  Phylodynamics seeks to estimate effective population size fluctuations from
molecular sequences of individuals sampled from a population of interest. One
way to accomplish this task formulates an observed sequence data likelihood
exploiting a coalescent model for the sampled individuals' genealogy and then
integrating over all possible genealogies via Monte Carlo or, less efficiently,
by conditioning on one genealogy estimated from the sequence data. However,
when analyzing sequences sampled serially through time, current methods
implicitly assume either that sampling times are fixed deterministically by the
data collection protocol or that their distribution does not depend on the size
of the population. Through simulation, we first show that, when sampling times
do probabilistically depend on effective population size, estimation methods
may be systematically biased. To correct for this deficiency, we propose a new
model that explicitly accounts for preferential sampling by modeling the
sampling times as an inhomogeneous Poisson process dependent on effective
population size. We demonstrate that in the presence of preferential sampling
our new model not only reduces bias, but also improves estimation precision.
Finally, we compare the performance of the currently used phylodynamic methods
with our proposed model through clinically-relevant, seasonal human influenza
examples.
