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Twitter reveals human mobility dynamics during the COVID-19 pandemic
The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that ha...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654838/ https://www.ncbi.nlm.nih.gov/pubmed/33170889 http://dx.doi.org/10.1371/journal.pone.0241957 |
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author | Huang, Xiao Li, Zhenlong Jiang, Yuqin Li, Xiaoming Porter, Dwayne |
author_facet | Huang, Xiao Li, Zhenlong Jiang, Yuqin Li, Xiaoming Porter, Dwayne |
author_sort | Huang, Xiao |
collection | PubMed |
description | The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people’s travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states. |
format | Online Article Text |
id | pubmed-7654838 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-76548382020-11-18 Twitter reveals human mobility dynamics during the COVID-19 pandemic Huang, Xiao Li, Zhenlong Jiang, Yuqin Li, Xiaoming Porter, Dwayne PLoS One Research Article The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people’s travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states. Public Library of Science 2020-11-10 /pmc/articles/PMC7654838/ /pubmed/33170889 http://dx.doi.org/10.1371/journal.pone.0241957 Text en © 2020 Huang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Huang, Xiao Li, Zhenlong Jiang, Yuqin Li, Xiaoming Porter, Dwayne Twitter reveals human mobility dynamics during the COVID-19 pandemic |
title | Twitter reveals human mobility dynamics during the COVID-19 pandemic |
title_full | Twitter reveals human mobility dynamics during the COVID-19 pandemic |
title_fullStr | Twitter reveals human mobility dynamics during the COVID-19 pandemic |
title_full_unstemmed | Twitter reveals human mobility dynamics during the COVID-19 pandemic |
title_short | Twitter reveals human mobility dynamics during the COVID-19 pandemic |
title_sort | twitter reveals human mobility dynamics during the covid-19 pandemic |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654838/ https://www.ncbi.nlm.nih.gov/pubmed/33170889 http://dx.doi.org/10.1371/journal.pone.0241957 |
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