Bayesian phylodynamic inference with complex models.
Erik M. Volz,Igor Siveroni +1 more
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TLDR
A framework for coalescent-based phylogenetic and phylodynamic inference which enables highly-flexible modeling of demographic and epidemiological processes is presented and 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.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.read more
Citations
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Journal ArticleDOI
BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis.
Remco R. Bouckaert,Remco R. Bouckaert,Timothy G. Vaughan,Timothy G. Vaughan,Joëlle Barido-Sottani,Joëlle Barido-Sottani,Sebastián Duchêne,Mathieu Fourment,Alexandra Gavryushkina,Joseph Heled,Graham Jones,Denise Kühnert,Nicola De Maio,Michael Matschiner,Fábio K. Mendes,Nicola F. Müller,Nicola F. Müller,Huw A. Ogilvie,Louis du Plessis,Alex Popinga,Andrew Rambaut,David A. Rasmussen,Igor Siveroni,Marc A. Suchard,Chieh-Hsi Wu,Dong Xie,Chi Zhang,Tanja Stadler,Tanja Stadler,Alexei J. Drummond +29 more
TL;DR: A series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release are described.
Journal ArticleDOI
Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity.
Erik M. Volz,Verity Hill,John T. McCrone,Anna Price,David Jorgensen,Áine O'Toole,Joel Southgate,Robert Johnson,Ben Jackson,Fabrícia F. Nascimento,Sara Rey,Samuel M. Nicholls,Rachel M. Colquhoun,Ana da Silva Filipe,James G Shepherd,David J Pascall,Rajiv Shah,Natasha Jesudason,Kathy Li,Ruth F. Jarrett,Nicole Pacchiarini,Matthew J. Bull,Lily Geidelberg,Igor Siveroni,Ian Goodfellow,Nicholas J. Loman,Oliver G. Pybus,David Robertson,E. Thomson,Andrew Rambaut,Thomas R. Connor +30 more
TL;DR: Investigation of the hypothesis for positive selection of Spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage.
Posted ContentDOI
Evaluating the effects of SARS-CoV-2 Spike mutation D614G on transmissibility and pathogenicity
Erik M. Volz,Verity Hill,John T. McCrone,Anna Price,David Jorgensen,Áine O'Toole,Joel Southgate,Robert Johnson,Ben Jackson,Fabrícia F. Nascimento,Sara Rey,Samuel M. Nicholls,Rachel M. Colquhoun,Ana da Silva Filipe,James G Shepherd,David J Pascall,Rajiv Shah,Natasha Jesudason,Kathy Li,Ruth F. Jarrett,Nicole Pacchiarini,Matthew J. Bull,Lily Geidelberg,Igor Siveroni,Ian Goodfellow,Nicholas J. Loman,Oliver G. Pybus,Oliver G. Pybus,David Robertson,E. Thomson,Andrew Rambaut,Thomas R. Connor,Thomas R. Connor +32 more
TL;DR: Investigation of the hypothesis for positive selection of Spike 614G at the level of an individual country, the United Kingdom, using more than 25,000 whole genome SARS-CoV-2 sequences collected by COVID-19 Genomics UK Consortium finds that Spike 6 14G clusters are introduced in the UK later on average than 614D clusters and grow to larger size after adjusting for time of introduction.
Posted ContentDOI
BEAST 2.5: An Advanced Software Platform for Bayesian Evolutionary Analysis
Remco R. Bouckaert,Timothy G. Vaughan,Joëlle Barido-Sottani,Sebastián Duchêne,Mathieu Fourment,Alexandra Gavryushkina,Joseph Heled,Graham Jones,Denise Kühnert,Nicola De Maio,Michael Matschiner,Fábio K. Mendes,Nicola F. Müller,Huw A. Ogilvie,Louis du Plessis,Alex Popinga,Andrew Rambaut,David A. Rasmussen,Igor Siveroni,Marc A. Suchard,Chieh-Hsi Wu,Dong Xie,Chi Zhang,Tanja Stadler,Alexei J. Drummond +24 more
TL;DR: The full range of new tools and models available on the BEAST 2.5 platform are described, which expand joint evolutionary inference in many new directions, especially for joint inference over multiple data types, non-tree models and complex phylodynamics.
Journal ArticleDOI
Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel.
Danielle Miller,Michael A. Martin,Noam Harel,Omer Tirosh,Talia Kustin,Moran Meir,Nadav Sorek,Shiraz Gefen-Halevi,Sharon Amit,Olesya Vorontsov,Avraham Shaag,Dana G. Wolf,Avi Peretz,Yonat Shemer-Avni,Diana Roif-Kaminsky,Naama M. Kopelman,Amit Huppert,Amit Huppert,Katia Koelle,Adi Stern +19 more
TL;DR: This work sequences 212 SARS-CoV-2 sequences and uses them to perform a comprehensive analysis to trace the origins and spread of the virus and finds that travelers returning from the United States of America significantly contributed to viral spread in Israel.
References
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Journal ArticleDOI
Bayesian Phylogenetics with BEAUti and the BEAST 1.7
TL;DR: The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7 is presented, which implements a family of Markov chain Monte Carlo algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography and related molecular evolutionary analyses.
Book
Infectious Diseases of Humans: Dynamics and Control
Roy M. Anderson,Robert M. May +1 more
TL;DR: This book discusses the biology of host-microparasite associations, dynamics of acquired immunity heterogeneity within the human community indirectly transmitted helminths, and the ecology and genetics of hosts and parasites.
Journal ArticleDOI
Bayesian Coalescent Inference of Past Population Dynamics from Molecular Sequences
TL;DR: The Bayesian skyline plot is introduced, a new method for estimating past population dynamics through time from a sample of molecular sequences without dependence on a prespecified parametric model of demographic history, and a Markov chain Monte Carlo sampling procedure is described that efficiently samples a variant of the generalized skyline plot, given sequence data.
Journal ArticleDOI
Bayesian phylogeography finds its roots.
TL;DR: It is concluded that the Bayesian phylogeographic framework will make an important asset in molecular epidemiology that can be easily generalized to infer biogeogeography from genetic data for many organisms.
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