Refining COVID-19 retrospective diagnosis with continuous serological tests: a Bayesian mixture model
Benjamin Glemain,
Xavier de Lamballerie,
Marie Zins
et al.
Abstract:COVID-19 serological tests with a "positive", "intermediate" or "negative" result according to predefined thresholds cannot be directly interpreted as a probability of having been infected with SARS-CoV-2. Based on 81,797 continuous anti-spike tests collected in France after the first wave, a Bayesian mixture model was developed to provide a tailored infection probability for each participant. Depending on the serological value and the context (age and administrative region), a negative or a positive test coul… Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.