Bayesian phylodynamic inference with complex models

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 BEAST phylogenetics platform. Introduction 1 Mechanistic models guided by expert knowledge can form an efficient prior on epidemic 2 history when conducting phylodynamic inference with genetic data [1]. Parameters 3 estimated by fitting mechanistic models, such as the reproduction number R 0 , are 4 important for epidemic surveillance and forecasting. Compartmental models defined in 5 terms of ordinary or stochastic differential equations are the most common type of 6 mathematical infectious disease model, but in the area of phylodynamic inference, 7 non-parametric approaches based on skyline coalescent models [2] or 8 sampling-birth-death models [3] are more commonly used. Methods to translate 9compartmental infectious disease models into a population genetic framework have been 10 developed only recently [4][5][6][7][8]. We address the gap in software tools for epidemic 11 modeling and phylogenetic inference by developing a BEAST package, PhyDyn, which 12 includes a highly-flexible mark-up language for defining compartmental infectious 13 1/17 disease models in terms of ordinary differential equations. This flexible framework 14 enables phylodynamic inference with the majority of published compartmental models, 15 such as the common susceptible-infected-removed (SIR) model [9] and its variants, ...

Comparison of cluster-based and source-attribution methods for estimating transmission risk using large HIV sequence databases

“…The algorithm for calculating the matrix of infector probabilities in this study is implemented in function phylo.source.attribution.hiv.msm of the R package phydynR [18].…”

“…We simulated coalescent trees by conditioning on HIV epidemic histories using the approach described by Volz, 2012 [30]. This method is implemented in the phydynR R package [18]. The simulated tree genealogy assumes that each infected patient corresponds to a single lineage of virus HIV-1 [31], ignoring super-infection, and that the time at which two lineages coalesce corresponds to a transmission event.…”

Evaluating whole HIV-1 genome sequence for estimation of incidence and migration in a rural South African community

“…We carried out fixed-tree phylodynamic analyses using dated phylogenies estimated by ML and treedater . This analysis was carried out with the BayesianTools R package version 0.1.6 ( Hartig et al , 2018 ) which implements Bayesian Markov chain Monte Carlo (MCMC) methods and the phydynR R package version 0.2.0 ( Volz, 2017 ) which implements the structured coalescent likelihood.…”