Author:
Allen William E.,Altae-Tran Han,Briggs James,Jin Xin,McGee Glen,Shi Andy,Raghavan Rumya,Kamariza Mireille,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,Coull Brent A.,Openshaw John,Greene Casey S.,Shalem Ophir,King Gary,Probasco Ryan,Cheng David R.,Silbermann Ben,Zhang Feng,Lin Xihong
Abstract
Summary ParagraphDespite social distancing and shelter-in-place policies, COVID-19 continues to spread in the United States. A lack of timely information about factors influencing COVID-19 spread and testing has hampered agile responses to the pandemic. We developed How We Feel, an extensible web and mobile application that aggregates self-reported survey responses, to fill gaps in the collection of COVID-19-related data. How We Feel collects longitudinal and geographically localized information on users’ 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 of COVID-19 test results, which may aid in identification of likely COVID-19 positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation, as well as for household and community exposure, occupation, and demographics being strong risk factors for COVID-19. We further reveal factors for which users have been SARS-CoV-2 PCR tested, as well as the temporal dynamics of self-reported symptoms and self-isolation behavior in positive and negative users. These results highlight the utility of collecting a diverse set of symptomatic, demographic, and behavioral self-reported data to fight the COVID-19 pandemic.
Publisher
Cold Spring Harbor Laboratory