Adaptive COVID-19 Forecasting via Bayesian Optimization

Accurate forecasts of infections for localized regions are valuable for policy making and medical capacity planning. Existing compartmental and agent-based models [1, 7–11] for epidemiological forecasting employ static parameter choices and cannot be readily contextualized, while adaptive solutions [4, 13] focus primarily on the reproduction number. In the current work, we propose a novel model-agnostic Bayesian optimization approach [3] for learning model parameters from observed data that generalizes to multiple application-specific fidelity criteria. Empirical results demonstrate the efficacy of the proposed approach with SEIR-like compartmental models on COVID-19 case forecasting tasks. A city-level forecasting system based on this approach is being used for COVID-19 response in a few highly impacted Indian cities.