  Exploiting pathogen genomes to reconstruct transmission represents a powerful
tool in the fight against infectious disease. However, their interpretation
rests on a number of simplifying assumptions that regularly ignore important
complexities of real data, in particular within-host evolution and non-sampled
patients.
  Here we propose a new approach to transmission inference called SCOTTI
(Structured COalescent Transmission Tree Inference). This method is based on a
statistical framework that models each host as a distinct population, and
transmissions between hosts as migration events. Our computationally efficient
implementation of this model enables the inference of host-to-host transmission
while accommodating within-host evolution and non-sampled hosts. SCOTTI is
distributed as an open source package for the phylogenetic software BEAST2.
  We show that SCOTTI can generally infer transmission events even in the
presence of considerable within-host variation, can account for the uncertainty
associated with the possible presence of non-sampled hosts, and can efficiently
use data from multiple samples of the same host, although there is some
reduction in accuracy when samples are collected very close to the infection
time.
  We illustrate the features of our approach by investigating transmission from
genetic and epidemiological data in a Foot and Mouth Disease Virus (FMDV)
veterinary outbreak in England and a Klebsiella pneumoniae outbreak in a Nepali
neonatal unit. Transmission histories inferred with SCOTTI will be important in
devising effective measures to prevent and halt transmission.
