A Time Series Model for Predicting Human Immunodeficiency Virus in the Presence of Opportunistic Infections among Farmers in Benue State, Nigeria

The aim of this study is to provide a short-term prediction of Human Immunodeficiency Virus (HIV) in the presence of opportunistic infections among farmers in Benue state, Nigeria using Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) time series model. Monthly secondary data on HIV, Tuberculosis (TB), and Hepatitis B Virus (HBV) infections from January 2010 to December 2024 were sourced from the Benue State Epidemiological Unit, Makurdi. The study employed summary statistics, normality measures, time series plots; Ng-Perron modified unit root test, and ARIMAX model as methods of analysis. Employing the Box-Jenkins procedure, autocorrelation function (ACF), and partial autocorrelation function (PACF), a mixed ARIMAX (p,d,q) process was identified, with model selection based on log likelihoods (LogL), Akaike information criterion (AIC), Schwartz information criterion (SIC), and Hannan Quinn information criterion (HQC). The analysis revealed the series to be stationary in the first difference hence integrated of order one, I(1). The chosen ARIMAX (4,1,3) model, explaining 65.93% of data variability, forecasted HIV infections for 24 months from January 2025 to December 2026. The forecast depicted fluctuating trends in HIV infection rates, reflecting original dynamics, emphasizing the dynamic nature of HIV infection rates alongside opportunistic infections among farmers in Benue state, Nigeria. The forecast suggested 27,225 HIV total cases in the study area over 2025-2026, with an average monthly incidence of 1134 persons. A reliability test using actual and forecast values indicated no significant difference, affirming the reliability and accuracy of the forecasts for policy implementation. The study advocates collaborative efforts among the government of Benue state, international donor agencies, health policymakers, and stakeholders to implement robust preventive and control measures to mitigate future HIV incidences in the study area.