Remote sensing of environmental risk factors for malaria in different geographic contexts

Abstract

Background Despite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies. Methods We used remotely sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by Plasmodium falciparum and Plasmodium vivax from 2014 to 2017 in two geographic settings in Ethiopia. Geospatial datasets were derived from multiple sources and characterized climate, vegetation, land use, topography, and surface water. All data were summarized annually at the sub-district (kebele) level for each of the two study areas. We analyzed the associations between environmental data and malaria cases with Boosted Regression Tree (BRT) models. Results We found considerable spatial variation in malaria occurrence. Spectral indices related to land cover greenness (NDVI) and moisture (NDWI) showed negative associations with malaria, as the highest malaria rates were found in landscapes with low vegetation cover and moisture during the months that follow the rainy season. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria. Settlement structure also played an important role, with different effects in the two study areas. Variables related to surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies, and height above nearest drainage did not have strong influences on malaria. Conclusion We found different relationships between malaria and environmental conditions in two geographically distinctive areas. These results emphasize that studies of malaria-environmental relationships and predictive models of malaria occurrence should be context specific to account for such differences.

Predicting Continental Scale Malaria With Land Surface Water Predictors Based on Malaria Dispersal Mechanisms and High‐Resolution Earth Observation Data

“…When comparing our results to similar studies that utilize exposures in BRT models for predicting malaria occurrence, both at large (Sinka et al, 2010) and small (McMahon et al, 2021;Solano-Villarreal et al, 2019) spatial contexts, we observe partial correspondence. These studies used lower-resolution surface water maps and reported low importance of surface water predictors.…”

“…Both "Classic" scenarios (5, 6) followed a pattern reported in similar studies, where surface water predictors contribute relatively little to model performance (McMahon et al, 2021;Sinka et al, 2010;Solano-Villarreal et al, 2019) et al, 2021), a short distance to river at the observation location may not be representative of the dispersal mechanisms at all possible locations of transmission. In contrast, Average Distance to Water represents the general location of water apropos land and the connectivity of waterbodies throughout the set spatial context, and by using multiple sizes of the spatial context and different persistencies of water this is done for more than one spatial context and waterbody temporality.…”

“…The poor performance at small scales could be explained by the fact that, at small to intermediate scale, proximity to small waterbodies and trunk river alluvial plains or tributary streams is most important for malaria transmission, as these contain oviposition sites (Clarke et al, 2002;Minakawa et al, 2005;Sinka et al, 2010;Smith et al, 2013). However, the lower-resolution JRC map is unable to capture such smaller waterbodies (McMahon et al, 2021;Mushinzimana et al, 2006), whereas the validation of the "2021" maps presented here indicates that our higher resolution maps are able to capture these. In contrast, at large scales, climatically determined surface water patterns are the most important determinants for malaria transmission (Smith et al, 2013).…”

WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases

“…This study [ 34 ] focused on geographical factors affecting malaria and analyzed the other factors that can control malaria transmission. Remote sensing was used to gather data on environmental factors affecting malaria in Ethiopia’s two different geographical locations.…”

Malaria and Helminthic Co-Infection during Pregnancy in Sub-Saharan Africa: A Systematic Review and Meta-Analysis

“…Although there are universal malaria interventions such as bed nets and access to prompt diagnosis and treatment for pregnant women in malaria-endemic settings, universal access to sanitation and hygiene should be implemented to prevent malaria and helminths co-infection in women of reproductive age and schoolchildren in endemic settings [157]. Moreover, improved diagnostic tools are required to better quantify the burden of malariahelminth co-infection, as this might help understand the burden of these infections for evidence-based planning and implementation of integrated control and elimination of both malaria and helminthic infections in co-endemic areas [158]. Future malaria vaccine development efforts might also need to understand the immune modulation in malariahelminth co-infection for better consideration of the effect of the helminth-malaria infection in vaccine immunogenicity [159].…”