Susceptibility to infectious diseases such as COVID-19 depends on how those diseases spread. Many studies have examined the decrease in COVID-19 spread due to reduction in travel. However, less is known about how much functional geographic regions, which capture natural movements and social interactions, limit the spread of COVID-19. To determine boundaries between functional regions, we apply community-detection algorithms to large networks of mobility and social-media connections to construct geographic regions that reflect natural human movement and relationships at the county level in the coterminous United States. We measure COVID-19 case counts, case rates, and case-rate variations across adjacent counties and examine how often COVID-19 crosses the boundaries of these functional regions. We find that regions that we construct using GPS-trace networks and especially commute networks have the lowest COVID-19 case rates along the boundaries, so these regions may reflect natural partitions in COVID-19 transmission. Conversely, regions that we construct from geolocated Facebook friendships and Twitter connections yield less effective partitions. Our analysis reveals that regions that are derived from movement flows are more appropriate geographic units than states for making policy decisions about opening areas for activity, assessing vulnerability of populations, and allocating resources. Our insights are also relevant for policy decisions and public messaging in future emergency situations.

对 COVID-19 等传染病的易感性取决于这些疾病的传播方式。许多研究都探讨了由于旅行减少而导致的 COVID-19 传播的减少。然而，人们对于捕捉自然活动和社会互动的功能性地理区域在多大程度上限制了 COVID-19 的传播知之甚少。为了确定功能区域之间的边界，我们将社区检测算法应用于大型移动网络和社交媒体连接，以构建反映美国本土县级自然人类活动和关系的地理区域。我们测量相邻县的 COVID-19 病例数、病例率和病例率变化，并检查 COVID-19 跨越这些功能区域边界的频率。我们发现，我们使用 GPS 追踪网络构建的区域，尤其是通勤网络，其边界沿线的 COVID-19 病例率最低，因此这些区域可能反映了 COVID-19 传播的自然分区。相反，我们根据地理定位的 Facebook 好友关系和 Twitter 连接构建的区域产生的分区效果较差。我们的分析表明，源自人口流动的区域是比州更适合制定有关开放活动区域、评估人口脆弱性和分配资源的政策决策的地理单位。我们的见解也与未来紧急情况下的政策决策和公共信息相关。