A Time Series Modeling of the Morbidity Incidence of Pneumocystis Pneumonia among Farmers in Benue State, Nigeria

Pneumocystis pneumonia (PCP) is a severe opportunistic infection that poses significant public health challenges, particularly among immunocompromised individuals, necessitating accurate modeling and forecasting for effective disease control and prevention. This study aims to identify an optimal Autoregressive Integrated Moving Average (ARIMA) model for accurately predicting short-term trends in Pneumocystis pneumonia (PCP) infection cases in Benue State, Nigeria. Monthly time series data on PCP cases from January 2010 to December 2023 were analyzed. The stationarity properties of the data were examined using time series plots and the Augmented Dickey-Fuller (ADF) unit root test, which confirmed that the series is integrated of order one, I(1). Following the Box-Jenkins methodology, an ARIMA (p,d,q) model was applied to the data. The results indicate that the ARIMA (5,1,2) model provided the best fit for modeling and forecasting PCP infection cases. The study identified a six-month infection cycle among the population, characterizing PCP as a chronic and potentially life-threatening condition if not properly managed. The selected ARIMA (5,1,2) model demonstrated dynamic stability and accounted for 76.16% of the variance in the data. It was subsequently used to generate short-term forecasts for 24 months (January 2024-December 2025). The projections reveal a fluctuating yet increasing trend in PCP cases, with an average of 698 infections per month. A forecast reliability test, comparing observed and predicted values, confirmed that the forecasted results were valid, accurate, and suitable for informing policy decisions. To enhance PCP infection control in Benue State, the study recommends that authorities should strengthen surveillance, improve early diagnosis and treatment, implement targeted public health interventions, utilize forecasting models for resource allocation, and encourage further research for improved predictive accuracy.