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JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook()
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 COV...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8886493/ https://www.ncbi.nlm.nih.gov/pubmed/35250112 http://dx.doi.org/10.1016/j.jue.2020.103314 |
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author | Kuchler, Theresa Russel, Dominic Stroebel, Johannes |
author_facet | Kuchler, Theresa Russel, Dominic Stroebel, Johannes |
author_sort | Kuchler, Theresa |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8886493 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88864932022-03-01 JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook() Kuchler, Theresa Russel, Dominic Stroebel, Johannes J Urban Econ Article 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. Elsevier Inc. 2022-01 2021-01-09 /pmc/articles/PMC8886493/ /pubmed/35250112 http://dx.doi.org/10.1016/j.jue.2020.103314 Text en © 2020 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Kuchler, Theresa Russel, Dominic Stroebel, Johannes JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook() |
title | JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook() |
title_full | JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook() |
title_fullStr | JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook() |
title_full_unstemmed | JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook() |
title_short | JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook() |
title_sort | jue insight: the geographic spread of covid-19 correlates with the structure of social networks as measured by facebook() |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8886493/ https://www.ncbi.nlm.nih.gov/pubmed/35250112 http://dx.doi.org/10.1016/j.jue.2020.103314 |
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