Imperial College London
Alternate

DrErikVolz

Faculty of Medicine, School of Public Health

Reader in Population Biology of Infectious Diseases
 
 
 
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Contact

 

+44 (0)20 7594 1933e.volz Website

 
 
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Location

 

UG10Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Volz:2018:10.1371/journal.pcbi.1006546,
author = {Volz, EM and Siveroni, I},
doi = {10.1371/journal.pcbi.1006546},
journal = {PLoS Computational Biology},
title = {Bayesian phylodynamic inference with complex models},
url = {http://dx.doi.org/10.1371/journal.pcbi.1006546},
volume = {14},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Volz,EM
AU  - Siveroni,I
DO  - 10.1371/journal.pcbi.1006546
PY  - 2018///
SN  - 1553-734X
TI  - Bayesian phylodynamic inference with complex models
T2  - PLoS Computational Biology
UR  - http://dx.doi.org/10.1371/journal.pcbi.1006546
UR  - https://www.ncbi.nlm.nih.gov/pubmed/30422979
UR  - http://hdl.handle.net/10044/1/65033
VL  - 14
ER  -
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