Machine learning and probabilistic approaches for forecasting infectious disease transmission and cases

Forecasting the effective reproductive number (R_t) and infection case counts is critical for guiding public health responses. We developed a machine learning and probabilistic forecasting framework to predict R_t and daily COVID-19 cases, respectively, across South Carolina counties, with the flexibility to generalize to other infectious diseases.

We first estimated R_t using the EpiNow2 R package, which incorporates Bayesian time-series modeling and accounts for reporting delay and incubation period. These initial estimates were refined using spatial covariate-adjusted smoothing through the Integrated Nested Laplace Approximation (INLA). We then generated R_t forecasts using an ensemble of linear regression, random forest, and XGBoost models. Daily case forecasts were obtained by linking R_t trajectories with historical case data via a Poisson model.

This ensemble-based approach 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 pepercentage agreement (PA) of 96.5% (IQR: 95.4-97.1%) for 7-day horizon R_t forecast, compared to 87.0% (IQR: 84.4-89.4%) from EpiNow2. In the second period (December 11, 2022-March 04, 2023), the ensemble attained a 93.0% median PA for R_t forecast (IQR: 90.8-95.4%), while EpiNow2 reached 86.8% (IQR: 82.5-89.2%). Similar trends were observed for case forecasts, with the ensemble model demonstrating improved performance.

This study presents a flexible forecasting framework that integrates Bayesian estimation, spatial smoothing, and ensemble machine learning to improve the accuracy of COVID-19 transmission and case forecasts. The approach enhances epidemic forecasting performance and offers scalable tools to support data-driven public health preparedness and response.