Temporal and climatic drivers of uncomplicated malaria in Ghana: Regional Generalised Additive and Generalised Additive Mixed Model analysis

Malaria remains a major public health challenge in Ghana despite recent reductions in cases attributable to various interventions, with disease endemicity varying across regions due to diverse seasonal and temporal factors that support mosquito proliferation. This study used Generalised Additive Models and Generalised Additive Mixed Models to explore the nonlinear effects of climatic conditions on uncomplicated malaria in Ghana. Monthly aggregated malaria cases from the District Health Information Management System II and average monthly rainfall and temperature data from the Ghana Meteorological Agency, covering 2012–2023, were analysed. Region-specific models incorporating climate variables were developed, fitted, and validated against observed data using model diagnostics to identify the most suitable model for each region. Using region-specific Negative-Binomial Generalised Additive Models and Generalised Additive Mixed Models fitted with a population offset, the analysis revealed complex temporal patterns in malaria incidence across Ghana, driven primarily by seasonality and long-term trend, which were significant predictors in all ten regions. Regions within the Coastal and Transitional Forest zones exhibited bimodal peak malaria seasons, while the Guinea Savannah showed a unimodal peak. Independent effects of rainfall and temperature were comparatively weak and largely non-significant, but significant interactions between rainfall and temperature emerged in the Ashanti, Eastern, and Central regions, with higher rainfall combined with temperatures around 27–28 °C associated with higher malaria incidence in the Eastern region, reflecting region-specific climatic influences. These findings point to the dynamic and heterogeneous nature of malaria in Ghana, underscoring the need for region-specific control strategies tailored to local climatic conditions. A key recommendation is the systematic integration of meteorological data into the National Malaria Data Repository to enable continuous monitoring and support timely, evidence-based intervention. Future research should incorporate socio-economic factors, intervention coverage, vector surveillance, and demographic characteristics to build a more comprehensive understanding of malaria in Ghana.