Math Modeling Seminar: Improving SARS-CoV-2 diagnostic testing accuracy using higher dimensional probability models

Math Modeling Seminar
Improving SARS-CoV-2 diagnostic testing accuracy using higher dimensional probability models

Dr. Rayanne Luke
Postdoctoral Scholar
Johns Hopkins University / NIST

*Dr. Luke will be delivering this talk in person!

You may attend this lecture in person at 2300 Gosnell Hall or virtually via Zoom.
If you’d like to attend virtually, you may register here for Zoom link.

Abstract:
Antibody tests are routinely used to identify past infection, with examples including Lyme disease and, of course, COVID-19. However, accurate classification of samples as positive or negative is difficult when the corresponding measurement values overlap. In this talk, I will discuss a way to first separate positive and negative populations and then classify samples. We accomplish this by using more available measurements per person to build probabilistic models that capture structural characteristics of the data. As a bonus, viewing the data in higher dimensions allows us to hold out fewer samples in an indeterminate class.

Speaker Bio:
Dr. Rayanne Luke is a Postdoctoral Fellow jointly appointed at Johns Hopkins University and the National Institute of Standards and Technology (NIST). She received her B.S. in Applied Mathematics from SUNY Geneseo, where she used image analysis to reduce cancer-monitoring CT scan time while maintaining image quality. She then attended the University of Delaware, where she received her Ph.D. in Applied Mathematics. Her Ph.D. research studied models of the human tear film, which she fit to data from tear film videos to identify clinically relevant parameters that cannot otherwise be estimated. She teaches for Johns Hopkins and does research through NIST, where she works in collaboration with epidemiologists on applied diagnostics problems focused on COVID-19. She has been an active member of the Association for Women in Mathematics (AWM) and in her spare time enjoys being a “pretty serious” runner. Read more here.

Intended Audience:
Undergraduates, graduates, and experts. Those with interest in the topic.

The Math Modeling Seminar will recur each week throughout the semester on the same day and time. Find out more about upcoming speakers on the Mathematical Modeling Seminar Series webpage.
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