The goal of this research was to investigate the multifaceted interrelationships between the built and social environments and the impact of this relationship on population-level health in the context of the novel coronavirus disease 2019 (COVID-19). More specifically, this study assessed the relationship between several social determinants of health, including housing quality, living condition, travel pattern, race/ethnicity, household income, and COVID-19 outcomes in Washington, D.C (DC). Using built environment and social environment data extracted from the DC energy benchmarking and American Community Survey databases, more than 130,000 housing units were analyzed against COVID-19 case counts, death counts, mortality rate, age adjusted incidence rate and fatality rate data for DC wards. The results demonstrated that housing quality, living condition, race and occupation were strongly correlated with COVID-19 death count. The potential hot spots within DC were also identified based the regression model using currently available data. It can be concluded that based on the current available COVID-19 information, the identified combined built and social environment variables are the strongest and most significant predicators of COVID-19 death counts. And among those variables, crowding ratio has most significant influence, followed by work commute time and Black American Ratio.

这项研究的目的是调查在新型冠状病毒病2019（COVID-19）背景下建筑环境与社会环境之间的多方面相互关系以及这种关系对人口水平健康的影响。更具体地说，本研究评估了华盛顿的几个健康决定因素之间的关系，包括住房质量，生活条件，出行方式，种族/民族，家庭收入和COVID-19结果。使用从DC能源基准测试和美国社区调查数据库中提取的建筑环境和社会环境数据，针对DC病房的COVID-19病例数，死亡数，死亡率，年龄调整后的发病率和死亡率数据，分析了超过130,000个住房单元。结果表明，住房质量，生活条件，种族和职业与COVID-19死亡计数密切相关。还使用当前可用数据基于回归模型确定了DC内部的潜在热点。可以得出结论，根据当前可用的COVID-19信息，所确定的建筑环境和社会环境组合变量是COVID-19死亡计数的最强和最重要的预测因子。在这些变量中，拥挤率的影响最大，其次是通勤时间和美国黑人比率。确定的建筑环境和社会环境综合变量是COVID-19死亡人数最强，最重要的预测因素。在这些变量中，拥挤率影响最大，其次是通勤时间和美国黑人比率。确定的建筑环境和社会环境综合变量是COVID-19死亡人数最强，最重要的预测因素。在这些变量中，拥挤率影响最大，其次是通勤时间和美国黑人比率。