The Geographic Spread of Covid-19 Correlates with the Structure of Social Networks as Measured by Facebook

We use anonymized and aggregated data from Facebook to show that areas with stronger social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally have more confirmed COVID-19 cases as of March 30, 2020. These relationships hold after controlling for geographic distance to the hotspots as well as for the income and population density of the regions. These results suggest that data from online social networks may prove useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.

[1]  Matt J. Keeling,et al.  Networks and the Epidemiology of Infectious Disease , 2010, Interdisciplinary perspectives on infectious diseases.

[2]  Alina Deshpande,et al.  Global Disease Monitoring and Forecasting with Wikipedia , 2014, PLoS Comput. Biol..

[3]  M. Keeling,et al.  Networks and epidemic models , 2005, Journal of The Royal Society Interface.

[4]  Cécile Viboud,et al.  Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic Scales , 2013, PLoS Comput. Biol..

[5]  Tao Wang,et al.  Learning from Friends in a Pandemic: Social Networks and the Macroeconomic Response of Consumption , 2020, SSRN Electronic Journal.

[6]  A Vespignani,et al.  Web‐based participatory surveillance of infectious diseases: the Influenzanet participatory surveillance experience , 2013, Clinical Microbiology and Infection.

[7]  Jianmin Jia,et al.  Population flow drives spatio-temporal distribution of COVID-19 in China , 2020, Nature.

[8]  Tarek Abdelzaher,et al.  Quantifying Projected Impact of Social Distancing Policies on COVID-19 Outcomes in the US , 2020, ArXiv.

[9]  R. Rosenfeld,et al.  A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States , 2019, Proceedings of the National Academy of Sciences.

[10]  Lang Li,et al.  Monitoring Potential Drug Interactions and Reactions via Network Analysis of Instagram User Timelines , 2015, PSB.

[11]  J. Potterat,et al.  Social networks and infectious disease: the Colorado Springs Study. , 1994, Social science & medicine.

[12]  E. Glaeser,et al.  Growth in Cities , 1991, Journal of Political Economy.

[13]  J. Gaudart,et al.  Using Mobile Phone Data to Predict the Spatial Spread of Cholera , 2015, Scientific Reports.

[14]  Rumi Chunara,et al.  Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons. , 2015, American journal of public health.

[15]  A. Klovdahl,et al.  Social networks and the spread of infectious diseases: the AIDS example. , 1985, Social science & medicine.

[16]  Theresa Kuchler,et al.  House Price Beliefs and Mortgage Leverage Choice , 2017, The Review of Economic Studies.

[17]  R. Mikolajczyk,et al.  Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases , 2008, PLoS medicine.

[18]  Evgeniy Gabrilovich,et al.  Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (version 1.0) , 2020, ArXiv.

[19]  Theresa Kuchler,et al.  The Economic Effects of Social Networks: Evidence from the Housing Market , 2017, Journal of Political Economy.

[20]  Konstantin Büchel,et al.  Cities and the Structure of Social Interactions: Evidence from Mobile Phone Data , 2017, Journal of Urban Economics.

[21]  Paramveer S. Dhillon,et al.  Interdependence and the cost of uncoordinated responses to COVID-19 , 2020, Proceedings of the National Academy of Sciences.

[22]  Theresa Kuchler,et al.  Peer Effects in Product Adoption , 2019, SSRN Electronic Journal.

[23]  Michael Bailey,et al.  The Determinants of Social Connectedness in Europe , 2020, SocInfo.

[24]  Alessandro Vespignani,et al.  Charting the Next Pandemic: Modeling Infectious Disease Spreading in the Data Science Age , 2018 .

[25]  Kevin R. Williams,et al.  Measuring Movement and Social Contact with Smartphone Data: A Real-Time Application to COVID-19 , 2020 .

[26]  M. Newman.  Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  J. Brueckner,et al.  Social Interaction and Urban Sprawl , 2006, SSRN Electronic Journal.

[28]  Manuel Rodriguez-Martinez,et al.  Twitter Health Surveillance (THS) System , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[29]  Ben Charoenwong,et al.  Social connections with COVID-19–affected areas increase compliance with mobility restrictions , 2020, Science Advances.

[30]  Theresa Kuchler,et al.  Social Connectedness in Urban Areas , 2019, Journal of Urban Economics.

[31]  Paul N Zivich,et al.  Social Media- and Internet-Based Disease Surveillance for Public Health. , 2020, Annual review of public health.

[32]  Raj Chetty,et al.  The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility , 2018 .

[33]  Glenn Ellison,et al.  Policy Implications of Models of the Spread of Coronavirus: Perspectives and Opportunities for Economists , 2020, SSRN Electronic Journal.

[34]  Kia Jahanbin,et al.  Using twitter and web news mining to predict COVID-19 outbreak , 2020 .

[35]  Tarek Abdelzaher,et al.  Analyzing the Design Space of Re-opening Policies and COVID-19 Outcomes in the US , 2020 .

[36]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[37]  Carol A Gotway Crawford,et al.  A New Source of Data for Public Health Surveillance: Facebook Likes , 2015, Journal of medical Internet research.

[38]  Kenth Engø-Monsen,et al.  Impact of human mobility on the emergence of dengue epidemics in Pakistan , 2015, Proceedings of the National Academy of Sciences.

[39]  J. Stroebel,et al.  Social Connectedness: Measurement, Determinants, and Effects. , 2018, The journal of economic perspectives : a journal of the American Economic Association.

[40]  Arpita Gupta,et al.  JUE Insight: Urban flight seeded the COVID-19 pandemic across the United States , 2022, Journal of Urban Economics.

[41]  S. Oliva,et al.  Modeling future spread of infections via mobile geolocation data and population dynamics. An application to COVID-19 in Brazil , 2020, PloS one.

[42]  Theresa Kuchler,et al.  International Trade and Social Connectedness , 2021 .

[43]  J. Stroebel,et al.  Social Proximity to Capital: Implications for Investors and Firms , 2020 .

[44]  David L. Smith,et al.  Quantifying the Impact of Human Mobility on Malaria , 2012, Science.

[45]  Pierre Picard,et al.  On spatial equilibria in a social interaction model , 2011, J. Econ. Theory.

[46]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[47]  J. Stroebel,et al.  Social Connectedness in Europe , 2020 .

[48]  Shashi Shekhar,et al.  Spatial Models , 2008, Encyclopedia of GIS.

[49]  Theresa Kuchler,et al.  Social Networks and Housing Markets , 2016, SSRN Electronic Journal.