  Coalescent theory combined with statistical modeling allows us to estimate
effective population size fluctuations from molecular sequences of individuals
sampled from a population of interest. When sequences are sampled serially
through time and the distribution of the sampling times depends on the
effective population size, explicit statistical modeling of sampling times
improves population size estimation. Previous work assumed that the genealogy
relating sampled sequences is known and modeled sampling times as an
inhomogeneous Poisson process with log-intensity equal to a linear function of
the log-transformed effective population size. We improve this approach in two
ways. First, we extend the method to allow for joint Bayesian estimation of the
genealogy, effective population size trajectory, and other model parameters.
Next, we improve the sampling time model by incorporating additional sources of
information in the form of time-varying covariates. We validate our new
modeling framework using a simulation study and apply our new methodology to
analyses of population dynamics of seasonal influenza and to the recent Ebola
virus outbreak in West Africa.
