The Effect of Stay-at-Home Orders on COVID-19 Cases and Fatalities in the United States

Abstract

Background In March and April 2020, public health authorities in the United States acted to mitigate transmission of and hospitalizations from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 . These actions were not coordinated at the national level, which raises the question of what might have happened if they were. It also creates an opportunity to use spatial and temporal variation to measure their effect with greater accuracy. MethodsWe 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. This enables us to estimate the effect of stay-at-home orders while accounting for local variation in factors like health systems and demographics, and temporal variation in national mitigation actions, access to tests, or exposure to media reports that could influence the course of the disease.Findings 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 a 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.Interpretation Although stay-at-home orders impose great costs to society, delayed responses and piecemeal application of these orders generate similar costs without obtaining the full potential benefits suggested by this analysis. The results here suggest that a coordinated 1 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) : medRxiv preprint nationwide stay-at-home order might have reduced by hundreds of thousands the current number of infections and by tens of thousands the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. Since stay-at-home orders reduce infection growth rates, early implementation when infection counts are still low would be most beneficial.Funding None.

What Explains Temporal and Geographic Variation in the Early US Coronavirus Pandemic?

“…Second, several recent papers have studied the effects of stay-at-home policies on economic outcomes (Baker et al 2020;Bartik et al 2020;Chen et al 2020b;Chetty et al 2020; Kong and Prinz 2020). Third, another set of papers quantifies the effects of regulation on health outcomes (Childs et al 2020;Flaxman et al 2020;Fowler et al 2020;Friedson et al 2020;Greenstone and Nigam 2020;Lasry et al 2020). In the epidemiological literature, there are a set of what economists might call "structural" models that use Bayesian techniques to estimate the reproduction number R; these estimates often pay less attention to identifying the causal effect of policies on R (Cori et al 2013;Thompson et al 2013).…”

“…Friedson et al (2020) use a synthetic control estimator with log cases on the left-hand side to estimate the effect of California's stay-at-home order Fowler et al (2020). andCourtemanche et al (2020) use a difference-in-difference specification with log(C i,t+1 ) − log(C it ) on the left-hand side, which gives log(C i,t+1 ) − log(C it ) = log(β it I it +C it ) − log(β i,t−1 I i,t−1 +C i,t−1 ).…”

Trends in Family Violence Are Not Causally Associated with COVID-19 Stay-at-Home Orders: a Commentary on Piquero et al.

“…Finally, inaccurate study results, when widely distributed to news outlets, have the potential to impact stay-athome policies and potentially, cause harm. Stay-at-home orders have been associated with a 60% reduction in COVID-19 cases three weeks after their implementation, with rates increasing each week following their enactment (Fowler, Hill, Obradovich and Levin, 2020). The widespread dissemination of inaccurate research findings, like those described above, have important implications for policy and the virus mitigation efforts, which might urge policymakers to terminate stay-at-home orders in an effort to reduce family violence and other social risk factors.…”

Donald Trump and the Parties: Impeachment, Pandemic, Protest, and Electoral Politics in 2020

“…His bans on travel from China and, weeks later, from some European countries were too little and too late. He resisted calling for social distancing, with school and business closures, until March 16, weeks after his scientific advisors had concluded these steps were essential, a delay that research suggests meant many more cases and deaths (Fowler, et al, 2020;Pei, Kandula and Shaman 2020).…”