Viral phylodynamics and genealogy-valued Markov processes
When
Phylodynamics is the project of extracting information from genome sequences to inform models of the biological processes that generate them. In the context of infectious disease, one seeks to parameterize models of pathogen transmission using genealogies reconstructed from virus genomes. In this talk, I show how the problem is naturally formulated in terms of a class of Markov processes on a space of genealogies. For interesting transmission models, the exact likelihood is intractable, but I show how to construct an efficient sequential Monte Carlo algorithm to estimate it with high accuracy.
Aaron A. King is the Nelson G. Hairston Collegiate Professor of Ecology, Evolutionary Biology, and Complex Systems at the University of Michigan. An applied mathematician and a biologist, he investigates problems in the population biology of infectious diseases using mathematical models. He is particularly interested in how nonlinearity, seasonality, and stochasticity interact to shape ecological dynamics and in the development and application of powerful model-based methods for inferring the structure and properties of ecological systems from observational data.
Place: Zoom: https://asu.zoom.us/j/85049043960