Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks.

2.50
Hdl Handle:
http://hdl.handle.net/10029/621344
Title:
Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks.
Authors:
Klinkenberg, Don; Backer, Jantien A; Didelot, Xavier; Colijn, Caroline; Wallinga, Jacco
Abstract:
Whole-genome sequencing of pathogens from host samples becomes more and more routine during infectious disease outbreaks. These data provide information on possible transmission events which can be used for further epidemiologic analyses, such as identification of risk factors for infectivity and transmission. However, the relationship between transmission events and sequence data is obscured by uncertainty arising from four largely unobserved processes: transmission, case observation, within-host pathogen dynamics and mutation. To properly resolve transmission events, these processes need to be taken into account. Recent years have seen much progress in theory and method development, but existing applications make simplifying assumptions that often break up the dependency between the four processes, or are tailored to specific datasets with matching model assumptions and code. To obtain a method with wider applicability, we have developed a novel approach to reconstruct transmission trees with sequence data. Our approach combines elementary models for transmission, case observation, within-host pathogen dynamics, and mutation, under the assumption that the outbreak is over and all cases have been observed. We use Bayesian inference with MCMC for which we have designed novel proposal steps to efficiently traverse the posterior distribution, taking account of all unobserved processes at once. This allows for efficient sampling of transmission trees from the posterior distribution, and robust estimation of consensus transmission trees. We implemented the proposed method in a new R package phybreak. The method performs well in tests of both new and published simulated data. We apply the model to five datasets on densely sampled infectious disease outbreaks, covering a wide range of epidemiological settings. Using only sampling times and sequences as data, our analyses confirmed the original results or improved on them: the more realistic infection times place more confidence in the inferred transmission trees.
Citation:
Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks. 2017, 13 (5):e1005495 PLoS Comput. Biol.
Journal:
Plos Comput Biol 2017; 13(5):e1005495
Issue Date:
May-2017
URI:
http://hdl.handle.net/10029/621344
DOI:
10.1371/journal.pcbi.1005495
PubMed ID:
28545083
Type:
Article
Language:
en
ISSN:
1553-7358
Appears in Collections:
Miscellaneous

Full metadata record

DC FieldValue Language
dc.contributor.authorKlinkenberg, Donen
dc.contributor.authorBacker, Jantien Aen
dc.contributor.authorDidelot, Xavieren
dc.contributor.authorColijn, Carolineen
dc.contributor.authorWallinga, Jaccoen
dc.date.accessioned2018-02-07T08:04:45Z-
dc.date.available2018-02-07T08:04:45Z-
dc.date.issued2017-05-
dc.identifier.citationSimultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks. 2017, 13 (5):e1005495 PLoS Comput. Biol.en
dc.identifier.issn1553-7358-
dc.identifier.pmid28545083-
dc.identifier.doi10.1371/journal.pcbi.1005495-
dc.identifier.urihttp://hdl.handle.net/10029/621344-
dc.description.abstractWhole-genome sequencing of pathogens from host samples becomes more and more routine during infectious disease outbreaks. These data provide information on possible transmission events which can be used for further epidemiologic analyses, such as identification of risk factors for infectivity and transmission. However, the relationship between transmission events and sequence data is obscured by uncertainty arising from four largely unobserved processes: transmission, case observation, within-host pathogen dynamics and mutation. To properly resolve transmission events, these processes need to be taken into account. Recent years have seen much progress in theory and method development, but existing applications make simplifying assumptions that often break up the dependency between the four processes, or are tailored to specific datasets with matching model assumptions and code. To obtain a method with wider applicability, we have developed a novel approach to reconstruct transmission trees with sequence data. Our approach combines elementary models for transmission, case observation, within-host pathogen dynamics, and mutation, under the assumption that the outbreak is over and all cases have been observed. We use Bayesian inference with MCMC for which we have designed novel proposal steps to efficiently traverse the posterior distribution, taking account of all unobserved processes at once. This allows for efficient sampling of transmission trees from the posterior distribution, and robust estimation of consensus transmission trees. We implemented the proposed method in a new R package phybreak. The method performs well in tests of both new and published simulated data. We apply the model to five datasets on densely sampled infectious disease outbreaks, covering a wide range of epidemiological settings. Using only sampling times and sequences as data, our analyses confirmed the original results or improved on them: the more realistic infection times place more confidence in the inferred transmission trees.en
dc.language.isoenen
dc.rightsArchived with thanks to PLoS computational biologyen
dc.subject.meshAlgorithms-
dc.subject.meshBacteria-
dc.subject.meshBacterial Infections-
dc.subject.meshComputational Biology-
dc.subject.meshDisease Transmission, Infectious-
dc.subject.meshGenome, Bacterial-
dc.subject.meshGenome, Viral-
dc.subject.meshHumans-
dc.subject.meshPhylogeny-
dc.subject.meshPolymorphism, Single Nucleotide-
dc.subject.meshVirus Diseases-
dc.subject.meshViruses-
dc.titleSimultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks.en
dc.typeArticleen
dc.identifier.journalPlos Comput Biol 2017; 13(5):e1005495en

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