Forecasting HIV Incidence Trajectories in Cebu City, Philippines: An ARIMA-Based Time-Series Analysis for Epidemic Surveillance and Public Health Planning

Abstract

Background: The Philippines is experiencing one of the fastest-growing HIV epidemics in the Western Pacific region, with Cebu City among the most affected urban centers. However, localized forecasting studies remain limited, constraining evidence-based public health planning and resource allocation. This study aims to develop and evaluate an Autoregressive Integrated Moving Average (ARIMA) model for forecasting monthly HIV incidence in Cebu City. Methods: Monthly HIV surveillance data from January 2022 to March 2025 were analyzed using time-series forecasting. Candidate ARIMA models were assessed using the Akaike Information Criterion (AIC), parameter significance, residual diagnostics, and out-of-sample forecasting accuracy. Results: ARIMA (0,1,1) demonstrated the best predictive performance, achieving a Mean Absolute Percentage Error (MAPE) of 25.04% and outperforming Naïve, Simple Moving Average, and Exponential Smoothing models. Forecasts for April to December 2025 project monthly HIV cases to increase from 33 to 36. Although forecasts suggest a persistent rise in HIV incidence, widening confidence intervals indicate greater uncertainty over longer forecasting horizons. Conclusion: The projected increase in HIV incidence signals a continuing public health challenge in Cebu City. ARIMA-based forecasting provides a practical decision-support tool for anticipating disease trends and informing surveillance, targeted prevention, and healthcare resource allocation.