Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
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pnas.2113561119 (1).pdf
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19.13 MB
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Adobe PDF
Checksum (MD5)
aeeda7c263936f2fd48b8a60254d8746
Author(s)
Shah, Devavrat
Date Issued
April 12, 2022
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
Abstract
Significance
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.
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.
MIT 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
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1073/pnas.2113561119
