In March and April 2020, public health authorities in the United States acted to mitigate transmission of COVID-19. These actions were not coordinated at the national level  , which creates an opportunity to use spatial and temporal variation to measure their effect with greater accuracy. We combine publicly available data sources on the timing of stay-at-home orders and daily confirmed COVID-19 cases  at the county level in the United States (N = 124,027). We then derive from the classic SIR model a two-way fixed-effects model and apply it to the data with controls for unmeasured differences between counties and over time. Mean county-level daily growth in COVID-19 infections peaked at 17.2\% just before stay-at-home  orders were issued. Two way fixed-effects regression estimates suggest that orders were associated with a 3.9 percentage point (95\% CI 1.2 to 6.6 ) reduction in the growth rate after one week and an 6.9 percentage point (2.4 to 11.5 ) reduction after two weeks . By day 27 the reduction (22.6 percentage points, 14.8 to 30.5) had surpassed the growth at the peak, indicating that growth had turned negative and the number of new daily infections was beginning to decline. A hypothetical national stay-at-home order issued on March 13 , 2020 when a national emergency was declared might have reduced cumulative infections by 63.3\%, and might have helped to reverse exponential growth in the disease by April 10. The results here suggest that a coordinated nationwide stay-at-home order may have reduced by hundreds of thousands the current number of infections and by tens of thousands the total number of deaths from COVID-19. Future efforts in the United States and elsewhere to control pandemics should coordinate stay-at-home orders at the national level,especially for diseases for which local spread has already occurred and testing availability is delayed .
Governments issue "stay at home" orders to reduce the spread of contagious diseases, but the magnitude of such orders' effectiveness is 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 with greater accuracy. Here, we combine data on the timing of stay-at-home orders with daily confirmed COVID-19 cases and fatalities at the county level in the United States . We estimate the effect of stay-at-home orders 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 ) reduction in weekly 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) reduction in weekly fatalities after three weeks. These results suggest that stay-at-home orders 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 where they were implemented .
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In March and April 2020, public health authorities in the United States acted to mitigate transmission of COVID-19. These actions
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during the coronavirus disease 2019 (COVID-19) pandemic
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We combine publicly available data sources
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and fatalities
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(N = 124,027). We then derive from the classic SIR model a two-way fixed-effects model and apply it to the data with controls for unmeasured differences between counties and over time. Mean county-level daily growth in COVID-19 infections peaked at 17.2\% just before
. We estimate the effect of
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orders 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
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2020 when a national emergency was declared might have reduced cumulative infections by 63.3\%, and might have helped to reverse exponential growth in the disease by April 10. The results here suggest that a coordinated nationwide
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) reduction in weekly fatalities after three weeks. These results suggest that
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order may have reduced by hundreds of thousands the current number of infections and by tens of thousands the total number of deaths from COVID-19. Future efforts in the United States and elsewhere to control pandemics should coordinate stay-at-home orders at the national level,especially for diseases for which local spread has already occurred and testing availability is delayed
orders 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 where they were implemented