Exploring the Interplay of Meteorological Variables, Government Policies, and Malaria Epidemiology: Towards an Optimal Predictive Model for Malaria Spread in Rivers State, Nigeria

Abstract

Background: Malaria continues to be a serious public health issue, especially in tropical and subtropical areas where the dynamics of its transmission are greatly influenced by environmental conditions. The objective of this research is to examine the impact of meteorological factors, and governmental policies on malaria epidemiology to determine the most effective model for forecasting and comprehending the transmission of malaria in Rivers State, Nigeria. Method: Three statistical models for count data were compared to identify the most influential meteorological variables and government policy and establish their associations with malaria transmission. The best count data model was compared with a seasonal autoregressive integrated moving average model using some known model evaluation metrics. Results: The results obtained showed that the best count data model out of the two models considered in this study is the Quasi-Poisson Model because it resulted in a smaller Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) value. The SARIMAX Model outperformed the Quasi-Poisson model and showed that season, precipitation and government policies were significant at a 5% level of significance in explaining the variations in the monthly number of malaria cases in Rivers State, Nigeria (Jan. 2007 – Dec. 2021). Further, the SARIMAX (1,1,0)(1,1,1)12 model showed that the current number of malaria cases depends on the past year’s number of cases. Conclusion: The findings of this study highlight the need for a multifaceted approach to malaria control in Rivers State, addressing not only the meteorological factors but also the governance-related determinant of the disease. The identified optimal model serves as a valuable resource for policymakers, researchers, and healthcare practitioners, enabling them to make informed decisions and implement targeted interventions to mitigate the impact of malaria outbreaks.