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 for epidemiological forecasting employ static parameter choices and cannot be readily contextualized, while adaptive solutions focus primarily on the reproduction number The current work proposes a novel model-agnostic Bayesian optimization approach for learning model parameters from observed data that generalizes to multiple application-specific fidelity criteria Empirical results point to the efficacy of the proposed method with SEIR-like models on COVID-19 case forecasting tasks A city-level forecasting system based on this method is being used for COVID-19 response in a few impacted Indian cities © 2021 Owner/Author