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Bilateral BBSRC-SFI: Tackling a multi-host pathogen problem - phylodynamic analyses of the epidemiology of M. bovis in Britain and Ireland

双边 BBSRC-SFI：解决多宿主病原体问题 - 英国和爱尔兰牛分枝杆菌流行病学的系统动力学分析

基本信息

批准号：
BB/P010598/1
负责人：
Rowland Kao
金额：
$ 57.09万
依托单位：
University of Edinburgh
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FP010598%2F1
关键词：
Bilateral BBSRC SFI Tackling multi

项目摘要

Diseases that infect more than one host species can be particularly difficult to control, and well known examples include avian influenza (in wild birds, poultry and humans), rabies (in dogs and humans), Brucellosis (in livestock and humans) and Ebola virus (in primates and humans). If one or more of those is a wildlife species, control can prove particularly difficult. Wildlife are harder to observe, harder to access for purposes of disease control, and often we need to counterbalance the requirements of disease control with the needs of wildlife conservation. For all these reasons, identifying the root causes of disease transmission and quantifying the impact of disease control is a challenging problem in these 'multi-host' systems, a problem that is exacerbated when human management results in ecological disturbances that in themselves increase disease risk. An increasingly important tool in disentangling potential sources and routes of transmission is the deployment of mass "whole genome sequencing" of the causative agent of disease, taken from infected individuals. By tracking changes in the genetic code of the disease agent as it passes from individual to individual, and combining them with computer models that take into account other information we have on the transmission of disease (e.g. who was in contact with whom, and when, and how long individuals are infectious for) these data provide the best opportunity to identify 'who infected whom' - if not on the individual level, then at least at a level that is impossible without this kind of information. Importantly, it helps us to identify how important the different species are in these multi-species systems and also helps us to best identify how to control the disease. However, because these technologies and these uses of them are still relatively new, it is important to have good, well studied systems on which we can test and understand how best to use them.One disease problem which exhibits all these characteristics, has exceptional information and also represents an important problem in itself is bovine Tuberculosis (bTB) in cattle and badgers. BTB is estimated to cost the UK about £100 million per year, results in thousands of cattle slaughtered every year, and is a zoonotic risk to farmers, veterinarians and in particular individuals with compromising existing infections (e.g. HIV/AIDS). Badgers are known to be involved in the transmission of the disease to cattle and without some form of badger-targeted disease control, it will be impossible to eradicate. With any effective vaccine still many years away, badger culling is an important potential means of control but because badgers are a protected and much-loved species, it is highly controversial. This controversy is made worse by conflicting evidence regarding the value of culling, with trial culls in England suggesting that it induces a social 'perturbation effect' that makes culling impractical, while trials in the Republic of Ireland indicating it can be effective. In this project, we shall aim to build upon existing work and generate sequences for bTB in Irish cattle and badgers, taking advantage of the exceptional record they have of their badger population. Using mathematical models based on principles of 'social networks' to help us understand these data, we aim to contrast the control of bTB in Ireland, where badger culling has long been extensively used, with Northern Ireland and England, where it is not. This will allow us to estimate the potential benefit, if any, that badger culling could play in England, and the potential impact should culling efforts cease in Ireland. Thus this project will be of both immediate benefit to the control of bTB in cattle, but also have long term benefit in developing new approaches and insights that will improve our conceptual understanding of multi-host diseases, and the role that ecological disturbance plays in zoonotic disease emergence and spread.

感染一种以上宿主物种的疾病可能特别难以控制，众所周知的例子包括禽流感（在野鸟、家禽和人类中）、狂犬病（在狗和人类中）、布鲁氏菌病（在牲畜和人类中）和埃博拉病毒（在灵长类动物和人类中）。如果其中一种或多种是野生动物，控制可能会特别困难。野生动物更难观察，更难进入以进行疾病控制，而且我们通常需要在疾病控制的要求与野生动物保护的需求之间取得平衡。出于所有这些原因，确定疾病传播的根本原因并量化疾病控制的影响是这些“多宿主”系统中的一个具有挑战性的问题，当人类管理导致生态干扰而本身增加疾病风险时，这个问题就会加剧。解开潜在来源和传播途径的一个日益重要的工具是对取自感染个体的疾病病原体进行大规模“全基因组测序”。通过跟踪疾病病原体在个体之间传播时遗传密码的变化，并将其与考虑到我们掌握的有关疾病传播的其他信息（例如谁与谁接触、何时以及个体具有传染性的时间）的计算机模型相结合，这些数据提供了识别“谁感染了谁”的最佳机会——如果不是在个体层面上，那么至少在没有此类信息的情况下不可能达到的水平。重要的是，它帮助我们确定不同物种在这些多物种系统中的重要性，也帮助我们最好地确定如何控制疾病。然而，由于这些技术及其用途仍然相对较新，因此拥有良好的、经过充分研究的系统非常重要，我们可以在这些系统上测试和了解如何最好地使用它们。具有所有这些特征、具有特殊信息并且本身也代表一个重要问题的疾病问题是牛和獾中的牛结核病 (bTB)。据估计，BTB 每年给英国造成约 1 亿英镑的损失，每年导致数千头牛被屠宰，并且对农民、兽医，特别是患有现有感染（例如艾滋病毒/艾滋病）的个人来说，是一种人畜共患风险。众所周知，獾将疾病传播给牛，如果没有某种形式的针对獾的疾病控制，就不可能根除这种疾病。由于有效的疫苗还需要很多年才能问世，扑杀獾是一种重要的潜在控制手段，但由于獾是受保护且深受喜爱的物种，因此它备受争议。关于扑杀价值的相互矛盾的证据使这一争议变得更加严重，英国的试验扑杀表明扑杀会产生社会“扰动效应”，使扑杀变得不切实际，而爱尔兰共和国的试验表明扑杀可能是有效的。在这个项目中，我们的目标是在现有工作的基础上，利用爱尔兰牛和獾的特殊记录，生成爱尔兰牛和獾的 bTB 序列。我们使用基于“社交网络”原理的数学模型来帮助我们理解这些数据，目的是将长期以来广泛使用獾扑杀的爱尔兰与北爱尔兰和英格兰的 bTB 控制进行对比，而北爱尔兰和英格兰则没有广泛使用獾扑杀。这将使我们能够估计獾扑杀在英格兰可能带来的潜在好处（如果有的话），以及如果爱尔兰停止扑杀活动可能产生的潜在影响。因此，该项目不仅对控制牛的 bTB 有直接好处，而且对开发新方法和见解也有长期好处，这些新方法和见解将提高我们对多宿主疾病的概念理解，以及生态干扰在人畜共患疾病出现和传播中所起的作用。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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

DOI：
10.1111/1365-2664.14046
发表时间：
2021-11-01
期刊：
JOURNAL OF APPLIED ECOLOGY
影响因子：
5.7
作者：
Rossi, Gianluigi;Crispell, Joseph;Kao, Rowland R.
通讯作者：
Kao, Rowland R.

Genomic epidemiology of Mycobacterium bovis infection in sympatric badger and cattle populations in Northern Ireland.

DOI：
10.1099/mgen.0.001023
发表时间：
2023-05
期刊：
MICROBIAL GENOMICS
影响因子：
3.9
作者：
Akhmetova, Assel;Guerrero, Jimena;McAdam, Paul;Salvador, Liliana C. M.;Crispell, Joseph;Lavery, John;Presho, Eleanor;Kao, Rowland R.;Biek, Roman;Menzies, Fraser;Trimble, Nigel;Harwood, Roland;Pepler, P. Theo;Oravcova, Katarina;Graham, Jordon;Skuce, Robin;du Plessis, Louis;Thompson, Suzan;Wright, Lorraine;Byrne, Andrew W.;Allen, Adrian R.
通讯作者：
Allen, Adrian R.

SCoVMod - a spatially explicit mobility and deprivation adjusted model of first wave COVID-19 transmission dynamics.