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Adaptive COVID-19 Forecasting via Bayesian Optimization
ACM Int. Conf. Proc. Ser. ; : 432, 2020.
Article in English | Scopus | ID: covidwho-1021131
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ABSTRACT
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.

Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: ACM Int. Conf. Proc. Ser. Year: 2020 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: ACM Int. Conf. Proc. Ser. Year: 2020 Document Type: Article