论文标题
SARS-COV-2传输的家庭订单的因果估计
Causal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission
论文作者
论文摘要
准确地估算出院订单（SHO）对减少社会接触和疾病传播的有效性对于缓解大流行者至关重要。利用个人级别位置数据的1000万智能手机数据，我们观察到，到4月30日，当10名美国人的九分之一处于SHO中时，每天的运动已从前卵库水平下降了70％。这一下降的四分之一是因果关系，归因于SHO，在依从性方面存在广泛的人口差异，最著名的是政治隶属关系。特朗普选民在当地SHO后可能会减少9％的行动，而克林顿投票给邻国的降低了21％，他们面临类似的曝光风险和相同的政府命令。将社会距离行为与流行模型联系起来，我们估计运动的减少使SARS-COV-2传输速率降低了49％。
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%.
