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.

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Despite 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.

Acute kidney injury (AKI) is common and associated with significant morbidity and mortality. Recovery from many forms of AKI involves the proliferation of renal proximal tubular epithelial cells (RPTECs), but the influence of the microenvironment in which this recovery occurs remains poorly understood. Here we report the development of a poly(ethylene glycol) (PEG) hydrogel platform to study the influence of substrate mechanical properties on the proliferation of human RPTECs as a model for recovery from AKI. PEG diacrylate based hydrogels were generated with orthogonal control of mechanics and cell-substrate interactions. Using this platform, we found that increased substrate stiffness promotes RPTEC spreading and proliferation. RPTECs showed similar degrees of apoptosis and Yes-associated protein (YAP) nuclear localization regardless of stiffness, suggesting these were not key mediators of the effect. However, focal adhesion formation, cytoskeletal organization, focal adhesion kinase (FAK) activation, and extracellular signal-regulated kinase (ERK) activation were all enhanced with increasing substrate stiffness. Inhibition of ERK activation substantially attenuated the effect of stiffness on proliferation. In long-term culture, hydrogel stiffness promoted the formation of more complete epithelial monolayers with tight junctions, cell polarity, and an organized basement membrane. These data suggest that increased stiffness potentially may have beneficial consequences for the renal tubular epithelium during recovery from AKI.