Phylodynamics and Markov Genealogy Processes

Explore a 56-minute lecture on phylodynamics and Markov genealogy processes presented by Aaron King from the University of Michigan at the Santa Fe Institute. Delve into the project of inferring determinants of disease transmission, progression, and immunity from genomic data, particularly focusing on genealogical and phylogenetic relationships among pathogen samples. Discover a unified approach to phylodynamics that extends existing full-information methods for parameterizing pathogen transmission models, offering exact expressions for likelihood calculations. Examine the concept of viewing genealogy as a dynamically evolving object rather than a static, retrospective account of ancestry. Learn how population-level processes induce genealogy-valued Markov processes and understand the derivation of a nonlinear filtering equation for inference from genomic data.