Abstract
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.
Highlights
IntroductionPhylodynamic inference—the study and estimation of population dynamics from genetic sequences—relies upon data sampled in a timeframe compatible with the evolutionary dynamics under question [1]
One indirect way of assessing population size changes is to take a sample of individuals from the population of interest and analyze genetic sequences from these individuals
Phylodynamic inference—the study and estimation of population dynamics from genetic sequences—relies upon data sampled in a timeframe compatible with the evolutionary dynamics under question [1]
Summary
Phylodynamic inference—the study and estimation of population dynamics from genetic sequences—relies upon data sampled in a timeframe compatible with the evolutionary dynamics under question [1]. One subtle and often ignored complication of phylodynamic inference occurs when there is a probabilistic dependence between the effective population trajectory and the temporal frequency of collecting data samples, such as in case of sampling infectious disease agent genetic sequences with increasing urgency and intensity during a rising epidemic. This issue of preferential sampling was studied in depth by Karcher et al in the limited context of a known, fixed genealogy reconstructed from the genetic data [4]. We propose a more flexible model for sequence sampling times that allows for inclusion of arbitrary time-dependent covariates and their interactions with the effective population size
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
