Markov genealogy processes: a new mathematical basis for phylodynamics
This Biostatistics seminar will feature Aaron A. King, professor of ecology, evolutionary biology, and complex systems at the University of Michigan.
About
Aaron A. King is the Nelson G. Hairston professor of ecology, evolutionary biology, and complex systems at the University of Michigan, as well as external professor with the Santa Fe Institute.
Abstract
Phylodynamics requires constructing a bridge between dynamic models and genome data. One route for this bridge lies through the genealogy that describes the patterns of shared ancestry among sampled genomes. A key problem in phylodynamics has been a mismatch between inference methodology and epidemiological models: the approximations that must be made to perform inference conflict with questions of great interest. I will describe new results in which we have obtained exact expressions for phylodynamic likelihoods associated with population models of (almost) arbitrary complexity. These results unify and strictly extend existing approaches and broaden the scope of phylodynamic inference methods. In particular, I will deduce an exact expression for the likelihood of an observed genealogy, as the solution to a well-defined filter equation. The most widely used existing approaches to phylodynamics are seen to be very special cases of these equations. Interestingly, the equations can be solved numerically using standard Monte Carlo techniques. I will conclude by highlighting the need for improved algorithms and indicating some open questions.
The Biostatistics seminar series invites researchers from across the nation to discuss methodological research and its implications for a variety of health issues.
Contact
Andy Ni