Passive Monitoring of Physiological Data and Self-reported Symptoms to Detect Clusters of People with COVID-19

Traditional screening for COVID-19 typically includes survey questions about symptoms, travel history, and sometimes temperature measurements. We explored whether longitudinal, personal sensor data can help identify subtle changes which may indicate an infection, such as COVID-19. To do this we developed an app that collects smartwatch and activity tracker data, as well as self-reported symptoms and diagnostic testing results from participants living in the US. We assessed whether symptoms and sensor data could differentiate COVID-19 positive versus negative cases in symptomatic individuals. Between March 25 and June 7, 2020, we enrolled 30,529 participants, of whom 3,811 reported symptoms, 54 reported testing positive for COVID-19, and 279 negative. We found that a combination of symptom and sensor data resulted in an AUC=0.80 [0.73 – 0.86] which was significantly better (p < 0.01) than a model which just considered symptoms alone (AUC=0.71 [0.63 – 0.79]) in the discrimination between symptomatic individuals positive or negative for COVID-19. Such orthogonal, continuous, passively captured data may be complementary to virus testing that is generally a one-off, or infrequent, sampling assay.