The Coronavirus Disease 19 (COVID-19) has quickly spread across the United States (U.S.) since community transmission was first identified in January 2020. While a number of studies have examined individual-level risk factors for COVID-19, few studies have examined geographic hotspots and community drivers associated with spatial patterns in local transmission. The objective of the study is to understand the spatial determinants of the pandemic in counties across the U.S. by comparing socioeconomic variables to case and death data from January 22nd to June 30th, 2020. A cluster analysis was performed to examine areas of high-risk, followed by a three-stage regression to examine contextual factors associated with elevated risk patterns for morbidity and mortality. The factors associated with community-level vulnerability included age, disability, language, race, occupation, and urban status. We recommend that cluster detection and spatial analysis be included in population-based surveillance strategies to better inform early case detection and prioritize healthcare resources.

自 2020 年 1 月首次发现社区传播以来，冠状病毒病 19 (COVID-19) 已在美国 (US) 迅速传播。虽然许多研究都检查了 COVID-19 的个体层面风险因素，但很少有研究检查与当地传播空间模式相关的地理热点和社区驱动因素。该研究的目的是通过将社会经济变量与 2020 年 1 月 22 日至 6 月 30 日的病例和死亡数据进行比较，了解美国各县疫情大流行的空间决定因素。随后进行三阶段回归，以检查与发病率和死亡率升高的风险模式相关的背景因素。与社区层面脆弱性相关的因素包括年龄、残疾、语言、种族、职业和城市地位。我们建议将聚类检测和空间分析纳入基于人群的监测策略中，以便更好地为早期病例检测提供信息并优先考虑医疗资源。