Pilehvari Asal, You Wen, Chen Jiangzhuo, Krulick John, Venkatramanan Srini, Marathe Achla
University of Virginia.
Biocomplexity Institute, University of Virginia.
Res Sq. 2021 Sep 14:rs.3.rs-798357. doi: 10.21203/rs.3.rs-798357/v1.
To quantify lessons learned to better prepare for similar pandemic crisis in the future, we assess the overall impact of social distancing on the daily growth rate of COVID-19 infections in the U.S. during the initial phase of the pandemic and the impacts' heterogeneity by urbanity and social vulnerability of the counties. The initial phase is chosen to purposely identify the essential and largest impact of the first-line of defense measure for similar pandemic: social distancing.
Spatial Durbin models with county fixed effects were used to account for spatial dependencies and identify spatial spillover effects and spatial heterogeneity.
Besides the substantial curve flattening effects of social distancing, our results show significant spillover effects induced by neighboring counties' social distancing levels even in the absence of significant within-county effects. Urban and areas with high social vulnerability are the ones benefit the most from social distancing and high level of compliance is needed. Moderate level is enough in reaching the peak marginal impact in rural and areas with low social vulnerability.
为了量化所吸取的经验教训，以便更好地为未来类似的大流行危机做好准备，我们评估了社交距离对美国新冠疫情初期新冠病毒感染每日增长率的总体影响，以及按县的城市化程度和社会脆弱性划分的影响异质性。选择初期阶段是为了有针对性地确定类似大流行的第一道防线措施——社交距离的关键和最大影响。
使用具有县固定效应的空间杜宾模型来考虑空间依赖性，并识别空间溢出效应和空间异质性。
除了社交距离对曲线的显著平缓作用外，我们的结果表明，即使在县内没有显著影响的情况下，相邻县的社交距离水平也会产生显著的溢出效应。城市和社会脆弱性高的地区从社交距离中受益最大，需要高度的遵守率。在农村和社会脆弱性低的地区，适度的社交距离水平足以达到最大边际影响。
