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.
【초록키워드】 COVID-19, coronavirus disease, pandemic, COVID-19 pandemic, Symptom, risk factor, Spread, Health, Predictive model, Asymptomatic, symptomatic, response, implementation, presymptomatic, behavioural, temporal dynamics, utility, Evidence, SARS-CoV-2 PCR, demographics, Self-isolation, Factor, public health measures, The United States, widespread, highlight, identify, the United State, facilitate, can be used, variety, individuals, build, 【제목키워드】 COVID-19 symptoms,