  Whole genome sequencing of pathogens from multiple hosts in an epidemic
offers the potential to investigate who infected whom with unparalleled
resolution, potentially yielding important insights into disease dynamics and
the impact of control measures. We considered disease outbreaks in a setting
with dense genomic sampling, and formulated stochastic epidemic models to
investigate person-to-person transmission, based on observed genomic and
epidemiological data. We constructed models in which the genetic distance
between sampled genotypes depends on the epidemiological relationship between
the hosts. A data augmented Markov chain Monte Carlo algorithm was used to
sample over the transmission trees, providing a posterior probability for any
given transmission route. We investigated the predictive performance of our
methodology using simulated data, demonstrating high sensitivity and
specificity, particularly for rapidly mutating pathogens with low
transmissibility. We then analyzed data collected during an outbreak of
methicillin-resistant Staphylococcus aureus in a hospital, identifying probable
transmission routes and estimating epidemiological parameters. Our approach
overcomes limitations of previous methods, providing a framework with the
flexibility to allow for unobserved infection times, multiple independent
introductions of the pathogen, and within-host genetic diversity, as well as
allowing forward simulation.
