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Isabel Rancu¹, Benjamin Sobkowiak¹, Joshua L Warren²

¹Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, Connecticut, USA.

Access microbiology

|June 16, 2025

概括

使用宿主数据预测结核病传播集群在高负担环境中具有挑战性. 一个回归模型在摩尔多瓦实现了17.2%的准确性,表明集群大小的增加改善了预测.

科学领域:

分子流行病学分子流行病学
传染病传播动态传染病传播动态
生物信息学和计算生物学

背景情况:

结核菌 (Mycobacterium tuberculosis) (Mtb) 的分子流行病学已经对传播动态的理解取得了进展.
由于缺乏培养或有限的测序资源,许多已报告的结核病例被排除在研究之外.
以前的基于回归的方法使用主机数据推断出无序的Mtb病例在传输集群中的成员身份.

研究的目的:

应用和评估基于回归的模型,以推断MTB在高负载环境中的传输集群成员资格 (摩尔多瓦共和国).
用全基因组测序数据和宿主人口/流行病学信息来评估模型的预测准确性.
调查遗传距离值 (SNP) 和最小集群大小对预测性能的影响.

主要方法:

使用了来自摩尔多瓦共和国的*Mycobacterium tuberculosis*的全基因组测序数据.
应用了基于回归的模型,以根据宿主数据预测未测序病例的集群成员资格.
使用不同的最大集群跨度 (例如,~40个SNP) 和最小集群大小 (例如,n≥10) 评估模型性能.

主要成果:

该模型实现了17.2%的预测准确度,用于识别摩尔多瓦最可能发生的集群mtb病例.
灵敏度分析显示,SNP值约20或80与约40个SNP相比,没有提高预测准确度.
增加最小集群大小显著提高了预测准确性.

结论:

在使用主机数据的高负载设置中推断Mtb传输存在重大挑战,与低负载设置相比,预测准确性较低.
这些发现强调了难以准确地将未经测序的结核病例分配到具有大量本地传播的人群中传播集群的困难.
优化像最小集群大小这样的参数可以提高这种预测模型的性能,但在复杂的流行病学场景中仍然存在局限性.

关键词:

这是分类分类的分类.流行病学流行病学结核病是一种肺结核病.

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