Population-scale Longitudinal Mapping of COVID-19 Symptoms, Behaviour, and Testing

Abstract

Despite the widespread implementation of public health measures, 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, 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 to identify likely COVID-19 positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation, show a variety of exposure, occupation, 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 behavior. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure, and behavioral self-reported data to fight the COVID-19 pandemic.