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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 (...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2020
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.
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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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