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Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data
Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We quantified interpersonal contact at the population level using mobile device geolocation data. We computed the frequency of contact (within 6 feet) between...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741180/ https://www.ncbi.nlm.nih.gov/pubmed/34995121 http://dx.doi.org/10.1126/sciadv.abi5499 |
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author | Crawford, Forrest W. Jones, Sydney A. Cartter, Matthew Dean, Samantha G. Warren, Joshua L. Li, Zehang Richard Barbieri, Jacqueline Campbell, Jared Kenney, Patrick Valleau, Thomas Morozova, Olga |
author_facet | Crawford, Forrest W. Jones, Sydney A. Cartter, Matthew Dean, Samantha G. Warren, Joshua L. Li, Zehang Richard Barbieri, Jacqueline Campbell, Jared Kenney, Patrick Valleau, Thomas Morozova, Olga |
author_sort | Crawford, Forrest W. |
collection | PubMed |
description | Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We quantified interpersonal contact at the population level using mobile device geolocation data. We computed the frequency of contact (within 6 feet) between people in Connecticut during February 2020 to January 2021 and aggregated counts of contact events by area of residence. When incorporated into a SEIR-type model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns. Contact in Connecticut explains the initial wave of infections during March to April, the drop in cases during June to August, local outbreaks during August to September, broad statewide resurgence during September to December, and decline in January 2021. The transmission model fits COVID-19 transmission dynamics better using the contact rate than other mobility metrics. Contact rate data can help guide social distancing and testing resource allocation. |
format | Online Article Text |
id | pubmed-8741180 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-87411802022-01-20 Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data Crawford, Forrest W. Jones, Sydney A. Cartter, Matthew Dean, Samantha G. Warren, Joshua L. Li, Zehang Richard Barbieri, Jacqueline Campbell, Jared Kenney, Patrick Valleau, Thomas Morozova, Olga Sci Adv Social and Interdisciplinary Sciences Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We quantified interpersonal contact at the population level using mobile device geolocation data. We computed the frequency of contact (within 6 feet) between people in Connecticut during February 2020 to January 2021 and aggregated counts of contact events by area of residence. When incorporated into a SEIR-type model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns. Contact in Connecticut explains the initial wave of infections during March to April, the drop in cases during June to August, local outbreaks during August to September, broad statewide resurgence during September to December, and decline in January 2021. The transmission model fits COVID-19 transmission dynamics better using the contact rate than other mobility metrics. Contact rate data can help guide social distancing and testing resource allocation. American Association for the Advancement of Science 2022-01-07 /pmc/articles/PMC8741180/ /pubmed/34995121 http://dx.doi.org/10.1126/sciadv.abi5499 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Social and Interdisciplinary Sciences Crawford, Forrest W. Jones, Sydney A. Cartter, Matthew Dean, Samantha G. Warren, Joshua L. Li, Zehang Richard Barbieri, Jacqueline Campbell, Jared Kenney, Patrick Valleau, Thomas Morozova, Olga Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data |
title | Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data |
title_full | Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data |
title_fullStr | Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data |
title_full_unstemmed | Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data |
title_short | Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data |
title_sort | impact of close interpersonal contact on covid-19 incidence: evidence from 1 year of mobile device data |
topic | Social and Interdisciplinary Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8741180/ https://www.ncbi.nlm.nih.gov/pubmed/34995121 http://dx.doi.org/10.1126/sciadv.abi5499 |
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