  Phylogeographic methods aim to infer migration trends and the history of
sampled lineages from genetic data. Applications of phylogeography are broad,
and in the context of pathogens include the reconstruction of transmission
histories and the origin and emergence of outbreaks. Phylogeographic inference
based on bottom-up population genetics models is computationally expensive, and
as a result faster alternatives based on the evolution of discrete traits have
become popular. In this paper, we show that inference of migration rates and
root locations based on discrete trait models is extremely unreliable and
sensitive to biased sampling. To address this problem, we introduce BASTA
(BAyesian STructured coalescent Approximation), a new approach implemented in
BEAST2 that combines the accuracy of methods based on the structured coalescent
with the computational efficiency required to handle more than just few
populations. We illustrate the potentially severe implications of poor model
choice for phylogeographic analyses by investigating the zoonotic transmission
of Ebola virus. Whereas the structured coalescent analysis correctly infers
that successive human Ebola outbreaks have been seeded by a large unsampled
non-human reservoir population, the discrete trait analysis implausibly
concludes that undetected human-to-human transmission has allowed the virus to
persist over the past four decades. As genomics takes on an increasingly
prominent role informing the control and prevention of infectious diseases, it
will be vital that phylogeographic inference provides robust insights into
transmission history.
