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Rapid implementation of mobile technology for real-time epidemiology of COVID-19
The rapid pace of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents challenges to the robust collection of population-scale data to address this global health crisis. We established the COronavirus Pandemic Epidemiology (...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7200009/ https://www.ncbi.nlm.nih.gov/pubmed/32371477 http://dx.doi.org/10.1126/science.abc0473 |
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author | Drew, David A. Nguyen, Long H. Steves, Claire J. Menni, Cristina Freydin, Maxim Varsavsky, Thomas Sudre, Carole H. Cardoso, M. Jorge Ourselin, Sebastien Wolf, Jonathan Spector, Tim D. Chan, Andrew T. |
author_facet | Drew, David A. Nguyen, Long H. Steves, Claire J. Menni, Cristina Freydin, Maxim Varsavsky, Thomas Sudre, Carole H. Cardoso, M. Jorge Ourselin, Sebastien Wolf, Jonathan Spector, Tim D. Chan, Andrew T. |
author_sort | Drew, David A. |
collection | PubMed |
description | The rapid pace of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents challenges to the robust collection of population-scale data to address this global health crisis. We established the COronavirus Pandemic Epidemiology (COPE) Consortium to unite scientists with expertise in big data research and epidemiology to develop the COVID Symptom Study, previously known as the COVID Symptom Tracker, mobile application. This application—which offers data on risk factors, predictive symptoms, clinical outcomes, and geographical hotspots—was launched in the United Kingdom on 24 March 2020 and the United States on 29 March 2020 and has garnered more than 2.8 million users as of 2 May 2020. Our initiative offers a proof of concept for the repurposing of existing approaches to enable rapidly scalable epidemiologic data collection and analysis, which is critical for a data-driven response to this public health challenge. |
format | Online Article Text |
id | pubmed-7200009 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-72000092020-05-06 Rapid implementation of mobile technology for real-time epidemiology of COVID-19 Drew, David A. Nguyen, Long H. Steves, Claire J. Menni, Cristina Freydin, Maxim Varsavsky, Thomas Sudre, Carole H. Cardoso, M. Jorge Ourselin, Sebastien Wolf, Jonathan Spector, Tim D. Chan, Andrew T. Science Reports The rapid pace of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents challenges to the robust collection of population-scale data to address this global health crisis. We established the COronavirus Pandemic Epidemiology (COPE) Consortium to unite scientists with expertise in big data research and epidemiology to develop the COVID Symptom Study, previously known as the COVID Symptom Tracker, mobile application. This application—which offers data on risk factors, predictive symptoms, clinical outcomes, and geographical hotspots—was launched in the United Kingdom on 24 March 2020 and the United States on 29 March 2020 and has garnered more than 2.8 million users as of 2 May 2020. Our initiative offers a proof of concept for the repurposing of existing approaches to enable rapidly scalable epidemiologic data collection and analysis, which is critical for a data-driven response to this public health challenge. American Association for the Advancement of Science 2020-06-19 2020-05-05 /pmc/articles/PMC7200009/ /pubmed/32371477 http://dx.doi.org/10.1126/science.abc0473 Text en Copyright © 2020 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). http://creativecommons.org/licenses/by/4.0/ https://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 use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Reports Drew, David A. Nguyen, Long H. Steves, Claire J. Menni, Cristina Freydin, Maxim Varsavsky, Thomas Sudre, Carole H. Cardoso, M. Jorge Ourselin, Sebastien Wolf, Jonathan Spector, Tim D. Chan, Andrew T. Rapid implementation of mobile technology for real-time epidemiology of COVID-19 |
title | Rapid implementation of mobile technology for real-time epidemiology of COVID-19 |
title_full | Rapid implementation of mobile technology for real-time epidemiology of COVID-19 |
title_fullStr | Rapid implementation of mobile technology for real-time epidemiology of COVID-19 |
title_full_unstemmed | Rapid implementation of mobile technology for real-time epidemiology of COVID-19 |
title_short | Rapid implementation of mobile technology for real-time epidemiology of COVID-19 |
title_sort | rapid implementation of mobile technology for real-time epidemiology of covid-19 |
topic | Reports |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7200009/ https://www.ncbi.nlm.nih.gov/pubmed/32371477 http://dx.doi.org/10.1126/science.abc0473 |
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