The bounded coalescent model: Conditioning a genealogy on a minimum root date

Carson, Jake; Ledda, Alice; Ferretti, Luca; Keeling, Matt J.; Didelot, Xavier

doi:10.1016/j.jtbi.2022.111186

Cited by 4 publications

(4 citation statements)

References 49 publications

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“…Our method implements a general pathogen population growth model rather than using the constant bounded coalescent model (Carson et al, 2022). The main restriction on the choice of model is that we must be able to calculate the likelihood of the phylogenetic tree, which in turn means that the coalescence rate must be integrable.…”

Section: Discussionmentioning

confidence: 99%

“…Thirdly, a complete transmission bottleneck meaning that only a single lineage is ever transmitted between hosts. In other words the within-host coalescent process is bounded so that the most recent common ancestor within a host occurs after the date of infection (Carson et al, 2022).…”

Section: New Approachesmentioning

confidence: 99%

“…In the original version of TransPhylo the coalescent model used was the bounded coalescent (Carson et al, 2022). This model follows the standard coalescent model with heterochronous sampling (Drummond et al, 2002), but conditions all lineages to coalesce before the infection time of each host.…”

Section: Coalescent Modelmentioning

confidence: 99%

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Inference of infectious disease transmission using multiple genomes per host

Carson

Keeling

Wyllie³

et al. 2023

Preprint

Self Cite

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In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.

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Section: Discussionmentioning

confidence: 99%

Section: New Approachesmentioning

confidence: 99%

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Inference of infectious disease transmission using multiple genomes per host

Carson

Keeling

Wyllie³

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, here we consider an alternative equivalent formulation of the coalescent model, in which the phylogeny is formed by iterating over the leaves one by one in increasing order of date, and considering how each leaf coalesces with the phylogeny made by the previous leaves ( Carson et al , 2022 ; Didelot et al , 2014 ). To do so, we consider that the dates

of the leaves are in increasing order, and that the dates

of the internal nodes are ordered so that

corresponds to the date of the internal node created when adding the leaf indexed k to the tree made of the first k − 1 leaves.…”

Section: Methodsmentioning

confidence: 99%

Distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease

et al. 2022

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Motivation The ability to distinguish imported cases from locally acquired cases has important consequences for the selection of public health control strategies. Genomic data can be useful for this, for example using a phylogeographic analysis in which genomic data from multiple locations is compared to determine likely migration events between locations. However, these methods typically require good samples of genomes from all locations, which is rarely available. Results Here we propose an alternative approach that only uses genomic data from a location of interest. By comparing each new case with previous cases from the same location we are able to detect imported cases, as they have a different genealogical distribution than that of locally acquired cases. We show that, when variations in the size of the local population are accounted for, our method has good sensitivity and excellent specificity for the detection of imports. We applied our method to data simulated under the structured coalescent model and demonstrate relatively good performance even when the local population has the same size as the external population. Finally, we applied our method to several recent genomic datasets from both bacterial and viral pathogens, and show that it can, in a matter of seconds or minutes, deliver important insights on the number of imports to a geographically limited sample of a pathogen population. Availability and Implementation The R package DetectImports is freely available from https://github.com/xavierdidelot/DetectImports Supplementary information Supplementary data are available at Bioinformatics online.

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Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host

Carson,

Keeling,

Wyllie

et al. 2024

Molecular Biology and Evolution

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In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. Furthermore, we remove the need for the assumption of a complete transmission bottleneck. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.

show abstract

The bounded coalescent model: Conditioning a genealogy on a minimum root date

Cited by 4 publications

References 49 publications

Inference of infectious disease transmission using multiple genomes per host

Inference of infectious disease transmission using multiple genomes per host

Distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease

Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host

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