AI Model Proactively Predicts if a COVID-19 Test Might be Positive or Not

Researchers trained five classification algorithms to create an accurate model to predict COVID-19 test results. Results identify the key symptom features associated with COVID-19 infection and provide a way for rapid screening and cost effective infection detection. Findings reveal that number of days experiencing symptoms such as fever and difficulty breathing play a large role in COVID-19 test results. Findings also show that molecular tests have much narrower post-symptom onset days compared to post-symptom onset days of serology tests. As a result, the molecular test has the lowest positive rate because it measures current infection.

Surveillance study finds disparities, high proportion of past COVID-19 infections among adults and children in Santa Ana

In a large-scale, population-based surveillance conducted in partnership with the City of Santa Ana, researchers at the University of California, Irvine’s Program in Public Health found 27% positivity of SARS-CoV-2 antibodies among participating Santa Ana residents. This unique study was one of the first to examine household transmission of COVID-19 and to include a pediatric population (ages 5+).