Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing
View/ Open
HowWeFeel_NatureHumanBehaviour.docx (131.2Kb)
Access Status
Full text of the requested work is not available in DASH at this time ("restricted access"). For more information on restricted deposits, see our FAQ.Author
Altae-Tran, Han
Nova, Nicole
Pereta, Albert
Danford, Chris
Kamel, Amine
Gothe, Patrik
Milam, Evrhet
Aurambault, Jean
Primke, Thorben
Li, Weijie
Inkenbrandt, Josh
Huynh, Tuan
Chen, Evan
Lee, Christina
Croatto, Michael
Bentley, Helen
Lu, Wendy
Murray, Robert
Travassos, Mark
Openshaw, John
Greene, Casey S.
Shalem, Ophir
Probasco, Ryan
Cheng, David R.
Silbermann, Ben
Published Version
https://doi.org/10.1038/s41562-020-00944-2Metadata
Show full item recordCitation
Allen, William E., Han Altae-Tran, James Briggs, Xin Jin, Glen McGee, Andy Shi, Rumya Raghavan, et al. “Population-Scale Longitudinal Mapping of COVID-19 Symptoms, Behaviour and Testing.” Nature Human Behaviour 4, no. 9 (September 2020): 972–82. https://doi.org/10.1038/s41562-020-00944-2.Abstract
Despite the widespread implementation of public health measures, 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, 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 to identify likely COVID-19 positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation, show a variety of exposure, occupation, 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 behavior. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure, and behavioral self-reported data to fight the COVID-19 pandemic.Citable link to this page
https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37371889
Collections
- SPH Scholarly Articles [6362]
Contact administrator regarding this item (to report mistakes or request changes)