Seasonality Prediction of Meningitis Using Artificial Neural Network (ANN)

Abstract

Communities are concerned about controlling, preventing, and handling infectious diseases due to recent epidemic outbreaks. Meningitis, an inflammation of the membranes surrounding the brain and spinal cord, is a significant risk in Nigeria. It can cause death within hours of infection, with an average case fatality rate of 10%. To prevent meningitis outbreaks, this paper focuses on using an Artificial Neural Network (ANN) to predict outbreaks based on climatological factors. Previous research has shown that climate plays a major role in these outbreaks. The study found that the Levenberg-Marquaralt ANN algorithm was the best model, with the lowest prediction error and fewer iterations. High temperature and low humidity were identified as major triggers for meningitis outbreaks. It is crucial to address these factors to prevent future outbreaks and protect communities.