Data-Driven-Forecasting-of-Morbidity-Trends-using-ARIMA-Models-Statistical-Approach-to-Public-Health-Planning

For the capability to enhance the public health systems and evidence-based policymaking, accurate prediction of disease patterns is crucial. Statistical models provide useful tools for the projection of future health issues, as indicated by the growing demand for proactive healthcare strategies. With the use of past statistics from 2020 to 2024, this present study makes use of the Autoregressive Integrated Moving Average (ARIMA) model to project morbidity trends for major health conditions in the Philippines from 2025 to 2030. Some of the diseases that were reviewed include heart disease, hypertension, diabetes, schistosomiasis, and tuberculosis (TB). Results reflect a steady decrease in deaths related to diabetes, a steady annual increase in heart disease and hypertension cases, and uneven trends in schistosomiasis and tuberculosis cases. The potential of the ARIMA model to effectively predict short-term health outcomes is reflected in the apparent linear and seasonal trends it exhibits across disease. In planning to lower future disease burdens, optimize healthcare resources, and create evidence-based interventions, health authorities must take such findings into account. Statistical forecasting is also stressed to be crucial in enhancing public health preparedness and decision-making.