Shanghai Key Laboratory of Urban Design and Urban Science, NYU Shanghai, Shanghai, China; Division of Arts and Sciences, NYU Shanghai, Shanghai, China.
Shanghai Key Laboratory of Urban Design and Urban Science, NYU Shanghai, Shanghai, China; PEAK Urban Programme, University of Oxford, Oxford, UK.
Health Place. 2023 Nov;84:103117. doi: 10.1016/j.healthplace.2023.103117. Epub 2023 Sep 26.
Previous research has explored the effect of the built environment on the spread of the coronavirus disease (COVID-19) pandemic. This study extends the existing literature by examining the relationship between pandemic prevalence and density, employment, and transit factors at the county level. Using multilinear spatial-lag regressions and time series clustering analyses on the Smart Location Database encompassing 3141 counties in the United States, our findings reveal the following: (1) Density, employment, and transit variables yield heterogeneous effects to infection rate, death rate, and mortality rate. (2) Pedestrian-oriented road density is positively correlated to the prevalence of COVID-19, every 0.011 miles/acre increase is associated with 1% increase in the infection rate. (3) A consistent negative correlation is observed between jobs per household and infection rate, while a decrease in unemployment rate leads to an increase in the death rate. (4) The results from time series analysis suggest that areas characterized by low auto-oriented intersection density but high pedestrian-oriented road density are more susceptible to the impacts of pandemics. This highlights the need to prioritize pandemic prevention efforts in the suburban and rural areas with low population density, as emphasized in existing literature emphasized.
先前的研究已经探讨了建筑环境对冠状病毒病(COVID-19)大流行传播的影响。本研究通过检查县一级的流行程度与密度、就业和交通因素之间的关系,扩展了现有文献。本研究使用包含美国 3141 个县的 Smart Location Database 进行多元线性空间滞后回归和时间序列聚类分析,得出以下结论:(1)密度、就业和交通变量对感染率、死亡率和病死率有不同的影响。(2)以行人为主导的道路密度与 COVID-19 的流行程度呈正相关,每增加 0.011 英里/英亩,感染率就会增加 1%。(3)每户工作岗位数与感染率呈负相关,而失业率的下降则会导致死亡率的上升。(4)时间序列分析的结果表明,那些以低汽车导向的交叉口密度但以高行人导向的道路密度为特征的地区更容易受到大流行的影响。这强调了在现有文献强调的人口密度较低的郊区和农村地区优先进行大流行预防工作的必要性。