John Barton, PhD
Associate Professor at University of Pittsburgh
The CEIRR CMC holds bi-monthly research seminars that feature leading researchers who develop state-of-the-art computational methods to better understand respiratory viruses. These events are designed to facilitate in-depth discussions and provide ample opportunity for questions from the audience, with each seminar lasting 1 hour and 30 minutes.
Title:
Inferring predictive models of viral dynamics from genomic surveillance data
Abstract:
Understanding the predictability of evolution is a classic problem in biology, with particular relevance for rapidly evolving pathogens like influenza A virus (IAV). Here, evolutionary forecasting could affect vaccine effectiveness. IAV vaccines must be designed to hit a “moving target,” as the virus evolves to escape human immune responses. We developed a novel approach to this problem by combining mathematical methods from statistical physics with epidemiological modeling. In this talk, I’ll describe our approach to estimating the fitness effects of mutations from genomic surveillance data, and how we can use these estimates to forecast future evolution. I’ll also discuss how we’ve applied this approach to model hemagglutinin and neuraminidase evolution in H3N2 and H1N1 subtypes of IAV from 2009 until the beginning of the SARS-CoV-2 pandemic in 2020. Despite a shifting immune environment, we can generate successful predictions for both the composition of the viral population in the near future and the fixation or loss of individual mutations.
After you register, click on the Eventbrite event page for a zoom link.
Note: CEIRR CMC events are open only to CEIRR participants. The meeting and discussion are confidential.