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dc.contributor.author
Duchene, Sebastian
dc.contributor.author
Bouckaert, Remco
dc.contributor.author
Duchene, David A.
dc.contributor.author
Stadler, Tanja
dc.contributor.author
Drummond, Alexei J.
dc.date.accessioned
2019-02-22T14:24:40Z
dc.date.available
2019-02-21T06:23:12Z
dc.date.available
2019-02-22T14:24:40Z
dc.date.issued
2019-03-01
dc.identifier.other
10.1093/sysbio/syy048
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/326544
dc.identifier.doi
10.3929/ethz-b-000326544
dc.description.abstract
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.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Oxford University Press
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Phylodynamic Model Adequacy Using Posterior Predictive Simulations
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2018-06-25
ethz.journal.title
Systematic Biology
ethz.journal.volume
68
en_US
ethz.journal.issue
2
en_US
ethz.pages.start
358
en_US
ethz.pages.end
364
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
New phylogenetic methods for inferring complex population dynamics
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::09490 - Stadler, Tanja / Stadler, Tanja
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02060 - Dep. Biosysteme / Dep. of Biosystems Science and Eng.::09490 - Stadler, Tanja / Stadler, Tanja
ethz.grant.agreementno
335529
ethz.grant.fundername
EC
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.program
FP7
ethz.date.deposited
2019-02-21T06:23:14Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-02-22T14:25:01Z
ethz.rosetta.lastUpdated
2020-02-15T17:29:21Z
ethz.rosetta.versionExported
true
ethz.COinS
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