Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo

Didelot, Xavier; Kendall, Michaela; Xu, Yuanwei; White, Peter J; McCarthy, Noel D.

doi:10.1002/cpz1.60

Cited by 42 publications

(33 citation statements)

References 78 publications

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“…The analysis was conducted with activated tip dates, a Generalized Time Reversible (GTR) site model with a gamma category count of 4, a relaxed clock model, and a Birth-Death Skyline Serial model as the prior. Parameter selection was influenced by our previous work on HIV transmission and other publications using these tools for SARS-CoV-2 (see below) [ 14 – 16 , 32 ].…”

Section: Methodsmentioning

confidence: 99%

“…Transmission tree inference was conducted using the Bayesian program TransPhylo; a dedicated software designed to reconstruct transmission networks from timed phylogenetic data [ 7 ]. TransPhylo is particularly well suited and has been used for different COVID-19 dataset analyses [ 32 ]. TransPhylo enables the inference of transmission trees for an ongoing pandemic complete with unsampled sources of infection and the date of infection.…”

Section: Methodsmentioning

confidence: 99%

“…These parameters were made variable in the MCMC chain as these distributions varied based on the geographical region under study. These distributions take the form of gamma distributions, this is a common assumption made in epidemiology to explain both sampling and infection dynamics in a population and is adopted by TransPhylo [ 7 , 32 ]. However, we also tested this assumption by checking whether the data does in fact fit to a gamma distribution using the R package fitdistrplus [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

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Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection

et al. 2021

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The COVID-19 pandemic has illustrated the importance of infection tracking. The role of asymptomatic, undiagnosed individuals in driving infections within this pandemic has become increasingly evident. Modern phylogenetic tools that take into account asymptomatic or undiagnosed individuals can help guide public health responses. We finetuned established phylogenetic pipelines using published SARS-CoV-2 genomic data to examine reasonable estimate transmission networks with the inference of unsampled infection sources. The system utilised Bayesian phylogenetics and TransPhylo to capture the evolutionary and infection dynamics of SARS-CoV-2. Our analyses gave insight into the transmissions within a population including unsampled sources of infection and the results aligned with epidemiological observations. We were able to observe the effects of preventive measures in Canada’s “Atlantic bubble” and in populations such as New York State. The tools also inferred the cross-species disease transmission of SARS-CoV-2 transmission from humans to lions and tigers in New York City’s Bronx Zoo. These phylogenetic tools offer a powerful approach in response to both the COVID-19 and other emerging infectious disease outbreaks.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection

et al. 2021

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“…Within-host diversity and evolution is significant for many bacterial pathogens which blurs the relationships between transmission tree and phylogeny [71]. However, TransPhylo can infer the transmission tree from a dated phylogeny in a way that accounts for within-host evolution [38,72,73]. Significant uncertainty typically remains in the inferred transmission tree, which is captured by the use of Bayesian statistics within TransPhylo.…”

Section: From Dated Phylogeny To Epidemiologymentioning

confidence: 99%

A scalable analytical approach from bacterial genomes to epidemiology

Didelot

Parkhill

2021

Preprint

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SummaryRecent years have seen a remarkable increase in the practicality of sequencing whole genomes from large numbers of bacterial isolates. The availability of this data has huge potential to deliver new insights into the evolution and epidemiology of bacterial pathogens, but the scalability of the analytical methodology has been lagging behind that of the sequencing technology. Here we present a step-by-step approach for such large-scale genomic epidemiology analyses, from bacterial genomes to epidemiological interpretations. A central component of this approach is the dated phylogeny, which is a phylogenetic tree with branch lengths measured in units of time. The construction of dated phylogenies from bacterial genomic data needs to account for the disruptive effect of recombination on phylogenetic relationships, and we describe how this can be achieved. Dated phylogenies can then be used to perform fine-scale or large-scale epidemiological analyses, depending on the proportion of cases for which genomes are available. A key feature of this approach is computational scalability, and in particular the ability to process hundreds or thousands of genomes within a matter of hours. This is a clear advantage of the step-by-step approach described here. We discuss other advantages and disadvantages of the approach, as well as potential improvements and avenues for future research.

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“…Particularly for diseases with a high proportion of asymptomatic infections like COVID-19, the use of genomic information might be especially relevant to understand their dissemination. Several methods have been developed to reconstruct infectious disease outbreaks using genomic information (e.g., Didelot et al 2014;Jombart et al 2014;Worby, Chang, et al 2014;Hall et al 2015;Hall et al 2016;Lumby et al 2018;Didelot et al 2021). However, these strategies rely on pathogen genomes mutating rapidly between infected individuals (Campbell et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Limited genomic reconstruction of SARS-CoV-2 transmission history within local epidemiological clusters

Gallego-García

Varela

Estévez-Gómez

et al. 2021

Preprint

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A detailed understanding of how and when SARS-CoV-2 transmission occurs is crucial for designing effective prevention measures. Other than contact tracing, genome sequencing provides information to help infer who infected whom. However, the effectiveness of the genomic approach in this context depends on both (high enough) mutation and (low enough) transmission rates. Today, the level of resolution that we can obtain when describing SARS-CoV-2 outbreaks using just genomic information alone remains unclear. In order to answer this question, we sequenced 49 SARS-CoV-2 patient samples from ten local clusters for which partial epidemiological information was available, and inferred transmission history using genomic variants. Importantly, we obtained high-quality genomic data, sequencing each sample twice and using unique barcodes to exclude cross-sample contamination. Phylogenetic and cluster analyses showed that consensus genomes were generally sufficient to discriminate among independent transmission clusters. However, levels of intrahost variation were low, which prevented in most cases the unambiguous identification of direct transmission events. After filtering out recurrent variants across clusters, the genomic data were generally compatible with the epidemiological information but did not support specific transmission events over possible alternatives. We estimated the effective transmission bottleneck size to be 1-2 viral particles for sample pairs whose donor-recipient relationship was likely. Our analyses suggest that intrahost genomic variation in SARS-CoV-2 might be generally limited and that homoplasy and recurrent errors complicate identifying shared intrahost variants. Reliable reconstruction of direct SARS-CoV-2 transmission based solely on genomic data seems hindered by a slow mutation rate, potential convergent events, and technical artifacts. Detailed contact tracing seems essential in most cases to study SARS-CoV-2 transmission at high resolution.

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Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo

Cited by 42 publications

References 78 publications

Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection

Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection

A scalable analytical approach from bacterial genomes to epidemiology

Limited genomic reconstruction of SARS-CoV-2 transmission history within local epidemiological clusters

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