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Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study
BACKGROUND: In early 2020, the response to the SARS-CoV-2 pandemic focused on non-pharmaceutical interventions, some of which aimed to reduce transmission by changing mixing patterns between people. Aggregated location data from mobile phones are an important source of real-time information about hu...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Author(s). Published by Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563007/ https://www.ncbi.nlm.nih.gov/pubmed/34740555 http://dx.doi.org/10.1016/S2589-7500(21)00214-4 |
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author | Kishore, Nishant Taylor, Aimee R Jacob, Pierre E Vembar, Navin Cohen, Ted Buckee, Caroline O Menzies, Nicolas A |
author_facet | Kishore, Nishant Taylor, Aimee R Jacob, Pierre E Vembar, Navin Cohen, Ted Buckee, Caroline O Menzies, Nicolas A |
author_sort | Kishore, Nishant |
collection | PubMed |
description | BACKGROUND: In early 2020, the response to the SARS-CoV-2 pandemic focused on non-pharmaceutical interventions, some of which aimed to reduce transmission by changing mixing patterns between people. Aggregated location data from mobile phones are an important source of real-time information about human mobility on a population level, but the degree to which these mobility metrics capture the relevant contact patterns of individuals at risk of transmitting SARS-CoV-2 is not clear. In this study we describe changes in the relationship between mobile phone data and SARS-CoV-2 transmission in the USA. METHODS: In this population-based study, we collected epidemiological data on COVID-19 cases and deaths, as well as human mobility metrics collated by advertisement technology that was derived from global positioning systems, from 1396 counties across the USA that had at least 100 laboratory-confirmed cases of COVID-19. We grouped these counties into six ordinal categories, defined by the National Center for Health Statistics (NCHS) and graded from urban to rural, and quantified the changes in COVID-19 transmission using estimates of the effective reproduction number (R(t)) between Jan 22 and July 9, 2020, to investigate the relationship between aggregated mobility metrics and epidemic trajectory. For each county, we model the time series of R(t) values with mobility proxies. FINDINGS: We show that the reproduction number is most strongly associated with mobility proxies for change in the travel into counties (0·757 [95% CI 0·689 to 0·857]), but this relationship primarily holds for counties in the three most urban categories as defined by the NCHS. This relationship weakens considerably after the initial 15 weeks of the epidemic (0·442 [−0·492 to −0·392]), consistent with the emergence of more complex local policies and behaviours, including masking. INTERPRETATION: Our study shows that the integration of mobility metrics into retrospective modelling efforts can be useful in identifying links between these metrics and R(t). Importantly, we highlight potential issues in the data generation process for transmission indicators derived from mobile phone data, representativeness, and equity of access, which must be addressed to improve the interpretability of these data in public health. FUNDING: There was no funding source for this study. |
format | Online Article Text |
id | pubmed-8563007 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Author(s). Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85630072021-11-03 Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study Kishore, Nishant Taylor, Aimee R Jacob, Pierre E Vembar, Navin Cohen, Ted Buckee, Caroline O Menzies, Nicolas A Lancet Digit Health Articles BACKGROUND: In early 2020, the response to the SARS-CoV-2 pandemic focused on non-pharmaceutical interventions, some of which aimed to reduce transmission by changing mixing patterns between people. Aggregated location data from mobile phones are an important source of real-time information about human mobility on a population level, but the degree to which these mobility metrics capture the relevant contact patterns of individuals at risk of transmitting SARS-CoV-2 is not clear. In this study we describe changes in the relationship between mobile phone data and SARS-CoV-2 transmission in the USA. METHODS: In this population-based study, we collected epidemiological data on COVID-19 cases and deaths, as well as human mobility metrics collated by advertisement technology that was derived from global positioning systems, from 1396 counties across the USA that had at least 100 laboratory-confirmed cases of COVID-19. We grouped these counties into six ordinal categories, defined by the National Center for Health Statistics (NCHS) and graded from urban to rural, and quantified the changes in COVID-19 transmission using estimates of the effective reproduction number (R(t)) between Jan 22 and July 9, 2020, to investigate the relationship between aggregated mobility metrics and epidemic trajectory. For each county, we model the time series of R(t) values with mobility proxies. FINDINGS: We show that the reproduction number is most strongly associated with mobility proxies for change in the travel into counties (0·757 [95% CI 0·689 to 0·857]), but this relationship primarily holds for counties in the three most urban categories as defined by the NCHS. This relationship weakens considerably after the initial 15 weeks of the epidemic (0·442 [−0·492 to −0·392]), consistent with the emergence of more complex local policies and behaviours, including masking. INTERPRETATION: Our study shows that the integration of mobility metrics into retrospective modelling efforts can be useful in identifying links between these metrics and R(t). Importantly, we highlight potential issues in the data generation process for transmission indicators derived from mobile phone data, representativeness, and equity of access, which must be addressed to improve the interpretability of these data in public health. FUNDING: There was no funding source for this study. The Author(s). Published by Elsevier Ltd. 2022-01 2021-11-02 /pmc/articles/PMC8563007/ /pubmed/34740555 http://dx.doi.org/10.1016/S2589-7500(21)00214-4 Text en © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license 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 | Articles Kishore, Nishant Taylor, Aimee R Jacob, Pierre E Vembar, Navin Cohen, Ted Buckee, Caroline O Menzies, Nicolas A Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study |
title | Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study |
title_full | Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study |
title_fullStr | Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study |
title_full_unstemmed | Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study |
title_short | Evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for SARS-CoV-2 transmission in the USA: a population-based study |
title_sort | evaluating the reliability of mobility metrics from aggregated mobile phone data as proxies for sars-cov-2 transmission in the usa: a population-based study |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563007/ https://www.ncbi.nlm.nih.gov/pubmed/34740555 http://dx.doi.org/10.1016/S2589-7500(21)00214-4 |
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