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Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing
We employ the Google and Apple mobility data to identify, quantify and classify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Appl...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892828/ https://www.ncbi.nlm.nih.gov/pubmed/33602967 http://dx.doi.org/10.1038/s41598-021-83441-4 |
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author | Cot, Corentin Cacciapaglia, Giacomo Sannino, Francesco |
author_facet | Cot, Corentin Cacciapaglia, Giacomo Sannino, Francesco |
author_sort | Cot, Corentin |
collection | PubMed |
description | We employ the Google and Apple mobility data to identify, quantify and classify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Apple data, independently from the political decisions. Our analysis allows us to classify different shades of social distancing measures for the first wave of the pandemic. We observe a strong decrease in the infection rate occurring two to five weeks after the onset of mobility reduction. A universal time scale emerges, after which social distancing shows its impact. We further provide an actual measure of the impact of social distancing for each region, showing that the effect amounts to a reduction by 20–40% in the infection rate in Europe and 30–70% in the US. |
format | Online Article Text |
id | pubmed-7892828 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78928282021-02-23 Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing Cot, Corentin Cacciapaglia, Giacomo Sannino, Francesco Sci Rep Article We employ the Google and Apple mobility data to identify, quantify and classify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Apple data, independently from the political decisions. Our analysis allows us to classify different shades of social distancing measures for the first wave of the pandemic. We observe a strong decrease in the infection rate occurring two to five weeks after the onset of mobility reduction. A universal time scale emerges, after which social distancing shows its impact. We further provide an actual measure of the impact of social distancing for each region, showing that the effect amounts to a reduction by 20–40% in the infection rate in Europe and 30–70% in the US. Nature Publishing Group UK 2021-02-18 /pmc/articles/PMC7892828/ /pubmed/33602967 http://dx.doi.org/10.1038/s41598-021-83441-4 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Cot, Corentin Cacciapaglia, Giacomo Sannino, Francesco Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing |
title | Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing |
title_full | Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing |
title_fullStr | Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing |
title_full_unstemmed | Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing |
title_short | Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing |
title_sort | mining google and apple mobility data: temporal anatomy for covid-19 social distancing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892828/ https://www.ncbi.nlm.nih.gov/pubmed/33602967 http://dx.doi.org/10.1038/s41598-021-83441-4 |
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