Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing

William E. Allen; Han Altae-Tran; James Briggs; Xin Jin; Glen McGee; Andy Shi; Rumya Raghavan; Mireille Kamariza; Nicole Nova; Albert Pereta; Chris Danford; Amine Kamel; Patrik Gothe; Evrhet Milam; Jean Aurambault; Thorben Primke; Weijie Li; Josh Inkenbrandt; Tuan Huynh; Evan Chen; Christina Lee; Michael Croatto; Helen Bentley; Wendy Lu; Robert Murray; Mark Travassos; Brent A. Coull; John Openshaw; Casey S. Greene; Ophir Shalem; Gary King; Ryan Probasco; David R. Cheng; Ben Silbermann; Feng Zhang; Xihong Lin

doi:10.1038/s41562-020-00944-2

Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing

William E. Allen^*
, Han Altae-Tran
, James Briggs
, Xin Jin
, Glen McGee
, Andy Shi
, Rumya Raghavan
, Mireille Kamariza
, Nicole Nova
, Albert Pereta
, Chris Danford
, Amine Kamel
, Patrik Gothe
, Evrhet Milam
, Jean Aurambault
, Thorben Primke
, Weijie Li
, Josh Inkenbrandt
, Tuan Huynh
, Evan Chen

Christina Lee, Michael Croatto, Helen Bentley, Wendy Lu, Robert Murray, Mark Travassos, Brent A. Coull, John Openshaw, Casey S. Greene, Ophir Shalem, Gary King, Ryan Probasco, David R. Cheng, Ben Silbermann, Feng Zhang^*, Xihong Lin^*

^*Corresponding author for this work

The How We Feel Project
Harvard University
Broad Institute
Massachusetts Institute of Technology
Schmidt Science Fellows
Stanford University
University of Maryland, Baltimore
University of Pennsylvania
Howard Hughes Medical Institute

Research output: Contribution to journal › Article › peer-review

82 Scopus citations

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.

Original language	English
Pages (from-to)	972-982
Number of pages	11
Journal	Nature Human Behaviour
Volume	4
Issue number	9
DOIs	https://doi.org/10.1038/s41562-020-00944-2
State	Published - 1 Sep 2020
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1038/s41562-020-00944-2

Cite this

Allen, W. E., 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., ... Lin, X. (2020). Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing. Nature Human Behaviour, 4(9), 972-982. https://doi.org/10.1038/s41562-020-00944-2

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title = "Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing",

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.",

author = "Allen, \{William E.\} and Han Altae-Tran and James Briggs and Xin Jin and Glen McGee and Andy Shi and Rumya Raghavan and Mireille Kamariza and Nicole Nova and Albert Pereta and Chris Danford and Amine Kamel and Patrik Gothe and Evrhet Milam and Jean Aurambault and Thorben Primke and Weijie Li and Josh Inkenbrandt and Tuan Huynh and Evan Chen and Christina Lee and Michael Croatto and Helen Bentley and Wendy Lu and Robert Murray and Mark Travassos and Coull, \{Brent A.\} and John Openshaw and Greene, \{Casey S.\} and Ophir Shalem and Gary King and Ryan Probasco and Cheng, \{David R.\} and Ben Silbermann and Feng Zhang and Xihong Lin",

note = "Publisher Copyright: {\textcopyright} 2020, The Author(s), under exclusive licence to Springer Nature Limited.",

year = "2020",

month = sep,

day = "1",

doi = "10.1038/s41562-020-00944-2",

language = "英语",

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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 2020, 'Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing', Nature Human Behaviour, vol. 4, no. 9, pp. 972-982. https://doi.org/10.1038/s41562-020-00944-2

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AU - Allen, William E.

AU - Altae-Tran, Han

AU - Briggs, James

AU - Jin, Xin

AU - McGee, Glen

AU - Shi, Andy

AU - Raghavan, Rumya

AU - Kamariza, Mireille

AU - Nova, Nicole

AU - Pereta, Albert

AU - Danford, Chris

AU - Kamel, Amine

AU - Gothe, Patrik

AU - Milam, Evrhet

AU - Aurambault, Jean

AU - Primke, Thorben

AU - Li, Weijie

AU - Inkenbrandt, Josh

AU - Huynh, Tuan

AU - Chen, Evan

AU - Lee, Christina

AU - Croatto, Michael

AU - Bentley, Helen

AU - Lu, Wendy

AU - Murray, Robert

AU - Travassos, Mark

AU - Coull, Brent A.

AU - Openshaw, John

AU - Greene, Casey S.

AU - Shalem, Ophir

AU - King, Gary

AU - Probasco, Ryan

AU - Cheng, David R.

AU - Silbermann, Ben

AU - Zhang, Feng

AU - Lin, Xihong

PY - 2020/9/1

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AB - 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.

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DO - 10.1038/s41562-020-00944-2

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SP - 972

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JO - Nature Human Behaviour

JF - Nature Human Behaviour

IS - 9

ER -

Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing

Abstract

UN SDGs

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