New Study: Can an Algorithm Predict COVID-19 Infections?

UCSF Osher Center faculty member Ashley Mason, PhD, has quickly launched a robust new study, TemPredict, which aims to understand whether an algorithm can predict the onset of symptoms, such as fever, cough, and fatigue, associated with COVID-19. Participants wear a sensor, the Oura Ring, that measures dermal body temperature, heart rate, and related metrics. The study, featured in the San Francisco Chronicle and on KQED, observes the associations between the recorded metrics and the emergence of symptoms. Participants include two groups: healthcare employees at UCSF and Zuckerberg San Francisco General Hospital and adult volunteers who personally own the wearable device used in the study. Data from the wearable device could provide early warning of a possible infection and thus allow participants to self-isolate early, an issue of critical importance for healthcare professionals who could unknowingly infect colleagues, patients, or others. TemPredict draws on Dr. Mason’s most recent study, now on hold due to the pandemic, which explores the connection between body temperature and depression.