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

May 11, 2020 Β· Declared Dead Β· πŸ› arXiv.org

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Authors M. Keith Chen, Yilin Zhuo, Malena de la Fuente, Ryne Rohla, Elisa F. Long arXiv ID 2005.05469 Category physics.soc-ph Cross-listed cs.SI, econ.GN, q-bio.PE Citations 36 Venue arXiv.org Last Checked 3 months ago
Abstract
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%.
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