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10.1126/science.abc0473 http://scihub22266oqcxt.onion/10.1126/science.abc0473 32371477!7200009!32371477     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Science 2020 ; 368 (6497): 1362-1367 Nephropedia Template TP {{tp|p=32371477|t=2020. Rapid implementation of mobile technology for real-time epidemiology of COVID-19 |pdf=|usr=}}{{32371477}} gab.com Text Rapid implementation of mobile technology for real-time epidemiology of COVID-19 JO: Science http://www.kidney.de/pget.php?&tw=1&pmid=32371477 #FOAvip 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. Twit Text FOAVip Rapid implementation of mobile technology for real-time epidemiology of COVID-19 ????Science http://www.kidney.de/pget.php?&tw=1&pmid=32371477 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32371477 #FOAvip English Wikipedia <ref>{{Cite pmid|32371477}}</ref> Rapid implementation of mobile technology for real-time epidemiology of COVID-19 #MMPMID32371477 Drew DA; Nguyen LH; Steves CJ; Menni C; Freydin M; Varsavsky T; Sudre CH; Cardoso MJ; Ourselin S; Wolf J; Spector TD; Chan AT Science 2020[Jun]; 368 (6497): 1362-1367 PMID32371477show ga 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. |*International Cooperation[MESH] |*Mobile Applications[MESH] |Betacoronavirus[MESH] |Big Data[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology[MESH] |Data Collection/instrumentation/*methods[MESH] |Global Health[MESH] |Humans[MESH] |Models, Theoretical[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology[MESH] |SARS-CoV-2[MESH] |United Kingdom[MESH]Rapid implementation of mobile technology for real-time epidemiology o(epmc).pdf DeepDyve Pubget Overpricing