Abstract

Abstract: Serosurveys are a key resource for measuring the proportion of the population that has been infected by SARS -CoV-2. However, reported assay performance characteristics are usually only applicable to early convalescent samples from hospitalized patients with severe disease, whereas accurate estimation of population seroprevalence relies on adequate characterization of assay sensitivity to detect prior infections in the general population. In this talk, I’ll present our recent work on characterizing the heterogeneity in antibody responses between assays, disease severity, and over time among participants in a post-infection cohort, as well as on developing a framework to incorporate these heterogeneities into more accurate seroprevalence estimation. I’ll also discuss a platform that our group has piloted to test remnant samples from two hospital networks in San Francisco for SARS -CoV-2 antibodies, which could be leveraged for broader application beyond this local context and for other pathogens. Bio: I am a postdoctoral scholar and a Schmidt Science Fellow at the EPP Icenter at UCSF . I’m broadly interested in understanding the interactions between pathogens and the host immune response across scales. In my post-doc, I’ve been combining high-throughput serological data with models to study how individuals acquire clinical immunity to malaria, and using serosurveillance to understand the magnitude of the SARS -CoV-2 pandemic. I completed my PhD in 2019 at Princeton University with Jessica Metcalf and Bryan Grenfell.