Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks.
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Your vote was cast
Thank you for your feedback
Thank you for your feedback
MetadataShow full item record
TitleSimultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks.
Published inPlos Comput Biol 2017; 13(5):e1005495
PubliekssamenvattingWhole-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.
- When are pathogen genome sequences informative of transmission events?
- Authors: Campbell F, Strang C, Ferguson N, Cori A, Jombart T
- Issue date: 2018 Feb
- Epidemic Reconstruction in a Phylogenetics Framework: Transmission Trees as Partitions of the Node Set.
- Authors: Hall M, Woolhouse M, Rambaut A
- Issue date: 2015 Dec
- Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data.
- Authors: Campbell F, Cori A, Ferguson N, Jombart T
- Issue date: 2019 Mar
- Molecular Infectious Disease Epidemiology: Survival Analysis and Algorithms Linking Phylogenies to Transmission Trees.
- Authors: Kenah E, Britton T, Halloran ME, Longini IM Jr
- Issue date: 2016 Apr
- Within-host bacterial diversity hinders accurate reconstruction of transmission networks from genomic distance data.
- Authors: Worby CJ, Lipsitch M, Hanage WP
- Issue date: 2014 Mar