Title: Using viral loads to improve virologic surveillance

YouTube Recording

Abstract: Virologic testing has been central to tracking the COVID-19 pandemic. Most routine tests provide a quantitative result in the form of a cycle threshold (Ct) value -- a metric proportional to the log viral load of the sample. These data are usually reported as a binary result, thereby removing much of the information inherent in their full quantitative value. We propose that, despite their caveats and variability, the Ct value is a useful measure that can be harnessed to improve public health surveillance. In this talk, I will explain why the distribution viral loads changes predictably during the course of an epidemic, and how this relationship can be used to estimate an epidemic's trajectory from a single cross-sectional sample of RT-qPCR data. I will also discuss the implications of these findings for comparing viral loads between emerging variants, demonstrating surveillance scenarios where variant-specific viral kinetics can and cannot be reliably inferred.

About AI4PAN Artificial Intelligence for Pandemics Seminar Series centred at UQ

Welcome to AI4PAN, the Artificial Intelligence for Pandemics group centered at The University of Queensland (UQ). The group's focus is the application of data science, machine learning, statistical learning, applied mathematics, computation, and other "artificial intelligence" techniques for managing pandemics both at the epidemic and clinical level.