Adaptive COVID-19 Forecasting via Bayesian Optimization
ACM Int. Conf. Proc. Ser.
; : 432, 2020.
Article
in English
| Scopus | ID: covidwho-1021131
Preprint
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This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
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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