Transmission analysis of a large tuberculosis outbreak in London: a mathematical modelling study using genomic data.
Antitubercular Agents
/ therapeutic use
Disease Outbreaks
Drug Resistance, Bacterial
/ genetics
Genome, Bacterial
/ genetics
High-Throughput Nucleotide Sequencing
Humans
Isoniazid
/ therapeutic use
London
/ epidemiology
Models, Theoretical
Mycobacterium tuberculosis
/ genetics
Polymorphism, Single Nucleotide
/ genetics
Tuberculosis, Pulmonary
/ epidemiology
Whole Genome Sequencing
genomic epidemiology
infectious disease
machine learning
modelling
tuberculosis
Journal
Microbial genomics
ISSN: 2057-5858
Titre abrégé: Microb Genom
Pays: England
ID NLM: 101671820
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
pubmed:
12
11
2020
medline:
17
8
2021
entrez:
11
11
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33174832
doi: 10.1099/mgen.0.000450
pmc: PMC7725332
doi:
Substances chimiques
Antitubercular Agents
0
Isoniazid
V83O1VOZ8L
Banques de données
figshare
['10.6084/m9.figshare.12413012']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Medical Research Council
ID : MR/R008345/1
Pays : United Kingdom
Références
BMJ. 2018 Aug 23;362:k2738
pubmed: 30139910
PLoS Med. 2016 Oct 4;13(10):e1002137
pubmed: 27701423
PLoS Pathog. 2018 Feb 8;14(2):e1006885
pubmed: 29420641
Front Microbiol. 2016 May 03;7:394
pubmed: 27199896
Lancet Infect Dis. 2017 Mar;17(3):275-284
pubmed: 27919643
J Infect Dis. 2017 Nov 3;216(suppl_6):S644-S653
pubmed: 29112745
PLoS Comput Biol. 2014 Apr 10;10(4):e1003537
pubmed: 24722319
Clin Microbiol Infect. 2018 Jun;24(6):604-609
pubmed: 29108952
Thorax. 2004 Apr;59(4):279-85
pubmed: 15047945
Mol Biol Evol. 2015 Jan;32(1):268-74
pubmed: 25371430
PLoS Med. 2013;10(2):e1001387
pubmed: 23424287
Sci Adv. 2018 Oct 17;4(10):eaat5869
pubmed: 30345355
Nat Rev Genet. 2018 Jan;19(1):9-20
pubmed: 29129921
J Clin Microbiol. 2018 Jul 26;56(8):
pubmed: 29848567
Proc Natl Acad Sci U S A. 2018 Apr 17;115(16):4200-4205
pubmed: 29610334
Mol Biol Evol. 2017 Apr 1;34(4):997-1007
pubmed: 28100788
J Public Health (Oxf). 2010 Mar;32(1):44-51
pubmed: 19542269
Evol Med Public Health. 2014 Jun 09;2014(1):96-108
pubmed: 24916411
Microb Genom. 2018 Oct;4(10):
pubmed: 30216147
Euro Surveill. 2011 Mar 31;16(13):
pubmed: 21489373
Mol Phylogenet Evol. 1997 Dec;8(3):398-414
pubmed: 9417897
J Clin Microbiol. 2004 Jul;42(7):2952-60
pubmed: 15243044
Lancet. 2015 Dec 5;386(10010):2324-33
pubmed: 26515676
Mol Ecol Resour. 2017 Nov;17(6):1385-1392
pubmed: 28374552
Genome Med. 2014 Nov 20;6(11):104
pubmed: 25593591
Mol Biol Evol. 2014 Jul;31(7):1869-79
pubmed: 24714079
Virus Evol. 2016 Apr 09;2(1):vew007
pubmed: 27774300