Causal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission

Accurately estimating the effectiveness of stay-at-home orders (SHOs) on reducing social contact and disease spread is crucial for mitigating pandemics. Leveraging individual-level location data for 10 million smartphones, we observe that by April 30th---when nine in ten Americans were under a SHO---daily movement had fallen 70% from pre-COVID levels. One-quarter of this decline is causally attributable to SHOs, with wide demographic differences in compliance, most notably by political affiliation. Likely Trump voters reduce movement by 9% following a local SHO, compared to a 21% reduction among their Clinton-voting neighbors, who face similar exposure risks and identical government orders. Linking social distancing behavior with an epidemic model, we estimate that reductions in movement have causally reduced SARS-CoV-2 transmission rates by 49%.

[1]  Danna Zhou,et al.  d. , 1934, Microbial pathogenesis.

[2]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[3]  J. Fowler,et al.  The Effect of Stay-at-Home Orders on COVID-19 Infections in the United States , 2020, medRxiv.

[4]  Babak Heydari,et al.  The Immediate Effect of COVID-19 Policies on Social-Distancing Behavior in the United States , 2020, medRxiv.

[5]  M. Lipsitch,et al.  Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period , 2020, Science.

[6]  M. Gentzkow,et al.  Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic☆ , 2020, Journal of Public Economics.

[7]  Martin Andersen,et al.  Early Evidence on Social Distancing in Response to COVID-19 in the United States , 2020, SSRN Electronic Journal.

[8]  Leonardo Bursztyn,et al.  Misinformation During a Pandemic , 2020, SSRN Electronic Journal.

[9]  Yael V. Hochberg,et al.  Risk Perception Through the Lens of Politics in the Time of the Covid-19 Pandemic , 2020, SSRN Electronic Journal.

[10]  N. Fullman,et al.  Pandemic Politics: Timing State-Level Social Distancing Responses to COVID-19 , 2020, medRxiv.

[11]  Kirsten Cornelson,et al.  Political polarization, social fragmentation, and cooperation during a pandemic , 2020 .

[12]  Adam Glynn,et al.  Front-Door Difference-in-Differences Estimators , 2017 .

[13]  S. Engle,et al.  Staying at Home: Mobility Effects of COVID-19 , 2020, SSRN Electronic Journal.

[14]  Tian Qiu,et al.  Political beliefs affect compliance with government mandates , 2020, Journal of Economic Behavior & Organization.

[15]  Jianmin Jia,et al.  Population flow drives spatio-temporal distribution of COVID-19 in China , 2020, Nature.

[16]  Joseph Sabia,et al.  Did California's Shelter-in-Place Order Work? Early Coronavirus-Related Public Health Effects , 2020, SSRN Electronic Journal.

[17]  J. Harris.  The Coronavirus Epidemic Curve is Already Flattening in New York City , 2020, SSRN Electronic Journal.

[18]  S. Bhatt,et al.  Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries , 2020 .

[19]  J. Pearl.  Causal diagrams for empirical research , 1995 .

[20]  M. K. Chen,et al.  The effect of partisanship and political advertising on close family ties , 2017, Science.

[21]  Long Wang,et al.  Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China☆ , 2020, Journal of Public Economics.

[22]  T. Hollingsworth,et al.  How will country-based mitigation measures influence the course of the COVID-19 epidemic? , 2020, The Lancet.