A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation
2018
Bacterial populations that colonize a host can 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. With the use of whole genome sequencing to determine genetic
distance between isolates, a common solution to estimate acquisition and clearance rates
has been to assume a fixed genetic distance threshold below which isolates are
considered to represent the same strain. 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 present a fully Bayesian model that provides
probabilities of whether two strains should be considered the same, allowing us to
determine bacterial clearance and acquisition from genomes sampled over time. Our
method explicitly models the within-host variation using population genetic simulation,
and the inference is done using a combination of Approximate Bayesian Computation
(ABC) and Markov Chain Monte Carlo (MCMC). We validate the method with
multiple carefully conducted simulations and demonstrate its use in practice by
analyzing a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates
from a large recently completed longitudinal clinical study. An R-code implementation
of the method is freely available at:
https://github.com/mjarvenpaa/bacterial-colonization-model.git.
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