I will describe a Bayesian approach, TransPhylo, and some of its recent extensions. TransPhylo reconstructs who infected whom and when, with the help of pathogen genetic data. It is a two-stage process, in which first, one or more timed phylogenetic trees are reconstructed from sequence data, and then these are augmented with transmission and timing information. As sequencing technologies have dramatically declined in cost, it is now feasible to sequence large numbers of viral or bacterial genomes in infectious disease outbreaks, and there have been high hopes that the resulting DNA or RNA sequences will tell the story of who infects whom and when, leading to both better infectious disease control and a better understanding of pathogen evolution. However, we find that having pathogen sequences does not directly reveal who infected whom – considerable uncertainty remains. I will outline our main approach and its underlying mathematics, and then I will describe several extensions to include multiple datasets and to handle covariates. I will give some applications and their results, describe the limitations of the method, and discuss open challenges in this area.
Watch here: https://www.youtube.com/watch?v=i5h_TX86kac