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?:abstract
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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. In the current work, we propose a novel model-agnostic Bayesian optimization approach 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.
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?:doi
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10.1101/2020.10.19.20215293
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document_parses/pdf_json/a0470557bf1f0780969b70a5927b78623a9140f4.json
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?:title
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Adaptive COVID-19 Forecasting via Bayesian Optimization
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