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Malaria remains a major public health challenge in Nigeria, particularly in Rivers State, where complex hydro-ecological conditions, rapid urbanization, and inadequate environmental management reinforce persistent transmission. This study applies Geographical Information Systems (GIS) and Remote Sensing (RS) techniques to assess malaria susceptibility across the 23 Local Government Areas (LGAs) of Rivers State using a multi-criteria decision analysis (MCDA) framework. Environmental and climatic parameters—including elevation, slope, rainfall, soil type, land use/land cover (LULC), vegetation indices (NDVI), flow direction and accumulation, proximity to water bodies, and concentration of waste dumpsites—were integrated to model spatial patterns of mosquito breeding suitability and malaria risk.Satellite imagery (Landsat, Sentinel-2, CHIRPS rainfall data, and SRTM elevation data) and ancillary datasets were processed to generate thematic layers for weighted overlay analysis. Results reveal that low-lying areas with poorly drained soils, wetlands, high rainfall, dense vegetation, and high dumpsite concentration exhibit the highest malaria susceptibility. The spatial risk classification shows that six LGAs—Port Harcourt, Obio/Akpor, Eleme, Oyigbo, Tai, and Ogu/Bolo—fall within the high-vulnerability zone. Twelve LGAs exhibit moderate vulnerability, while five LGAs located in higher or better-drained terrains show low susceptibility. The study highlights the strong influence of hydrological and anthropogenic factors on malaria transmission dynamics. The resulting susceptibility and vulnerability maps provide essential tools for targeted vector control, improved drainage planning, environmental sanitation, and resource allocation. By integrating health records with environmental datasets, the study offers a robust spatial decision-support framework for malaria control and public health planning in Rivers State.
Young et al. (Sun,) studied this question.
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