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

arXiv: https://arxiv.org/abs/2005.05469 · HTML: https://mlsanity.com/paper/2005.05469/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 1 of 1)

- [Improving Causal Effect Estimation of Weighted RegressionBased Estimator using Neural Networks](https://mlsanity.com/paper/2110.15075/) (2021)

## Abstract (arXiv)

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%.