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JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook

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  • Kuchler, Theresa
  • Russel, Dominic
  • Stroebel, Johannes

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.

Suggested Citation

  • Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    • Handle: RePEc:eee:juecon:v:127:y:2022:i:c:s0094119020300851
    DOI: 10.1016/j.jue.2020.103314
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    References listed on IDEAS

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    3. Zhang, Zhiwei & Liu, Gao & Chen, Bin & Huang, Kun, 2022. "Social asset or social liability? How partisanship moderates the relationship between social capital and Covid-19 vaccination rates across United States counties," Social Science & Medicine, Elsevier, vol. 311(C).
    4. Coven, Joshua & Gupta, Arpit & Yao, Iris, 2023. "JUE Insight: Urban flight seeded the COVID-19 pandemic across the United States," Journal of Urban Economics, Elsevier, vol. 133(C).
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    6. Michael Bailey & Drew M. Johnston & Martin Koenen & Theresa Kuchler & Dominic Russel & Johannes Stroebel, 2022. "The Social Integration of International Migrants: Evidence from the Networks of Syrians in Germany," NBER Working Papers 29925, National Bureau of Economic Research, Inc.
    7. Liu, Yandong & Shi, Yu & Wang, Yang & Tian, Zhu, 2024. "Social networks and household consumption," Finance Research Letters, Elsevier, vol. 60(C).
    8. Wan, Liyang & Wan, Qian, 2022. "High-speed railway and the intercity transmission of epidemics: Evidence from COVID-19 in China," Economic Modelling, Elsevier, vol. 114(C).

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