Fig. 1. Proposed framework and timeline for model development and prospective evaluation.

a Our proposed AI-augmented epidemiology framework for COVID-19 forecasting is an extension to the standard Susceptible-Exposed-Infectious-Removed (SEIR) model^23,48. We model compartments for undocumented cases explicitly as they can dominate COVID-19 spread, and introduce compartments for hospital resource usage as they are crucial to forecasts for COVID-19 healthcare planning. Learnable encoders infer the rates at which individuals move through different compartments, trained on static and time-varying public data, to model the changing disease dynamics over time and extract the predictive signals from relevant data. The models are trained daily on all available data up to the day each prediction is made (see “Methods”). b Public dashboard that shows generated 28-day forecasts at county- and state level for the USA. A dashboard was similarly created in Japan at the prefecture level. c Predictions for the effective R number and force of infection that come from the compartmental nature of the model, as well as feature importances for the rates from the variable encoder architectures. d Simulations of counterfactual scenarios can be used to estimate the potential impact of vaccines or policy measures. e Prospective evaluation of the forecasts— on each prediction date, 28-day forecasts are released publicly, and the evaluation of the accuracy is performed at the end of the 28-day horizon.