PropertyValue
?: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
?:creator
?:journal
  • ACM_Int._Conf._Proc._Ser.
?:license
  • unk
?:publication_isRelatedTo_Disease
?:source
  • WHO
?:title
  • Adaptive COVID-19 Forecasting via Bayesian Optimization
?:type
?:who_covidence_id
  • #1021131
?:year
  • 2020

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