Phylodynamics and Markov genealogy processes
Abstract: Phylodynamics is the project of inferring determinants of disease transmission, progression, and immunity from genomic data, in particular, from the genealogical or phylogenetic relationships among pathogen samples. I describe an approach to phylodynamics that unifies and extends existing full-information methods for parameterizing pathogen transmission models. While existing methods rely on approximations that are often violated in practice, our approach yields exact expressions for the likelihood. The key ingredient is to view a genealogy not as a static, retrospective account of ancestry but as dynamically evolving object. Specifically, we show how a given population-level process induces a genealogy-valued Markov process, and derive a nonlinear filtering equation that can be used as the basis for inference from genomic data.