Malaria remains a significant public health challenge in tropical regions where environmental and governance factors influence transmission dynamics. This study examined how meteorological conditions and shifts in government policy affect malaria incidence in Rivers State from 2007 to 2021. Poisson, quasi-Poisson, and negative binomial models were applied to identify significant environmental predictors, followed by comparison with a SARIMAX model to evaluate forecasting performance. The quasi-Poisson model provided the best fit among count models; however, SARIMAX demonstrated superior predictive accuracy. The SARIMAX(1,1,0)(1,1,1)₁₂ model revealed that monthly malaria incidence is significantly influenced by seasonality, rainfall, and changes in government malaria-control policy periods. These findings highlight the value of integrating climatic and governance indicators into malaria early-warning systems. The optimal model identified here provides a practical tool for strengthening malaria surveillance, planning, and response efforts in Rivers State.
Egbom et al. (Sat,) studied this question.
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