Phylodynamic analysis of an emergent Mycobacterium bovis outbreak in an area with no previously known wildlife infections

Phylodynamic analysis of an emergent Mycobacterium bovis outbreak in an area with no previously known wildlife infections
复制标题

DOI:
10.1111/1365-2664.14046
复制
发表时间:
2021-11-01
影响因子:
5.7
通讯作者:
Kao, Rowland R.
Kao, Rowland R.
中科院分区:
环境科学与生态学1区
文献类型:
--
作者:
Rossi, Gianluigi;Crispell, Joseph;Kao, Rowland R.

文献摘要

被引文献

相似文献

了解新出现的病原体如何成功地建立自己,并坚持在以前未受影响的人群是一个至关重要的问题,在疾病生态学,具有重要意义的疾病管理。在多宿主病原体系统中,这个问题特别困难,因为每个宿主物种对传播的重要性通常很难描述,并且疾病流行病学很复杂。观察和分析这种紧急情况的机会很少。在这里,我们利用了一个独特的数据集相结合的密集收集的牛结核分枝杆菌(牛结核病的病原体,bTB)的流行病学和进化特征的爆发在一个地区的牛和獾的人口被认为是低风险的bTB,没有以前的记录,无论是持续感染牛,或任何感染野生动物。我们使用数学建模、贝叶斯进化分析和机器学习相结合的方法来分析疫情动态。比较M。来自北方爱尔兰的牛全基因组序列证实这是来自后者地区的病原体单一引入,进化分析支持在獾中首次发现之前6年直接引入当地牛群。一旦引入,证据支持M。牛的流行病学动态经历了两个阶段,第一个阶段主要是牛与牛之间的传播,然后才在当地獾种群中建立起来。合成与应用。牛分枝杆菌突发疫情是本研究的对象,由于地理上远离先前已知的高风险地区,因此引起了相当大的关注。关于疫情控制的初步决定得到了全基因组测序数据的支持。本文所述的进一步分析用于估计传入时间(以及任何隐藏爆发的可能程度)和跨物种传播率,并提供了有价值的确认,即所实施控制的范围和重点是适当的。这些发现不仅加强了对基因组监测的呼吁,而且还为未来的疫情控制铺平了道路,为更快速和更果断的循证决策提供了见解。由于我们使用和开发的方法对疾病本身是不可知的,它们对其他缓慢传播的病原体也很有价值。
Understanding how emergent pathogens successfully establish themselves and persist in previously unaffected populations is a crucial problem in disease ecology, with important implications for disease management. In multi-host pathogen systems this problem is particularly difficult, as the importance of each host species to transmission is often poorly characterised, and the disease epidemiology is complex. Opportunities to observe and analyse such emergent scenarios are few. Here, we exploit a unique dataset combining densely collected data on the epidemiological and evolutionary characteristics of an outbreak of Mycobacterium bovis (the causative agent of bovine tuberculosis, bTB) in a population of cattle and badgers in an area considered low risk for bTB, with no previous record of either persistent infection in cattle, or of any infection in wildlife. We analyse the outbreak dynamics using a combination of mathematical modelling, Bayesian evolutionary analyses and machine learning. Comparison to M. bovis whole-genome sequences from Northern Ireland confirmed this to be a pathogen single introduction from the latter region, with evolutionary analysis supporting an introduction directly into the local cattle population 6 years prior to its first discovery in badgers. Once introduced, the evidence supports M. bovis epidemiological dynamics passing through two phases, the first dominated by cattle-to-cattle transmission before becoming established in the local badger population. Synthesis and applications. The Mycobacterium bovis emergent outbreak that was the object of this study was of considerable concern because of the geographical distance from previously known high-risk areas. Initial decisions about the outbreak control were supported by the whole-genome sequencing data. The further analyses described here were used to estimate the time of introduction (and therefore the likely magnitude of any hidden outbreak) and the rates of cross-species transmission, and provided valuable confirmation that the extent and focus of the imposed controls were appropriate. Not only do these findings strengthen the call for genomic surveillance, but they also pave the path for future outbreaks control, providing insights for more rapid and decisive evidence-based decision-making. As the methods we used and developed are agnostic to the disease itself, they are also valuable for other slowly transmitting pathogens.