Phylodynamic Model Adequacy Using Posterior Predictive Simulations.


Journal

Systematic biology
ISSN: 1076-836X
Titre abrégé: Syst Biol
Pays: England
ID NLM: 9302532

Informations de publication

Date de publication:
01 03 2019
Historique:
received: 25 02 2018
accepted: 15 06 2018
pubmed: 27 6 2018
medline: 9 4 2019
entrez: 27 6 2018
Statut: ppublish

Résumé

Rapidly evolving pathogens, such as viruses and bacteria, accumulate genetic change at a similar timescale over which their epidemiological processes occur, such that, it is possible to make inferences about their infectious spread using phylogenetic time-trees. For this purpose it is necessary to choose a phylodynamic model. However, the resulting inferences are contingent on whether the model adequately describes key features of the data. Model adequacy methods allow formal rejection of a model if it cannot generate the main features of the data. We present TreeModelAdequacy, a package for the popular BEAST2 software that allows assessing the adequacy of phylodynamic models. We illustrate its utility by analyzing phylogenetic trees from two viral outbreaks of Ebola and H1N1 influenza. The main features of the Ebola data were adequately described by the coalescent exponential-growth model, whereas the H1N1 influenza data were best described by the birth-death susceptible-infected-recovered model.

Identifiants

pubmed: 29945220
pii: 5044211
doi: 10.1093/sysbio/syy048
pmc: PMC6368481
doi:

Banques de données

Dryad
['10.5061/dryad.65p331m']

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

358-364

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Auteurs

Sebastian Duchene (S)

Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Melbourne, Australia.

Remco Bouckaert (R)

Centre for Computational Evolution, University of Auckland, Auckland, New Zealand.
Max Planck Institute for the Science of Human History, Jena, Germany.

David A Duchene (DA)

School of Life and Environmental Sciences, University of Sydney, Sydney, Australia.

Tanja Stadler (T)

Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Swiss Institute of Bioinformatics, Lausanne, Switzerland.

Alexei J Drummond (AJ)

Centre for Computational Evolution, University of Auckland, Auckland, New Zealand.

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Classifications MeSH