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Integrating DHS/MIS Biomarkers with 34 Years of CHIRPS-NDVI Climate Data for Malaria Risk Prediction in Nigeria: A Machine Learning and Spatial Mapping Approach

2025-12-15

Abstract excerpt

<h4>Background</h4> The estimates of national disease risk are considerably limited by the time of conducted surveys and the geographical inadequacies in surveillance, notwithstanding malaria’s continued prominence in morbidity and mortality in Nigeria. There is limited research employing machine learning to integrate long term environmental trends with DHS/MIS biomarker data on a national scale, despite the esta...

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Literature Corpus work: 9f3f0b59-f93b-5a98-ac9a-77555fed38a0
DOI: 10.64898/2025.12.14.25342244

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Integrating DHS/MIS Biomarkers with 34 Years of CHIRPS-NDVI Climate Data for Malaria Risk Prediction in Nigeria: A Machine Learning and Spatial Mapping ApproachDOI 10.64898/2025.12.14.25342244Read

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