Machine Learning and Probabilistic Approaches for Forecasting Infectious Disease Transmission and Cases

disease transmission dynamics. Integrated Regression, Random Forest, and XGBoost models into an ensemble forecasting approach. Achieved up to 96% agreement for 7-day forecasts for COVID-19 across counties in SC. Framework supports real-time epidemic surveillance and early intervention decisions. The approach is generalizable to other infectious diseases and finer geographic scales for real-time outbreak monitoring and response.