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10.1038/s41562-020-00944-2 http://scihub22266oqcxt.onion/10.1038/s41562-020-00944-2 32848231!7501153!32848231     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32848231&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Hum+Behav 2020 ; 4 (9): 972-982 Nephropedia Template TP {{tp|p=32848231|t=2020. Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing |pdf=|usr=}}{{32848231}} gab.com Text Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing JO: Nat Hum Behav http://www.kidney.de/pget.php?&tw=1&pmid=32848231 #FOAvip Despite the widespread implementation of public health measures, coronavirus disease 2019 (COVID-19) continues to spread in the United States. To facilitate an agile response to the pandemic, we developed How We Feel, a web and mobile application that collects longitudinal self-reported survey responses on health, behaviour and demographics. Here, we report results from over 500,000 users in the United States from 2 April 2020 to 12 May 2020. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19-positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation; show a variety of exposure, occupational and demographic risk factors for COVID-19 beyond symptoms; reveal factors for which users have been SARS-CoV-2 PCR tested; and highlight the temporal dynamics of symptoms and self-isolation behaviour. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure and behavioural self-reported data to fight the COVID-19 pandemic. Twit Text FOAVip Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing ????Nat Hum Behav http://www.kidney.de/pget.php?&tw=1&pmid=32848231 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32848231 #FOAvip English Wikipedia <ref>{{Cite pmid|32848231}}</ref> Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing #MMPMID32848231 Allen WE; Altae-Tran H; Briggs J; Jin X; McGee G; Shi A; Raghavan R; Kamariza M; Nova N; Pereta A; Danford C; Kamel A; Gothe P; Milam E; Aurambault J; Primke T; Li W; Inkenbrandt J; Huynh T; Chen E; Lee C; Croatto M; Bentley H; Lu W; Murray R; Travassos M; Coull BA; Openshaw J; Greene CS; Shalem O; King G; Probasco R; Cheng DR; Silbermann B; Zhang F; Lin X Nat Hum Behav 2020[Sep]; 4 (9): 972-982 PMID32848231show ga Despite the widespread implementation of public health measures, coronavirus disease 2019 (COVID-19) continues to spread in the United States. To facilitate an agile response to the pandemic, we developed How We Feel, a web and mobile application that collects longitudinal self-reported survey responses on health, behaviour and demographics. Here, we report results from over 500,000 users in the United States from 2 April 2020 to 12 May 2020. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19-positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation; show a variety of exposure, occupational and demographic risk factors for COVID-19 beyond symptoms; reveal factors for which users have been SARS-CoV-2 PCR tested; and highlight the temporal dynamics of symptoms and self-isolation behaviour. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure and behavioural self-reported data to fight the COVID-19 pandemic. |*Betacoronavirus[MESH] |Adult[MESH] |Asymptomatic Diseases/epidemiology[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Clinical Laboratory Techniques/*statistics & numerical data[MESH] |Coronavirus Infections/diagnosis/*epidemiology/prevention & control/psychology[MESH] |Female[MESH] |Humans[MESH] |Longitudinal Studies[MESH] |Male[MESH] |Mobile Applications[MESH] |Models, Statistical[MESH] |Pandemics/prevention & control/statistics & numerical data[MESH] |Pneumonia, Viral/diagnosis/*epidemiology/prevention & control/psychology[MESH] |SARS-CoV-2[MESH]Population-scale longitudinal mapping of COVID-19 symptoms, behaviour (epmc).pdf DeepDyve Pubget Overpricing