Malaria remains one of the most persistent public health challenges in sub-Saharan Africa, responsible for significant morbidity and mortality despite decades of control efforts. In Mozambique, the disease is endemic, with transmission shaped by a complex interplay of environmental, climatic, and socio-economic factors. The southern provinces; Maputo, Gaza, and Inhambane, experience distinct wet and dry seasons that influence mosquito breeding cycles and, ultimately, malaria transmission. Recent studies have demonstrated that climatic variables such as precipitation, temperature, and large-scale ocean–atmosphere oscillations, including the El Niño–Southern Oscillation (ENSO) and Indian Ocean Subtropical Dipole (IOSD), can influence malaria incidence with time-lagged effects. Understanding these relationships at a provincial scale is essential for designing data-driven interventions that anticipate and mitigate outbreaks.This study builds on existing work linking climate variability and malaria transmission by focusing on Maputo Province over the past five years. Using high-resolution climate datasets integrated with malaria incidence data, we explore lagged relationships between climatic drivers and disease trends. While not intended as a formal epidemiological model, this analysis provides actionable insights for operational decision-making, enhancing public health sector's ability to leverage climate information for program planning, early warning systems, and community-level response.
This analysis focuses on Maputo Province over the past five years, integrating malaria incidence data with high-resolution climate datasets to explore lagged relationships between climatic drivers and the disease trends. Results show that precipitation generally exhibits a positive relationship with malaria incidence at shorter time lags, though the strength of this association varies. The IOSD emerged as a key driver, with a one standard deviation increase corresponding to an estimated 0.4 additional malaria cases per 1,000 population, while ENSO showed no consistent signal. Conducted as an independent learning and research effort, this project represents an initial step toward developing a climate-informed malaria early warning systems.
