ArticleMicrobial genomics2020
Transmission analysis of a large tuberculosis outbreak in London: a mathematical modelling study using genomic data.
Article in Microbial genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Whole genome sequencing analysis of Mycobacterium tuberculosis reveals circulating strain types and drug-resistance mutations in the Philippines.Scientific reports · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host.Molecular biology and evolution · 2024Article
- Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review.IEEE access : practical innovations, open solutions · 2024Article
- An overview of tuberculosis outbreaks reported in the years 2011-2020.BMC infectious diseases · 2023Review
- Phylogenetic inference of pneumococcal transmission from cross-sectional data, a pilot study.Wellcome open research · 2023Article
- Transmission history of SARS-CoV-2 in humans and white-tailed deer.Scientific reports · 2022Article
- Methods Combining Genomic and Epidemiological Data in the Reconstruction of Transmission Trees: A Systematic Review.Pathogens (Basel, Switzerland) · 2022Review
- Genomic Epidemiology Analysis of Infectious Disease Outbreaks Using TransPhylo.Current protocols · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Outbreaks of tuberculosis (TB) - such as the large isoniazid-resistant outbreak centred on London, UK, which originated in 1995 - provide excellent opportunities to model transmission of this devastating disease. Transmission chains for TB are notoriously difficult to ascertain, but mathematical modelling approaches, combined with whole-genome sequencing data, have strong potential to contribute to transmission analyses. Using such data, we aimed to reconstruct transmission histories for the outbreak using a Bayesian approach, and to use machine-learning techniques with patient-level data to identify the key covariates associated with transmission. By using our transmission reconstruction method that accounts for phylogenetic uncertainty, we are able to identify 21 transmission events with reasonable confidence, 9 of which have zero SNP distance, and a maximum distance of 3. Patient age, alcohol abuse and history of homelessness were found to be the most important predictors of being credible TB transmitters.
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Registered trials
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