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Bayesian phylodynamic inference with complex models

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Title: Bayesian phylodynamic inference with complex models
Authors: Volz, EM
Siveroni, I
Item Type: Journal Article
Abstract: Population genetic modeling can enhance Bayesian phylogenetic inference by providing a realistic prior on the distribution of branch lengths and times of common ancestry. The parameters of a population genetic model may also have intrinsic importance, and simultaneous estimation of a phylogeny and model parameters has enabled phylodynamic inference of population growth rates, reproduction numbers, and effective population size through time. Phylodynamic inference based on pathogen genetic sequence data has emerged as useful supplement to epidemic surveillance, however commonly-used mechanistic models that are typically fitted to non-genetic surveillance data are rarely fitted to pathogen genetic data due to a dearth of software tools, and the theory required to conduct such inference has been developed only recently. We present a framework for coalescent-based phylogenetic and phylodynamic inference which enables highly-flexible modeling of demographic and epidemiological processes. This approach builds upon previous structured coalescent approaches and includes enhancements for computational speed, accuracy, and stability. A flexible markup language is described for translating parametric demographic or epidemiological models into a structured coalescent model enabling simultaneous estimation of demographic or epidemiological parameters and time-scaled phylogenies. We demonstrate the utility of these approaches by fitting compartmental epidemiological models to Ebola virus and Influenza A virus sequence data, demonstrating how important features of these epidemics, such as the reproduction number and epidemic curves, can be gleaned from genetic data. These approaches are provided as an open-source package PhyDyn for the BEAST2 phylogenetics platform.
Issue Date: 13-Nov-2018
Date of Acceptance: 5-Oct-2018
URI: http://hdl.handle.net/10044/1/65033
DOI: https://dx.doi.org/10.1371/journal.pcbi.1006546
ISSN: 1553-734X
Publisher: Public Library of Science (PLoS)
Journal / Book Title: PLoS Computational Biology
Volume: 14
Issue: 11
Copyright Statement: © 2018 Volz, Siveroni. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Keywords: 06 Biological Sciences
08 Information And Computing Sciences
01 Mathematical Sciences
Publication Status: Published
Conference Place: United States
Article Number: e1006546
Online Publication Date: 2018-11-13
Appears in Collections:School of Public Health