Working Paper: NBER ID: w26990
Authors: Theresa Kuchler; Dominic Russel; Johannes Stroebel
Abstract: We use aggregated data from Facebook to show that COVID-19 was 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 as of the end of March. These relationships hold after controlling for geographic distance to the hotspots as well as for the income and population densities of the regions. As the pandemic progressed in the U.S., a county's social proximity to recent COVID- 19 cases predicts future outbreaks over and above physical proximity. These results suggest 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: COVID-19; Social Networks; Epidemiology; Public Health
JEL Codes: I00; R00
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| A county's social proximity to early COVID-19 hotspots (R23) | number of confirmed cases in that county (R10) |
| increase in social proximity to cases over time (C21) | growth in actual COVID-19 cases (O42) |
| stronger social network connections (Z13) | higher likelihood of COVID-19 spread (I14) |
| doubling in social proximity to cases (C21) | 225% increase in actual cases per 10,000 residents (I19) |
| models incorporating social proximity to cases (C31) | better predictions of future case growth (C53) |