This field has rapidly evolved since the beginning of the pandemic, with many analyses of these datasets focusing on COVID-19 diagnostics (i.e., symptoms, test results, medical background) 11, care-seeking 15, contact-tracing 16, patient care 17, effects on healthcare workers 18, hospital attendance 19, cancer 20, primary care 21, clinical symptoms 22, and triage 23. Studies in the US and Canada (CovidNearYou 10, 11), UK (Covid Symptom Study 12, 13, also in US) and Israel (PredictCorona 14), have reported large cohorts of users drawn from the general population with a goal towards capturing information about COVID-19 along a variety of dimensions, from symptoms to behavior, and have demonstrated some ability to detect and predict the spread of disease 12– 14. Since the start of the COVID-19 pandemic, several different applications have been launched throughout the world to collect COVID-19 symptoms, testing, and contact-tracing information 9. Previous studies, such as FluNearYou, have demonstrated the potential for using online surveys for disease surveillance 8. One approach to collecting this type of data on a population scale is to use web- and mobile-phone based surveys that enable large-scale collection of self-reported data. These data will allow medical professionals, public health officials, and policy makers to understand the effects of the pandemic on society, tailor intervention measures, efficiently allocate testing resources, and address disparities. In order to understand where and why the disease continues to spread, there is a pressing need for real-time individual-level data on COVID-19 infections and tests, as well as on the behavior, exposure, and demographics of individuals at the population scale with granular location information. Yet, in spite of widespread lockdowns and social distancing throughout the US, many states continue to exhibit steady increases in the number of cases 7. As a result, there is currently intense pressure to safely wind down these measures. The COVID-19 pandemic and ensuing response has produced a concurrent economic crisis of a scale not seen for nearly a century 6, exacerbating the effect of the pandemic on different socioeconomic groups and producing adverse health outcomes beyond COVID-19. ![]() In the United States, efforts to slow the spread of disease have included, to varying extents, social distancing, home-quarantine and treating infected patients, mandatory facial covering, closure of schools and non-essential businesses, and testing-trace-isolate measures 4, 5. The rapid global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the novel virus causing coronavirus disease 2019 (COVID-19) 1– 3, has created an unprecedented public health emergency. 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. 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. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19 positive individuals. Here we report results from over 500,000 users in the United States from Apto May 12, 2020. 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. ![]() Despite the widespread implementation of public health measures, COVID-19 continues to spread in the United States.
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