Nonlinear and lagged climatic drivers of malaria incidence in sub-Saharan Africa using multi-country panel analysis 2015-2024.

Abstract

Malaria remains a major public health challenge in sub-Saharan Africa (SSA), where climatic variability continues to influence transmission dynamics despite ongoing control efforts. This study investigates the nonlinear and lagged effects of climatic factors on malaria incidence using a balanced monthly panel of 20 SSA countries covering the period 2015-2024 (N = 2400 observations). Monthly malaria incidence per 1000 population is modelled as a function of rainfall, 2-m air temperature, and vegetation greenness, while population density and elevation are included as demographic and topographic controls. Fixed-effects generalised additive models (GAMs) are employed to capture nonlinear exposure-response relationships, complemented by distributed lag nonlinear models (DLNMs) with lags of 0-3 months and a log-linear fixed-effects panel specification. Climatic and environmental variables are obtained from publicly accessible datasets, including CHIRPS, ERA5-Land, MODIS, WorldPop, and NASA SRTM, while malaria incidence data are compiled from national surveillance systems and World Health Organisation repositories. The results reveal significant nonlinear and temporally lagged effects of rainfall and temperature on malaria incidence, with evidence of threshold behaviour across climatic ranges. Vegetation greenness exhibits an increasing-then-saturating association with transmission. Population density is positively associated with malaria incidence, whereas elevation exerts a significant protective effect. The models explain approximately 70-73% of the observed variation in malaria incidence and satisfy key diagnostic requirements. These findings demonstrate the importance of accounting for nonlinear and delayed climatic influences when modelling malaria risk and provide evidence to support climate-informed early warning systems and adaptive malaria control strategies across SSA.