Xu, Y;
Stockdale, JE;
Naidu, V;
Hatherell, H;
Stimson, J;
Stagg, HR;
Abubakar, I;
(2020)
Transmission analysis of a large tuberculosis outbreak in London: a mathematical modelling study using genomic data.
Microbial Genomics
10.1099/mgen.0.000450.
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Transmission analysis of a large tuberculosis outbreak in London: a mathematical modelling study using genomic data |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1099/mgen.0.000450 |
| Publisher version: | https://doi.org/10.1099/mgen.0.000450 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Genomic epidemiology, infectious disease, machine learning, modelling, tuberculosis |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10115723 |
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