Applied Interdisciplinary Mathematics (AIM) Seminar

Phylodynamics seeks to extract information on the transmission and evolution of a pathogen from genetic sequences. The problem boils down to that of computing the likelihood function that links a dynamical system model to the genetic data. The two approaches to the problem currently in use view the pathogen genealogy as a static, historical record of the generating process and rely on paradoxical assumptions and approximations. By viewing the generative model instead as a genealogy-valued Markov process, we show that one can derive exact expressions for the likelihood and develop efficient Monte Carlo algorithms for its calculation. Speaker(s): Aaron King (University of Michigan)