AI4Pandemics Talk #16: James Hay, Harvard University

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.