Abstract

The spread of COVID-19 in the U.S. prompted nonpharmaceutical interventions which caused a reduction in mobility everywhere, although with large disparities between different counties. Using a Bayesian spatial modeling framework, we investigated the association of county-level demographic and socioeconomic factors with changes in workplace mobility at two points in time: during the early stages of the epidemic (lockdown phase) and in the following phase (recovery phase) up to July 2020. While controlling for the perceived risk of infection, socioeconomic and demographic covariates explain about 40% of the variance in changes in workplace mobility during the lockdown phase, which reduces to about 10% during the recovery phase. During the lockdown phase, the results show larger drops in mobility in counties with richer families, that are less densely populated, with an older population living in dense neighborhoods, and with a lower proportion of Hispanic population. When also accounting for the residual spatial variability, the variance explained by the model increases to more than 70%, suggesting strong proximity effects potentially related to state- and county-wise regulations. These results provide community-level insights on the evolution of the U.S. mobility during the first wave of the epidemic that could directly benefit policy evaluation and interventions.

摘要

COVID-19 在美国的传播促使非药物干预措施导致各地流动性下降，尽管不同县之间存在很大差异。使用贝叶斯空间建模框架，我们在两个时间点调查了县级人口和社会经济因素与工作场所流动性变化的关联：在流行的早期阶段（锁定阶段）和随后的阶段（恢复阶段）) 直到 2020 年 7 月。在控制感知的感染风险的同时，社会经济和人口统计协变量解释了锁定阶段工作场所流动性变化的约 40% 的差异，而在恢复阶段减少到约 10%。在锁定阶段，结果显示，在家庭较富裕、人口密度较低、老年人口居住在密集社区以及西班牙裔人口比例较低的县，流动性下降幅度更大。当还考虑剩余空间变异性时，模型解释的方差增加到 70% 以上，表明可能与州和县级法规相关的强烈邻近效应。这些结果为美国的演变提供了社区层面的见解