Environmental Modelling of Malaria Risk Areas in The Local Governments of Sakété and Ifangni, in Southeastern Benin

Abstract

Malaria remains a major public health challenge in Benin, with transmission strongly influenced by local environmental and climatic conditions. Identifying areas at high risk is essential for guiding targeted interventions. This study aimed to model environmental determinants of malaria risk in the local government areas of Sakété and Ifangni in southeastern Benin. Climatic data (temperature and relative humidity) and remote sensing-derived environmental indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Pond Index (NDPI), were analyzed to assess their association with malaria morbidity. A linear mixed-effects model was used to quantify the contributions of these variables to spatial variations in malaria prevalence. Risk maps were generated by integrating climatic and environmental predictors. Malaria morbidity exhibited a heterogeneous spatial distribution, with higher incidence observed in areas combining favorable climatic conditions and suitable environmental habitats for Anopheles mosquitoes. Temperature and relative humidity were identified as primary climatic determinants, influencing vector survival and parasite development. NDVI and NDPI were positively associated with malaria risk, indicating the importance of vegetation cover and aquatic habitats as breeding sites, while NDWI highlighted the role of soil surface moisture in sustaining larval habitats. The resulting risk map effectively delineated high-risk zones, providing a spatially explicit tool for targeted malaria control.
Environmental modelling combining climatic and remote sensing variables provides valuable insights into spatial patterns of malaria risk. The generated maps can support evidence-based targeting of vector control and public health interventions in southeastern Benin.