Modelling Climate Variability and Meningococcal Meningitis in the African Meningitis Belt: A Review of Predictive Systems for Early Warning in Senegal

**Revised Abstract**

Epidemics of meningococcal meningitis (MM) pose a recurrent public health threat across the African meningitis belt, with Senegal experiencing frequent outbreaks. Seasonal hyperendemicity and epidemic surges are strongly influenced by climatic conditions, particularly during the dry season. Predictive modelling, which integrates climate variables with epidemiological data, offers a pathway towards proactive outbreak management through early warning systems (EWS). This systematic review critically evaluates the current state of evidence on climate-driven predictive models for MM in Senegal. Following PRISMA-ScR guidelines, we systematically searched PubMed, Scopus, Web of Science, and relevant grey literature sources up to May 2024. Search strings combined terms for meningitis, climate, prediction, and Senegal. Included studies employed statistical or mechanistic models linking climatic factors—such as absolute humidity, dust, and temperature—to MM incidence. Our synthesis identifies a consensus that low humidity and high atmospheric dust concentrations are critical precursors to epidemic risk. We critically compare modelling methodologies and assess the operational performance of emerging EWS in Senegal, which increasingly utilise satellite-derived climate data. While these integrated systems show potential for district-level outbreak forecasting, significant challenges remain in validation and operational scalability. The findings underscore the necessity of robust, interdisciplinary collaboration between meteorology and public health. Refining and implementing these climate-informed models is imperative for optimising pre-emptive interventions, such as targeted vaccine deployment and resource mobilisation, to reduce MM burden within Senegal's resource-constrained health system.