial Intelligence (AI) is the computer programme based model that serves needs step by step with rapid and accuracy based on the density of vector host population density and the geo-ecological variables leads to spatial prediction of malaria transmission in the human community much earlier in advance. A mobile based healthcare information management system, using the mobile application and cloud computing together for better sharing, storing, updating, and retrieval of electronic healthcare data, so as to be able to forewarn a community early in advance. Based on the map illustrate the areas vulnerable to the high risk of malaria epidemics, could be recommended for full vaccination in the community along with vector strategy in the settlement areas and followed by a systematic surveillance for the control leads to clutch the profusion of Anopheles vector population in and around the human settlements. Multispectral and Synthetic Aperture Radar microwave remote sensing satellite data have been synchronized in the GIS expert engine to develop a spatial model based on the environmental, climate, and socio-economic risk factors. A big data analysis of malaria epidemic risk factors has been carried out by many researchers, and accomplished that when the value of risk indices exceeds the threshold limit of the determinant variables in a particular place, there could be a chance/highest probability of malaria epidemic outbreaks categorically. Remote Sensing and GIS applications to mapping, monitoring, and spatial modelling for early detection of environmental conditions and change in climate determinant variables have highly been used for prediction of malaria outbreaks ^{19, 20, 21, 22, 23, 24, 25}, and has been used to construct a baseline for choosing appropriate measures for malaria mosquito control, prevention and control of malaria diffusion widely ^{19, 20, 21, 22, 23, 24, 25}. An artificial intelligence spatial model has been applied to a routine vector surveillance and control measures could be varied, initiated depends upon the environmental and weather recurrent conditions.

7. Conclusion

Remote sensing of multispectral and SAR microwave satellite data could provide the information on micro level climate changes in the environment, land use/land cover changes, recurrent weather conditions of atmosphere accurately. Gathering the information on climate parameters are reasonably possible at an affordable price, and some extent available in the public domain free of cost, and hence, the expert team of biologists, entomologist, and remote sensing scientist could make use these data for forecast malaria outbreaks for any of the need based regions in the endemic world. Satellite data under the umbrella of GIS could be assisted to assess the risk of malaria outbreaks in various places including inaccessible remote locality. Based on the predicted information, public health officials could make an arrangement for prevention, control measure, and management activities of the outbreak situation successfully.

References