Human-Network Regions as Effective Geographic Units for Disease Mitigation

Susceptibility to infectious diseases such as COVID-19 depends on how they spread, and many studies have captured the decrease in COVID-19 spread due to reduction in travel. However, less is known about practical geographic boundaries for that limit the spread of COVID-19 to adjacent places. To detect such boundaries, 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 for the continental United States. We measure COVID-19 cases, 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 commuter networks have the smallest rates of 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 the least 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.