Computing Science and Mathematics, Faculty of Natural Sciences, University of Stirling, Stirling, United Kingdom.
Royal (Dick) School of Veterinary Studies and The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom.
PLoS Comput Biol. 2021 Jun 25;17(6):e1009005. doi: 10.1371/journal.pcbi.1009005. eCollection 2021 Jun.
Multi-host pathogens are particularly difficult to control, especially when at least one of the hosts acts as a hidden reservoir. Deep sequencing of densely sampled pathogens has the potential to transform this understanding, but requires analytical approaches that jointly consider epidemiological and genetic data to best address this problem. While there has been considerable success in analyses of single species systems, the hidden reservoir problem is relatively under-studied. A well-known exemplar of this problem is bovine Tuberculosis, a disease found in British and Irish cattle caused by Mycobacterium bovis, where the Eurasian badger has long been believed to act as a reservoir but remains of poorly quantified importance except in very specific locations. As a result, the effort that should be directed at controlling disease in badgers is unclear. Here, we analyse densely collected epidemiological and genetic data from a cattle population but do not explicitly consider any data from badgers. We use a simulation modelling approach to show that, in our system, a model that exploits available cattle demographic and herd-to-herd movement data, but only considers the ability of a hidden reservoir to generate pathogen diversity, can be used to choose between different epidemiological scenarios. In our analysis, a model where the reservoir does not generate any diversity but contributes to new infections at a local farm scale are significantly preferred over models which generate diversity and/or spread disease at broader spatial scales. While we cannot directly attribute the role of the reservoir to badgers based on this analysis alone, the result supports the hypothesis that under current cattle control regimes, infected cattle alone cannot sustain M. bovis circulation. Given the observed close phylogenetic relationship for the bacteria taken from cattle and badgers sampled near to each other, the most parsimonious hypothesis is that the reservoir is the infected badger population. More broadly, our approach demonstrates that carefully constructed bespoke models can exploit the combination of genetic and epidemiological data to overcome issues of extreme data bias, and uncover important general characteristics of transmission in multi-host pathogen systems.
多宿主病原体特别难以控制，尤其是当至少有一种宿主充当隐藏的储主时。对密集采样的病原体进行深度测序有可能改变这种理解，但需要采用分析方法，共同考虑流行病学和遗传数据，以最佳解决这个问题。虽然在单一物种系统的分析中已经取得了相当大的成功，但隐藏的储主问题相对研究较少。这个问题的一个众所周知的例子是牛结核病，一种在英国和爱尔兰牛中发现的疾病，由牛分枝杆菌引起，长期以来，欧亚獾一直被认为是储主，但除了在非常特定的地点外，其重要性仍然难以量化。因此，不清楚应该将控制疾病的努力指向獾。在这里，我们分析了从一个牛群中收集的密集流行病学和遗传数据，但没有明确考虑来自獾的数据。我们使用模拟建模方法表明，在我们的系统中，一种利用可用的牛群人口统计和畜群间移动数据，但只考虑隐藏储主产生病原体多样性的能力的模型，可以用于在不同的流行病学情景之间进行选择。在我们的分析中，与在更广泛的空间尺度上产生多样性和/或传播疾病的模型相比，一个储主不产生任何多样性但在当地农场规模上导致新感染的模型更受青睐。虽然仅凭这一分析我们不能直接将储主的作用归因于獾，但这一结果支持了这样一种假设，即在当前的牛群控制制度下，仅受感染的牛群无法维持牛分枝杆菌的循环。鉴于从牛群和附近采样的獾中提取的细菌具有密切的系统发育关系，最合理的假设是储主是受感染的獾群。更广泛地说，我们的方法表明，精心构建的定制模型可以利用遗传和流行病学数据的组合来克服数据严重偏差的问题，并揭示多宿主病原体系统中传播的重要一般特征。
