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

Music City Center 

Accurate forecasting of the effective reproductive number (R_t) is crucial for informed public health decision-making. In this study, we develop a forecasting framework that integrates machine learning and probabilistic methods to improve predictive performance. We estimate R_t using the EpiNow2 R software package and further refine these estimates with a spatial (covariate-adjusted) smoothing technique. Forecasts are generated using an ensemble approach incorporating XGBoost, Random Forest (RF), and regression models. A stochastic Poisson framework is employed for daily COVID-19 case counts prediction. The ensemble method consistently outperformed EpiNow2, with a median percentage agreement of 94.7% (IQR:93.9–95.1%) for 7-day ahead forecasts during Wave-2, compared to 87.0% (IQR:84.4–89.4%) for EpiNow2. Similar improvements were observed in Wave-6, where the ensemble approach achieved a median percentage agreement of 92.5% (IQR:90.5–93.6, while EpiNow2 had a lower percentage agreement of 86.8% (IQR:82.5–89.2%). For daily case forecasting, the ensemble model maintained a higher percentage agreement across all horizons during both Wave-2 and Wave-6.

Infectious Disease Modeling

COVID-19

Effective Reproductive Number

Forecasting

Machine Learning 

Section on Statistical Learning and Data Science