This study explored how population mobility flows form commuting networks across US counties and influence the spread of COVID-19. We utilized 3-level mixed effects negative binomial regression models to estimate the impact of network COVID-19 exposure on county confirmed cases and deaths over time. We also conducted weighting-based analyses to estimate the causal effect of network exposure. Results showed that commuting networks matter for COVID-19 deaths and cases, net of spatial proximity, socioeconomic, and demographic factors. Different local racial and ethnic concentrations are also associated with unequal outcomes. These findings suggest that commuting is an important causal mechanism in the spread of COVID-19 and highlight the significance of interconnected of communities. The results suggest that local level mitigation and prevention efforts are more effective when complemented by similar efforts in the network of connected places. Implications for research on inequality in health and flexible work arrangements are discussed.
翻译:这项研究探讨了人口流动流动如何形成全美国各州的通勤网络,如何影响COVID-19的传播。我们利用3级混合效应负二元回归模型来估计COVID-19网络对县确诊病例和一段时间内死亡的影响。我们还进行了加权分析,以估计网络暴露的因果关系。研究结果显示,通勤网络对COVID-19的死亡和病例、在空间相近、社会经济和人口因素之间产生作用。不同的地方种族和族裔集中也与不平等的结果有关。这些调查结果表明,通勤是COVID-19传播的一个重要因果机制,突出了社区相互联系的重要性。结果显示,如果由互联地点网络的类似努力加以补充,地方一级的缓解和预防努力将更加有效。讨论了对保健不平等和灵活工作安排研究的影响。