Rapid implementation of mobile technology for real-time epidemiology of COVID-19

doi:10.1126/science.abc0473

GSTDTAP > 气候变化

DOI	10.1126/science.abc0473
	Rapid implementation of mobile technology for real-time epidemiology of COVID-19
	David A. Drew; Long H. Nguyen; Claire J. Steves; Cristina Menni; Maxim Freydin; Thomas Varsavsky; Carole H. Sudre; M. Jorge Cardoso; Sebastien Ourselin; Jonathan Wolf; Tim D. Spector; Andrew T. Chan; COPE Consortium§
	2020-06-19
发表期刊	Science
出版年	2020
英文摘要	The rapidity with which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreads through a population is defying attempts at tracking it, and quantitative polymerase chain reaction testing so far has been too slow for real-time epidemiology. Taking advantage of existing longitudinal health care and research patient cohorts, Drew et al. pushed software updates to participants to encourage reporting of potential coronavirus disease 2019 (COVID-19) symptoms. The authors recruited about 2 million users (including health care workers) to the COVID Symptom Study (previously known as the COVID Symptom Tracker) from across the United Kingdom and the United States. The prevalence of combinations of symptoms (three or more), including fatigue and cough, followed by diarrhea, fever, and/or anosmia, was predictive of a positive test verification for SARS-CoV-2. As exemplified by data from Wales, United Kingdom, mathematical modeling predicted geographical hotspots of incidence 5 to 7 days in advance of official public health reports. Science , this issue p. [1362][1] 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. [1]: /lookup/doi/10.1126/science.abc0473
领域	气候变化 ; 资源环境
URL	查看原文
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/276698
专题	气候变化资源环境科学
推荐引用方式 GB/T 7714	David A. Drew,Long H. Nguyen,Claire J. Steves,et al. Rapid implementation of mobile technology for real-time epidemiology of COVID-19[J]. Science,2020.
APA	David A. Drew.,Long H. Nguyen.,Claire J. Steves.,Cristina Menni.,Maxim Freydin.,...&COPE Consortium§.(2020).Rapid implementation of mobile technology for real-time epidemiology of COVID-19.Science.
MLA	David A. Drew,et al."Rapid implementation of mobile technology for real-time epidemiology of COVID-19".Science (2020).