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

Nat Hum Behav. 2020 Sep;4(9):972-982. doi: 10.1038/s41562-020-00944-2. Epub 2020 Aug 26.

Authors

William E Allen^#^{1

2

3}, Han Altae-Tran^#^{4

5

6}, James Briggs^#^{4

5

7}, Xin Jin^#^{4

8

5}, Glen McGee^#^{4

9}, Andy Shi^#^{4

9}, Rumya Raghavan^{4

5

10}, Mireille Kamariza^{4

8

5}, Nicole Nova^{4

11}, Albert Pereta⁴, Chris Danford⁴, Amine Kamel⁴, Patrik Gothe⁴, Evrhet Milam⁴, Jean Aurambault⁴, Thorben Primke⁴, Weijie Li⁴, Josh Inkenbrandt⁴, Tuan Huynh⁴, Evan Chen⁴, Christina Lee⁴, Michael Croatto⁴, Helen Bentley⁴, Wendy Lu⁴, Robert Murray⁴, Mark Travassos^{4

12}, Brent A Coull⁹, John Openshaw^{4

13}, Casey S Greene^{4

14}, Ophir Shalem^{4

15}, Gary King^{4

16}, Ryan Probasco⁴, David R Cheng⁴, Ben Silbermann⁴, Feng Zhang^{17

18

19

20

21

22}, Xihong Lin^{23

24

25

26}

Affiliations

¹ The How We Feel Project, San Leandro, CA, USA. weallen@fas.harvard.edu.
² Society of Fellows, Harvard University, Cambridge, MA, USA. weallen@fas.harvard.edu.
³ Broad Institute of MIT and Harvard, Cambridge, MA, USA. weallen@fas.harvard.edu.
⁴ The How We Feel Project, San Leandro, CA, USA.
⁵ Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁶ Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁷ Schmidt Science Fellows, Oxford, UK.
⁸ Society of Fellows, Harvard University, Cambridge, MA, USA.
⁹ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
¹⁰ Health Sciences and Technology Program, Massachusetts Institute of Technology and Harvard Medical School, Cambridge, MA, USA.
¹¹ Department of Biology, Stanford University, Stanford, CA, USA.
¹² Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD, USA.
¹³ Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
¹⁴ Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
¹⁵ Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
¹⁶ Albert J. Weatherhead III University Professor, Institute for Quantitative Social Sciences, Harvard University, Cambridge, MA, USA.
¹⁷ The How We Feel Project, San Leandro, CA, USA. zhang_f@mit.edu.
¹⁸ Broad Institute of MIT and Harvard, Cambridge, MA, USA. zhang_f@mit.edu.
¹⁹ Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. zhang_f@mit.edu.
²⁰ McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA. zhang_f@mit.edu.
²¹ Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA. zhang_f@mit.edu.
²² Howard Hughes Medical Institute, Chevy Chase, MD, USA. zhang_f@mit.edu.
²³ The How We Feel Project, San Leandro, CA, USA. xlin@hsph.harvard.edu.
²⁴ Broad Institute of MIT and Harvard, Cambridge, MA, USA. xlin@hsph.harvard.edu.
²⁵ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. xlin@hsph.harvard.edu.
²⁶ Department of Statistics, Harvard University, Cambridge, MA, USA. xlin@hsph.harvard.edu.

^# Contributed equally.

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.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Adult
Asymptomatic Diseases / epidemiology
Betacoronavirus*
COVID-19
COVID-19 Testing
Clinical Laboratory Techniques / statistics & numerical data*
Coronavirus Infections / diagnosis
Coronavirus Infections / epidemiology*
Coronavirus Infections / prevention & control
Coronavirus Infections / psychology
Female
Humans
Longitudinal Studies
Male
Mobile Applications
Models, Statistical
Pandemics / prevention & control
Pandemics / statistics & numerical data
Pneumonia, Viral / diagnosis
Pneumonia, Viral / epidemiology*
Pneumonia, Viral / prevention & control
Pneumonia, Viral / psychology
SARS-CoV-2
United States / epidemiology

Abstract

Publication types

MeSH terms

Grants and funding