What factors explain spatial variation in the severity of COVID-19 across the United States? To answer this question, we analyze the correlates of COVID-19 cases and deaths across US counties. We document four sets of facts. First, effective density is an important and persistent determinant of COVID-19 severity. Second, counties with more nursing home residents, lower income, higher poverty rates, and a greater presence of African Americans and Hispanics are disproportionately impacted, and these effects show no sign of disappearing over time. Third, the effect of certain characteristics, such as the distance to major international airports and the share of elderly individuals, dies out over time. Fourth, Trump-leaning counties are less severely affected early on, but later suffer from a large severity penalty.

哪些因素可以解释美国各地 COVID-19 严重程度的空间差异？为了回答这个问题，我们分析了美国各县 COVID-19 病例和死亡的相关性。我们记录了四组事实。首先，有效密度是 COVID-19 严重程度的一个重要且持久的决定因素。其次，疗养院居民较多、收入较低、贫困率较高以及非裔美国人和西班牙裔人口较多的县受到的影响尤为严重，而且这些影响并没有随着时间的推移而消失的迹象。第三，某些特征的影响，例如到主要国际机场的距离和老年人的比例，随着时间的推移而消失。第四，倾向于特朗普的县早期受到的影响较小，但后来受到了严重的惩罚。