Machine Learning and Probabilistic Approaches for Forecasting COVID-19 Transmission and Cases.

ABSTRACT: Forecasting the effective reproductive number ( Rt ) and COVID-19 case counts are critical for guiding public health responses. We developed a machine learning and probabilistic forecasting framework to predict Rt and daily case counts at the county level in South Carolina (SC). Our approach utilized initial Rt estimates from EpiNow2 R package refined with spatial (covariate-adjusted) smoothing. We then generated Rt forecasts using an ensemble of regression, Random Forest, and XGBoost models, and predicted case counts with a probabilistic Poisson model. This ensemble-based approach consistently outperformed EpiNow2 across different forecast horizons (7-day, 14-day, and 21-day). In the first forecast period (November 11, 2020 - February 02, 2021), the ensemble achieved a median percentage a

SUBMITTER: Hossain MS 

PROVIDER: S-EPMC12262794 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature