An Autoregressive Moving Average Model for Short-Term Prediction of Non-Insulin Dependent Diabetes Among Farmers in Benue State

Abstract

This study employs an Autoregressive Moving Average (ARMA) time series model to forecast the short-term incidence of non-insulin-dependent diabetes mellitus (Type 2 Diabetes) among farmers in Benue State, Nigeria. The data was collected from the Benue State Epidemiological Unit, Makurdi, and covered a 20-year period from January 2005 to June 2025. The study employed descriptive statistics and normality measures, Augmented Dickey-Fuller (ADF) unit root test and ARMA (p,q) model as the principal analytical techniques and procedures used to examine the data. The descriptive statistics indicated moderate variability in diabetes cases over the years, while the Augmented Dickey-Fuller (ADF) test confirmed the stationarity of the series in level. Model choice based on Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC), and Hannan–Quinn Criterion (HQC) identified the ARMA(3,3) model as the best fit for forecasting diabetic cases in the study area. The model’s high coefficient of determination (R² = 0.8905) and statistically significant parameters (p < 0.05) demonstrated its robustness and predictive accuracy. Diagnostic checks using autocorrelation, partial autocorrelation, and the Ljung–Box Q-statistics showed that the residuals behaved like white noise, indicating a well-specified model. Forecast evaluations using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) confirmed that the model accurately good for predicting out-of-sample values. The forecast for July 2025 to June 2027 revealed a potential average of approximately 6,420 diabetes cases per month among farmers, with expected fluctuations over time. The study underscored the growing public health concern of diabetes among the farming population in Benue State and its implications for agricultural productivity and postharvest losses. The study concluded that predictive modeling can serve as a vital tool for health planners to design early intervention strategies, integrate health management with agricultural development, and enhance the overall well-being of rural farmers.