Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations.
Identifying likely transmissions in Mycobacterium bovis infected populations of cattle and badgers using the Kolmogorov Forward Equations.
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使用柯尔莫哥洛夫正向方程确定牛分枝杆菌感染的牛和獾群体中可能的传播。
DOI：
10.1038/s41598-020-78900-3
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发表时间：
2020-12-15
影响因子：
4.6
通讯作者：
Kao RR
中科院分区：
文献类型：
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作者：
Rossi G;Crispell J;Balaz D;Lycett SJ;Benton CH;Delahay RJ;Kao RR
已建立的全基因组测序（WGS）技术方法允许检测来源于宿主样品的病原体基因组中的单核苷酸多态性（SNP）。所获得的信息可用于及时跟踪病原体的演变，并可能以前所未有的准确性识别“谁感染了谁”。成功的方法包括整合进化和流行病学数据的“动态方法”。然而，它们通常是计算密集型的，需要大量的数据，并且当存在强分子时钟信号和大量病原体多样性时最好应用。为了确定当病原体遗传多样性低且元数据有限时可以推断出多少传播信息，我们提出了一种分析方法，该方法将病原体WGS数据和受感染宿主的采样时间相结合。它解释了“尺度间”过程，特别是宿主内病原体进化和宿主间传播。我们将其应用于具有地方性牛分枝杆菌（牛/人畜共患结核病的病原体，bTB）感染的特征良好的人群。我们的研究结果表明，即使在这样有限的数据和低多样性，主机对之间的传播概率的计算可以帮助区分可能的和不可能的感染途径，因此有助于确定潜在的传播网络。然而，该方法可能是敏感的假设内主机的演变。
Established methods for whole-genome-sequencing (WGS) technology allow for the detection of single-nucleotide polymorphisms (SNPs) in the pathogen genomes sourced from host samples. The information obtained can be used to track the pathogen’s evolution in time and potentially identify ‘who-infected-whom’ with unprecedented accuracy. Successful methods include ‘phylodynamic approaches’ that integrate evolutionary and epidemiological data. However, they are typically computationally intensive, require extensive data, and are best applied when there is a strong molecular clock signal and substantial pathogen diversity. To determine how much transmission information can be inferred when pathogen genetic diversity is low and metadata limited, we propose an analytical approach that combines pathogen WGS data and sampling times from infected hosts. It accounts for ‘between-scale’ processes, in particular within-host pathogen evolution and between-host transmission. We applied this to a well-characterised population with an endemic Mycobacterium bovis (the causative agent of bovine/zoonotic tuberculosis, bTB) infection. Our results show that, even with such limited data and low diversity, the computation of the transmission probability between host pairs can help discriminate between likely and unlikely infection pathways and therefore help to identify potential transmission networks. However, the method can be sensitive to assumptions about within-host evolution.
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影响因子：
6.7
作者：
Biek, Roman;O'Hare, Anthony;Kao, Rowland R.
通讯作者：
Kao, Rowland R.
在非常不同的时间量表上的疾病动态：英国牲畜运动网络上的脚和口径。
DOI：
10.1098/rsif.2007.1129
发表时间：
2007-10-22
期刊：
Journal of the Royal Society, Interface
影响因子：
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作者：
Kao RR;Green DM;Johnson J;Kiss IZ
通讯作者：
Kiss IZ
DOI：
10.1098/rspb.2007.1442
发表时间：
2008-04-22
影响因子：
4.7
作者：
Cottam, Eleanor M.;Thebaud, Gael;Haydon, Daniel T.
通讯作者：
Haydon, Daniel T.
影响因子：
4.3
作者：
De Maio, Nicola;Wu, Chieh-Hsi;Wilson, Daniel J.
通讯作者：
Wilson, Daniel J.
影响因子：
30.8
作者：
通讯作者：
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