W. Allen, H. Altae-Tran, J. Briggs, Xin Jin, G. McGee, A. Shi, R. Raghavan, M. Kamariza, N. Nova, A. Pereta, C. Danford, A. Kamel, P. Gothe, E. Milam, J. Aurambault, T. Primke, Weijie Li, J. Inkenbrandt, T. Huynh, E. Chen, C. Lee, M. Croatto, H. Bentley, Wendy Lu, R. Murray, M. Travassos, B. Coull, J. Openshaw, C. Greene, O. Shalem, G. King, R. Probasco, D. Cheng, B. Silbermann, F. Zhang, Xihong Lin
Nature Human Behaviour, 2020
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. How We Feel is a web and mobile-phone application for collecting de-identified self-reported COVID-19-related data. These data are used to map a diverse set of symptomatic, demographic, exposure and behavioural factors relevant to the ongoing pandemic.
Cited by 29 publications.
Field of study: Medicine