Modeling the association of rainfall and temperature with malaria incidence in Adamawa State, Nigeria

Malaria transmission is heavily influenced by environmental factors, particularly rainfall and temperature,which play a significant role in the breeding, survival, and biting behavior of Anopheles mosquitoes, theprimary malaria vectors. This study examines the relationship between rainfall, temperature, and malaria incidencein Adamawa State, Nigeria, over a ten-year period from January 2015 to April 2024. Through time seriesanalysis, it identifies patterns and trends in malaria cases related to environmental conditions. The studyuses the Box-Jenkins autoregressive integrated moving average (ARIMA) forecasting method, comparing modelsusing criteria like the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) todetermine the most accurate forecast model. Diagnostic measures, including RMSE, MAE, and MAPE, wereemployed to select the optimal forecasting model. Results indicated that the SARIMAX(1, 1, 2)(0, 1, 1)[12] model outperformed the SARIMA(1, 0, 1)(2, 1, 0)[12] model, showing the lowest values for AIC, BIC, RMSE,MAPE, and MAE. Malaria cases in Adamawa State were forecasted using the SARIMAX(1, 1, 2)(0, 1, 1)[12] model for the period from May 2024 to December 2025, with a minimum forecasted case count of 40,295.98 in May 2024 and a maximum of 78,600.28 in October 2025. Based on these forecasts, it is crucial for the Adamawa State government to strengthen malaria vaccination policies to effectively address this public healthissue.