Speaker
Description
Meningitis in West Africa exhibits strong seasonal patterns shaped by climatic conditions, yet changing climate patterns, rapid urbanization, and uneven surveillance complicates outbreak predication beyond the traditional meningitis belt. In this study, we developed Ghana’s first spatially explicit, climate-informed epidemiological model of meningitis that integrates case data, carriage and serogroup information, and high-resolution climate drivers. The compartmental process model captures carriage dynamics, climate-driven host susceptibility, and transmission heterogeneity across urban, peri-urban, and rural settings. Forced with observed and projected climate data, the model reproduces past epidemic timing and highlights elevated future risk in northern and transitional zones under warming scenarios. It also distinguishes differing outbreak potentials between densely urban and rural districts. This climate-driven model functions as a prototype early-warning system for Ghanaian health authorities, supporting targeted vaccination and preparedness. It also offers a transferable approach for other West African countries facing climate-sensitive meningitis risks.