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. These data are usually reported as a binary result, thereby removing much of the information inherent in the 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. I will present three projects that combine mathematical models and an understanding of viral kinetics to generate insights into surveillance testing, efficient sample pooling and reconstruction of epidemic dynamics.

Bio: I completed my PhD with Steven Riley in 2019 at the Department of Infectious Disease Epidemiology, Imperial College London, where I developed methods to infer antibody kinetics, infection histories and epidemic dynamics using serological data. I have since been a postdoc in CCCD at Harvard SPH working with Michael Mina on COVID -19 testing (though I was meant to be using epitope-level serological data to study population immunology).