Applied & Computational Mathematics seminar: Optimal multiclass classification and prevalence estimation with applications to SARS-CoV-2 antibody assays

Speaker:  Rayanne Luke, NIST

Title:  Optimal multiclass classification and prevalence estimation with applications to SARS-CoV-2 antibody assays

Abstract:  Antibody tests are routinely used to identify past infection, with examples including Lyme disease and, of course, COVID-19. An accurate classification strategy is crucial to interpreting diagnostic test results and includes problems with more than two classes. Classification is further complicated when the relative fraction of the population in each class, or generalized prevalence, is unknown. In this talk, I will present a prevalence estimation method that is independent of classification and an associated classification scheme that minimizes false classifications. This work hinges on constructing probability models for data that are inputs to an optimal-decision theory framework. As an illustration, I will apply the method to antibody data with SARS-CoV-2 naïve, previously infected, and vaccinated classes. This is based on joint work with Paul Patrone and Anthony Kearsley.
 

Time:  Friday, September 9, 2022 - 10:00am-11:00am

Place:  Exploratory Hall, Room 4106 or  Zoom