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Stay-at-home orders associate with subsequent decreases in COVID-19 cases and fatalities in the United States

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  • James H Fowler
  • Seth J Hill
  • Remy Levin
  • Nick Obradovich

Abstract

Governments issue “stay-at-home” orders to reduce the spread of contagious diseases, but the magnitude of such orders’ effectiveness remains uncertain. In the United States these orders were not coordinated at the national level during the coronavirus disease 2019 (COVID-19) pandemic, which creates an opportunity to use spatial and temporal variation to measure the policies’ effect. Here, we combine data on the timing of stay-at-home orders with daily confirmed COVID-19 cases and fatalities at the county level during the first seven weeks of the outbreak in the United States. We estimate the association between stay-at-home orders and alterations in COVID-19 cases and fatalities using a difference-in-differences design that accounts for unmeasured local variation in factors like health systems and demographics and for unmeasured temporal variation in factors like national mitigation actions and access to tests. Compared to counties that did not implement stay-at-home orders, the results show that the orders are associated with a 30.2 percent (11.0 to 45.2) average reduction in weekly incident cases after one week, a 40.0 percent (23.4 to 53.0) reduction after two weeks, and a 48.6 percent (31.1 to 61.7) reduction after three weeks. Stay-at-home orders are also associated with a 59.8 percent (18.3 to 80.2) average reduction in weekly fatalities after three weeks. These results suggest that stay-at-home orders might have reduced confirmed cases by 390,000 (170,000 to 680,000) and fatalities by 41,000 (27,000 to 59,000) within the first three weeks in localities that implemented stay-at-home orders.

Suggested Citation

  • James H Fowler & Seth J Hill & Remy Levin & Nick Obradovich, 2021. "Stay-at-home orders associate with subsequent decreases in COVID-19 cases and fatalities in the United States," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-15, June.
  • Handle: RePEc:plo:pone00:0248849
    DOI: 10.1371/journal.pone.0248849
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    Cited by:

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    2. Yuqi Guo & Andrea R. Kaniuka & Jingjing Gao & Omar T. Sims, 2022. "An Epidemiologic Analysis of Associations between County-Level Per Capita Income, Unemployment Rate, and COVID-19 Vaccination Rates in the United States," IJERPH, MDPI, vol. 19(3), pages 1-9, February.
    3. Fatemeh Navazi & Yufei Yuan & Norm Archer, 2022. "The effect of the Ontario stay-at-home order on Covid-19 third wave infections including vaccination considerations: An interrupted time series analysis," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-18, April.
    4. Andy Hong & Sandip Chakrabarti, 2023. "Compact living or policy inaction? Effects of urban density and lockdown on the COVID-19 outbreak in the US," Urban Studies, Urban Studies Journal Limited, vol. 60(9), pages 1588-1609, July.
    5. Erhan Bayraktar & Asaf Cohen & April Nellis, 2021. "A Macroeconomic SIR Model for COVID-19," Mathematics, MDPI, vol. 9(16), pages 1-24, August.
    6. Ruohao Zhang, 2022. "Economic impact payment, human mobility and COVID-19 mitigation in the USA," Empirical Economics, Springer, vol. 62(6), pages 3041-3060, June.
    7. Mauro Caselli & Andrea Fracasso & Sergio Scicchitano, 2022. "From the lockdown to the new normal: individual mobility and local labor market characteristics following the COVID-19 pandemic in Italy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(4), pages 1517-1550, October.

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