Modeling the Effect of Meteorological Parameters on Malaria Epidemiology: Identification of an Optimal Model for Application

Abstract Background: This study aims to investigate the relationship between meteorological parameters and malaria epidemiology to identify an optimal model for predicting and understanding the spread of malaria in Rivers State of Nigeria. Malaria remains a significant public health concern, particularly in tropical and subtropical regions, where climatic factors play a crucial role in its transmission dynamics. By analyzing historical malaria and meteorological data from Rivers State, we developed a comprehensive modeling framework to quantify the impact of meteorological parameters on malaria incidence. Method: Five statistical models for count data were employed to identify the most influential meteorological variables and establish their associations with malaria transmission. Results: The results obtained show that, the best count data model out of the five models considered in this study is the Quasi-Poisson Regression Model because it resulted to smaller Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The Quasi-Poisson Regression Model showed that none of the meteorological variables used in the models were significant at 5% level of significance in predicting the number of cases of malaria in the study location. 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 biological, social and economic determinants 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.