  Bacterial populations that colonize a host play important roles in host
health, including serving as a reservoir that transmits to other hosts and from
which invasive strains emerge, thus emphasizing the importance of understanding
rates of acquisition and clearance of colonizing populations. Studies of
colonization dynamics have been based on assessment of whether serial samples
represent a single population or distinct colonization events. A common
solution to estimate acquisition and clearance rates is to use a fixed genetic
distance threshold. However, this approach is often inadequate to account for
the diversity of the underlying within-host evolving population, the time
intervals between consecutive measurements, and the uncertainty in the
estimated acquisition and clearance rates. Here, we summarize recently
submitted work \cite{jarvenpaa2018named} and present a Bayesian model that
provides probabilities of whether two strains should be considered the same,
allowing to determine bacterial clearance and acquisition from genomes sampled
over time. We explicitly model the within-host variation using population
genetic simulation, and the inference is done by combining information from
multiple data sources by using a combination of Approximate Bayesian
Computation (ABC) and Markov Chain Monte Carlo (MCMC). We use the method to
analyse a collection of methicillin resistant Staphylococcus aureus (MRSA)
isolates.
