Public health implications of opening National Football League stadiums during the COVID-19 pandemic
Name
pnas.2114226119.pdf
Description
Published version
Size
1.81 MB
Format
Adobe PDF
Checksum (MD5)
3845996250da56716239f46d1104280e
Author(s)
García Bulle, Bernardo
Shen, Dennis
Shah, Devavrat
Hosoi, Anette E
Date Issued
April 5, 2022
Journal
Proceedings of the National Academy of Sciences
Publisher
Proceedings of the National Academy of Sciences
Citation
García Bulle, Bernardo, Shen, Dennis, Shah, Devavrat and Hosoi, Anette E. 2022. "Public health implications of opening National Football League stadiums during the COVID-19 pandemic." Proceedings of the National Academy of Sciences, 119 (14).
Version
Final published version
Abstract
Significance
Using data from 2020, we measure the public health impact of allowing fans into sports stadiums during the COVID-19 pandemic; these results may inform future policy decisions regarding large outdoor gatherings during public health crises. Second, we demonstrate the utility of robust synthetic control in this context. Synthetic control and other statistical approaches may be used to exploit the underlying low-dimensional structure of the COVID-19 data and serve as useful instruments in analyzing the impact of mitigation strategies adopted by different communities. As with all statistical methods, reliable outcomes depend on proper implementation strategies and well-established robustness tests; in the absence of these safeguards, these statistical methods are likely to produce specious or misleading conclusions.
Using data from 2020, we measure the public health impact of allowing fans into sports stadiums during the COVID-19 pandemic; these results may inform future policy decisions regarding large outdoor gatherings during public health crises. Second, we demonstrate the utility of robust synthetic control in this context. Synthetic control and other statistical approaches may be used to exploit the underlying low-dimensional structure of the COVID-19 data and serve as useful instruments in analyzing the impact of mitigation strategies adopted by different communities. As with all statistical methods, reliable outcomes depend on proper implementation strategies and well-established robustness tests; in the absence of these safeguards, these statistical methods are likely to produce specious or misleading conclusions.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1073/pnas.2114226119
