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Population-scale Longitudinal Mapping of COVID-19 Symptoms, Behavior, and Testing Identifies Contributors to Continued Disease Spread in the United States
William E Allen; Han Altae-Tran; James Briggs; Xin Jin; Glen McGee; Rumya Raghavan; Andy Shi; Mireille Kamariza; Nicole Nova; Albert Pereta; Chris Danford; Amine Kamel; Patrik Gothe; Evrhet Milam; Jean Aurambault; Thorben Primke; Claire Li; Josh Inkenbrandt; Tuan Huynh; Evan Chen; Christina Lee; Michael Croatto; Helen Bentley; Wendy Lu; Robert Murray; Mark Travassos; John Openshaw; Brent Coull; Casey Greene; Ophir Shalem; Gary King; Ryan Probasco; David Cheng; Ben Silbermann; Feng Zhang; Xihong Lin.
  • William E Allen; Harvard University
  • Han Altae-Tran; MIT
  • James Briggs; Broad Institute of MIT and Harvard
  • Xin Jin; Broad Institute of MIT and Harvard
  • Glen McGee; Harvard University
  • Rumya Raghavan; Broad Institute of MIT and Harvard
  • Andy Shi; Harvard University
  • Mireille Kamariza; Harvard University
  • Nicole Nova; Stanford University
  • Albert Pereta; The How We Feel Project
  • Chris Danford; The How We Feel Project
  • Amine Kamel; The How We Feel Project
  • Patrik Gothe; The How We Feel Project
  • Evrhet Milam; The How We Feel Project
  • Jean Aurambault; The How We Feel Project
  • Thorben Primke; The How We Feel Project
  • Claire Li; The How We Feel Project
  • Josh Inkenbrandt; The How We Feel Project
  • Tuan Huynh; The How We Feel Project
  • Evan Chen; The How We Feel Project
  • Christina Lee; The How We Feel Project
  • Michael Croatto; The How We Feel Project
  • Helen Bentley; The How We Feel Project
  • Wendy Lu; The How We Feel Project
  • Robert Murray; The How We Feel Project
  • Mark Travassos; University of Maryland School of Medicine
  • John Openshaw; Stanford University
  • Brent Coull; Harvard University
  • Casey Greene; University of Pennsylvania
  • Ophir Shalem; University of Pennsylvania
  • Gary King; Harvard University
  • Ryan Probasco; The How We Feel Project
  • David Cheng; The How We Feel Project
  • Ben Silbermann; The How We Feel Project
  • Feng Zhang; MIT
  • Xihong Lin; Harvard University
Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-20126813
ABSTRACT
Summary ParagraphDespite social distancing and shelter-in-place policies, COVID-19 continues to spread in the United States. A lack of timely information about factors influencing COVID-19 spread and testing has hampered agile responses to the pandemic. We developed How We Feel, an extensible web and mobile application that aggregates self-reported survey responses, to fill gaps in the collection of COVID-19-related data. How We Feel collects longitudinal and geographically localized information on users health, behavior, and demographics. Here we report results from over 500,000 users in the United States from April 2, 2020 to May 12, 2020. We show that self-reported surveys can be used to build predictive models of COVID-19 test results, which may aid in identification of likely COVID-19 positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation, as well as for household and community exposure, occupation, and demographics being strong risk factors for COVID-19. We further reveal factors for which users have been SARS-CoV-2 PCR tested, as well as the temporal dynamics of self-reported symptoms and self-isolation behavior in positive and negative users. These results highlight the utility of collecting a diverse set of symptomatic, demographic, and behavioral self-reported data to fight the COVID-19 pandemic.
Texte intégral: Disponible Collection: Preprints Base de données: medRxiv Type d'étude: Étude observationnelle / Étude pronostique langue: Anglais Année: 2020 Type de document: Preprint

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Texte intégral: Disponible Collection: Preprints Base de données: medRxiv Type d'étude: Étude observationnelle / Étude pronostique langue: Anglais Année: 2020 Type de document: Preprint