Matthew Wascher, “Monitoring disease prevalence and transmission in a population under repeated testing”

When: Mar 2nd, 14:00-15:00
Where: Tyler 053, Zoom link:
Abstract: In this talk, I will describe a statistical methodology developed as part of the COVID-19 monitoring efforts of The Ohio State University (OSU) and which is designed for monitoring disease transmission using repeated testing data. Under a repeated testing scheme in which individuals who test positive are subsequently isolated, naive estimation techniques may produce biased estimates of disease prevalence. Our method avoids this bias by viewing the repeated testing data as an interval censoring of infection times. The other important novelty of our method is that it allows for changes in transmission dynamics and human behavior through changes in the model parameters without rendering the model computationally prohibitive. To illustrate the usefulness of our methodology, we apply it to both synthetic data and real SARS-CoV-2 testing data collected at the OSU Columbus campus. This is joint work with Patrick Schnell, Wasiur Khudabukhsh, Greg Rempala, and Joe Tien.