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.

中文翻译：

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伦敦大规模结核病疫情的传播分析：使用基因组数据的数学建模研究


结核病 (TB) 的暴发 — — 例如起源于 1995 年、以英国伦敦为中心的大规模异烟肼耐药疫情 — — 为模拟这种毁灭性疾病的传播提供了绝佳的机会。众所周知，结核病的传播链很难确定，但数学建模方法与全基因组测序数据相结合，具有为传播分析做出贡献的巨大潜力。利用这些数据，我们的目标是使用贝叶斯方法重建疫情爆发的传播历史，并使用机器学习技术和患者级别的数据来识别与传播相关的关键协变量。通过使用考虑系统发育不确定性的传输重建方法，我们能够以合理的置信度识别 21 个传输事件，其中 9 个 SNP 距离为零，最大距离为 3。发现了患者年龄、酗酒和无家可归史成为可靠的结核病传播者的最重要的预测因素。