Forecasting Meningitis Outbreak with a Climate-Inspired Model

Abstract

Recently, meningitis outbreaks have posed substantial public health issues across the world, prompting effective preventative and control measures. Therefore, this work proposes a unique method for estimating meningitis incidence by incorporating atmospheric data into a predictive model, christened as climate-based predictive meningitis model (CBPMM). The CBPMM is created using machine learning formalities, with meteorological data serving as a key component of the predictor. The model incorporates powerful prediction techniques that analyze historical data and environmental patterns comprehensively and thus, provide useful insights for early identification and proactive intervention strategies. With infection transmission rate at 0.88, carrier natural recovery rate 0.06, and the efficacy of treatment is 0.001, ; it implies that the infectious disease persists in the community. However, when ; that is, the disease is controllable. The CBPMM marks a huge step forward in meningitis surveillance, providing healthcare authorities with information to promptly limit the effect of outbreaks.