Optimizing Seasonal Malaria Chemoprevention (SMC) Through Climate-Responsive and Spatial-Temporal Modeling in Katsina State, Nigeria

Abstract

Malaria remains a significant public health challenge in Nigeria, which bears approximately 27% of global malaria cases and 31% of global malaria deaths. In 2023, Nigeria reported nearly 200,000 malaria-related deaths, with children under five and pregnant women being the most affected groups. Despite the implementation of SMC program, malaria transmission persists, necessitating a data-driven approach to optimize intervention strategies. This study integrates climate data, mathematical modeling, and geospatial analysis to enhance the effectiveness of SMC in Katsina State, Nigeria. Utilizing malaria incidence data from 2018 to 2023 and corresponding rainfall records, Negative binomial regression was employed to assess the relationship between rainfall patterns and malaria incidence. A modified SIR-Sc transmission model evaluated the impact of varying SMC coverage levels (60%, 70%, 80%, and 90%) on malaria burden. Additionally, GIS-based spatial analysis and hotspot detection to identify high-burden LGAs requiring intensified interventions. Findings revealed a statistically significant but modest correlation (r = 0.21, p < 0.00001) between rainfall and malaria incidence, indicating that while rainfall influences transmission, other factors such as intervention coverage and adherence are critical. The negative binomial regression (β = 0.00145, p < 0.00001) further supported this association. However, linear regression analysis (β = -0.0075, p = 0.94) indicated that increasing SMC coverage alone does not significantly reduce malaria incidence. Spatial analysis revealed geographic disparities in malaria burden across LGAs, highlighting the need for targeted intervention strategies rather than a uniform SMC distribution. These findings have profound policy and operational implications. To maximize impact, SMC deployment must incorporate real-time climate surveillance into early malaria warning systems for predictive, climate-adaptive intervention planning. Strengthening adherence monitoring and optimizing intervention timing are crucial for ensuring effectiveness. Policymakers should adopt GIS-based risk stratification to prioritize high-burden LGAs for targeted interventions, ensuring that resources are strategically allocated where they are needed most.

Keywords: Malaria, Seasonal Malaria Chemoprevention, Rainfall