 JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by FacebookTheresa Kuchler, Dominic Russel and Johannes Stroebel Journal of Urban Economics, 2022, vol. 127, issue C Abstract: We use aggregated data from Facebook to show that COVID-19 is more likely to spread between regions with stronger social network connections. Areas with more social ties to two early COVID-19 “hotspots” (Westchester County, NY, in the U.S. and Lodi province in Italy) generally had more confirmed COVID-19 cases by the end of March. These relationships hold after controlling for geographic distance to the hotspots as well as the population density and demographics of the regions. As the pandemic progressed in the U.S., a county’s social proximity to recent COVID-19 cases and deaths predicts future outbreaks over and above physical proximity and demographics. In part due to its broad coverage, social connectedness data provides additional predictive power to measures based on smartphone location or online search data. These results suggest that data from online social networks can be useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19. Keywords: Social connectedness; COVID-19; Coronavirus; Communicable disease (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:juecon:v:127:y:2022:i:c:s0094119020300851 DOI: 10.1016/j.jue.2020.103314 Access Statistics for this article Journal of Urban Economics is currently edited by S.S. Rosenthal and W.C. Strange More articles in Journal of Urban Economics from Elsevier |
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