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dc.contributor.author
Barido-Sottani, Joëlle
dc.contributor.author
Vaughan, Timothy
dc.contributor.author
Stadler, Tanja
dc.date.accessioned
2020-04-22T06:48:36Z
dc.date.available
2018-11-09T04:54:44Z
dc.date.available
2018-11-09T13:44:19Z
dc.date.available
2019-08-14T08:08:04Z
dc.date.available
2020-04-22T06:48:36Z
dc.date.issued
2018-09
dc.identifier.issn
1742-5689
dc.identifier.issn
1742-5662
dc.identifier.other
10.1098/rsif.2018.0512
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/302176
dc.identifier.doi
10.3929/ethz-b-000302176
dc.description.abstract
HIV patients form clusters in HIV transmission networks. Accurate identification of these transmission clusters is essential to effectively target public health interventions. One reason for clustering is that the underlying contact network contains many local communities. We present a new maximum-likelihood method for identifying transmission clusters caused by community structure, based on phylogenetic trees. The method employs a multi-state birth–death (MSBD) model which detects changes in transmission rate, which are interpreted as the introduction of the epidemic into a new susceptible community, i.e. the formation of a new cluster. We show that the MSBD method is able to reliably infer the clusters and the transmission parameters from a pathogen phylogeny based on our simulations. In contrast to existing cutpoint-based methods for cluster identification, our method does not require that clusters be monophyletic nor is it dependent on the selection of a difficult-to-interpret cutpoint parameter. We present an application of our method to data from the Swiss HIV Cohort Study. The method is available as an easy-to-use R package.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Royal Society
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Phylodynamics
en_US
dc.subject
Multi-state birth-death model
en_US
dc.subject
Transmission clusters
en_US
dc.subject
Maximum likelihood
en_US
dc.title
Detection of HIV transmission clusters from phylogenetic trees using a multi-state birth–death model
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2018-09-05
ethz.journal.title
Interface: Journal of the Royal Society
ethz.journal.volume
15
en_US
ethz.journal.issue
146
en_US
ethz.journal.abbreviated
J. R. Soc. Interface
ethz.pages.start
20180512
en_US
ethz.size
11 p.
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
en_US
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
2018-11-09T04:54:46Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-11-09T13:44:32Z
ethz.rosetta.lastUpdated
2021-02-15T10:27:16Z
ethz.rosetta.versionExported
true
ethz.COinS
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