ArticlePLoS computational biology2013
Bayesian estimation of mixture models with prespecified elements to compare drug resistance in treatment-naïve and experienced tuberculosis cases.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Mutations inInfection and drug resistance · 2026Article
- Differential Host Gene Expression in Response to Infection by DifferentMicroorganisms · 2024Article
- Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review.IEEE access : practical innovations, open solutions · 2024Article
- Genomic analysis of globally diverse Mycobacterium tuberculosis strains provides insights into the emergence and spread of multidrug resistance.Nature genetics · 2017Article
- Isoniazid-resistant tuberculosis: a cause for concern?The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2017Review
- Origin and proliferation of multiple-drug resistance in bacterial pathogens.Microbiology and molecular biology reviews : MMBR · 2015Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We propose a Bayesian approach for estimating branching tree mixture models to compare drug-resistance pathways (i.e. patterns of sequential acquisition of resistance to individual antibiotics) that are observed among Mycobacterium tuberculosis isolates collected from treatment-naïve and treatment-experienced patients. Resistant pathogens collected from treatment-naïve patients are strains for which fitness costs of resistance were not sufficient to prevent transmission, whereas those collected from treatment-experienced patients reflect both transmitted and acquired resistance, the latter of which may or may not be associated with lower transmissibility. The comparison of the resistance pathways constructed from these two groups of drug-resistant strains provides insight into which pathways preferentially lead to the development of multiple drug resistant strains that are transmissible. We apply the proposed statistical methods to data from worldwide surveillance of drug-resistant tuberculosis collected by the World Health Organization over 13 years.
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Registered trials
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