The study employs a time series approach to assess the impact of malaria fever in Osun State. Data for this research was collected from the records of the State of Osun Hospitals Management Board in Ede. Time series models were developed, and a range of tests were applied to the data, with the stationarity test being the most crucial. This test was conducted using graphical methods, correlograms, and unit root tests. The results of the stationarity test showed that the series became stationary after the first difference. Analysis using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) revealed that AIC slightly outperformed BIC in generating the best predictions. Furthermore, performance indices such as RMSE and Theil U inequality proved to be the most reliable metrics. The forecast indicates that malaria is more prevalent during the rainy season, particularly in stagnant water, and is influenced by other factors. The study recommends that the government take action to reduce malaria transmission by providing health education and launching awareness campaigns at various religious centers, including mosques, churches, and shrines, to fight this deadly disease.
Olawale et al. (Tue,) studied this question.
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