Description:Human Immunodeficiency Virus (HIV) remains a major global public health challenge despite advancements in antiretroviral therapy and preventive healthcare systems. Accurate forecasting of HIV incidence and understanding disease transmission dynamics are essential for improving public health planning and clinical decision-making. The proposed invention introduces a hybrid analytical framework that integrates statistical time-series forecasting models with mechanistic epidemiological modeling for HIV incidence prediction and long-term disease analysis. The invention utilizes monthly HIV incidence data collected over a twenty-year period (2005–2025) and applies advanced forecasting techniques including ARIMA, SARIMA, and Exponential Smoothing (ETS) models. The ETS (Holt-Winters) model demonstrated superior predictive performance with the lowest Mean Absolute Percentage Error (MAPE) of 16.83%, enabling reliable short-term HIV forecasting. To enhance biological interpretability, the framework incorporates a Susceptible–Infected–AIDS (SIA) compartmental epidemiological model that evaluates disease transmission behavior using differential equations and reproduction number analysis. The estimated reproduction number (R0 ≈ 1.01) indicates a stable endemic equilibrium with controlled but persistent transmission. , C , C , Claims:• Hybrid Integrated Framework combining statistical forecasting models with epidemiological compartmental modeling for HIV incidence analysis.
• Dual-function predictive architecture capable of performing both short-term forecasting and long-term transmission dynamics evaluation.
• Application of ETS (Holt-Winters) forecasting model achieving superior prediction accuracy with minimum forecasting error (MAPE = 16.83%).
• Incorporation of SIA compartmental mathematical model for biological interpretation of HIV spread and epidemic stability assessment.
• Automated comparative evaluation mechanism using RMSE, MAE, MAPE, AIC, and BIC performance metrics.
• Integrated epidemic equilibrium assessment through computation of the basic reproduction number (R0).
• Seasonal decomposition-based trend analysis for identifying long-term HIV incidence patterns and periodic fluctuations.
• Residual validation and model calibration framework improving reliability and robustness of epidemiological predictions.
• Scalable architecture capable of integration with machine learning models such as LSTM and ensemble forecasting methods.
• Public health decision-support capability enabling healthcare organizations to optimize intervention and disease-control strategies.