Malaria Risk Modelling Based on Household and Environmental Mosquito-Breeding Points: Application to Makueni County, Kenya

James M. Malusha

James M. Malusha Dr., Kenya Methodist University, Kenya

Keywords: Malaria, Modelling, Mosquitoes, GIS, Kenya

Abstract

A sociocultural–spatial modelling approach was applied to model household-level malaria risk in Makueni County, Kenya. Using household surveys (N = 80 households sampled across affected and unaffected areas), larval habitat mapping and sociobehavioural data on vector control, we screened candidate predictors, ran Pearson correlations, and developed a stepwise multiple regression model to predict malaria incidence (household-level). We then produced a spatial risk surface using inverse distance weighting (IDW) in a GIS to identify very-low to very-high risk zones based on the combined contribution of the most important predictors. Key predictors retained in the final model were: proximity to surface water/irrigation, presence of puddles/animal hoof-prints near the house, frequency of open water storage, house eave status (open vs closed), use of insecticide-treated nets (ITNs), indoor residual spraying (IRS) history, presence of livestock near house, and solid-waste accumulation. The final model explained a large proportion of the variation in household malaria incidence (Adjusted R² = 0.87) and can guide targeted larval source management and household interventions in Makueni County.

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