Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing
Name
2020.06.09.20126813v1.full.pdf
Description
Submitted version
Size
1.81 MB
Format
Adobe PDF
Checksum (MD5)
1a6acf453d32ebad272ec848ec9ab2f4
Author(s)
Allen, William E.
Altae-Tran, Han
Briggs, James
Jin, Xin
McGee, Glen
Shi, Andy
Raghavan, Rumya
Kamariza, Mireille
Nova, Nicole
Pereta, Albert
Date Issued
August 2020
Journal
Nature Human Behaviour
Publisher
Springer Science and Business Media LLC
Citation
Allen, William E. et al. "Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing." Nature Human Behaviour 4, 9 (August 2020): 972–982 © 2020 The Author(s)
Version
Original manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Harvard University--MIT Division of Health Sciences and Technology
McGovern Institute for Brain Research at MIT
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41562-020-00944-2
