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Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Author(s)
Shah, Devavrat
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Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/
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Abstract
<jats:title>Significance</jats:title> <jats:p>This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.</jats:p>
Date issued
2022-04-12
URI
https://hdl.handle.net/1721.1/143879
Department
Massachusetts Institute of Technology. Operations Research Center; Sloan School of Management; Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology; Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Journal
Proceedings of the National Academy of Sciences
Publisher
Proceedings of the National Academy of Sciences
Citation
Shah, Devavrat. 2022. "Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States." Proceedings of the National Academy of Sciences, 119 (15).
Version: Final published version

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