Modelling Seasonal Trends Of Malaria Incidence In Nasarawa State, Nigeria Using Health Facility Surveillance Data

Malaria transmission in Nigeria is highly seasonal and climate-sensitive, yet routine surveillance and meteorological datasets remain underutilized for predictive modelling at subnational levels. This study modelled seasonal malaria incidence trends in Nasarawa State, Nigeria using routine surveillance and climatic data. A retrospective ecological time-series study was conducted using monthly confirmed malaria incidence data from all 13 Local Government Areas of Nasarawa State between 2021 and 2025. Rainfall and temperature were examined as the climatic predictors. Seasonal decomposition and cross-correlation analyses were performed to identify the temporal patterns and lag structures. Seasonal Autoregressive Integrated Moving Average (SARIMA) and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models were developed using the Box-Jenkins framework. Model performance was evaluated using the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Malaria incidence showed pronounced seasonal peaks, with the highest transmission occurring during the rainy season. Cross-correlation analysis identified rainfall at a one-month lag and contemporaneous temperature as significant predictors of malaria incidence. The SARIMAX model outperformed the univariate SARIMA model, achieving strong predictive accuracy (MAPE = 8.7%). Forecast projections indicate sustained transmission with a peak incidence expected between June and August 2026. Malaria transmission in Nasarawa follows a predictable seasonal pattern that is influenced by climatic variability. Incorporating rainfall and temperature into SARIMAX models improves the forecasting performance and provides evidence supporting climate-informed malaria surveillance and preparedness in endemic settings.