Efficient Bayesian modelling of infectious diseases in wildlife

野生动物传染病的高效贝叶斯建模

• 批准号: NE/V000616/1
• 负责人: Trevelyan McKinley
• 金额: $ 40.93万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FV000616%2F1
• 关键词: Efficient; Bayesian; modelling; infectious; diseases

Infectious diseases of wildlife result in significant welfare and conservation costs to wild animal populations. For example, chytridiomycosis is a fungal pathogen driving the mass extinction of numerous amphibian species worldwide, putting at risk >10% of all vertebrate species; while white-nose syndrome is estimated to have caused >6 million bat deaths in North America by 2012 alone. Diseases in wildlife can also have significant impacts on agriculture. For example, bovine tuberculosis (bTB) is a notifiable disease in livestock, which costs the UK government over £100 million per year in terms of testing and compensation for slaughtered animals, and also has huge impacts on the livelihoods of farmers. The pathogen has a wide host range, which includes protected species such as badgers, and currently, bTB is the subject of highly controversial badger culling trials aimed at attenuating disease transmission to livestock. Wildlife infectious diseases also represent a considerable threat to humans, as emerging zoonoses such as Ebola, Zika, West Nile virus, HIV and plague all attest to. In short, the list of WID outbreaks is long and growing, and we desperately need better tools to study their epidemiology.Mathematical modelling provides tools that enable us to better understand infectious disease dynamics, and can thus be used to help inform management strategies. However, the use of mathematical models without robustly fitting to observed data can lead to poor model predictions and inference, in turn hindering scientific enquiry and increasing the probability of making poor policy decisions. Fitting dynamic transmission models to observed data is highly challenging, since available data is incomplete, and thus standard statistical approaches that rely on estimation of the likelihood function cannot be employed.We will extend recent advances in simulation-based Bayesian inference methods, which have shown great utility in overcoming these difficulties. These approaches are flexible and tractable, but can be computationally demanding. This project will extend recent advances in the field to deal with key challenges, both in the scaling up of these methods to larger systems, and also in dealing with the complexities that typify wildlife disease systems, such as: incomplete longitudinal sampling of individuals (i.e. capture-mark-recapture), the application of multiple diagnostic tests, uncertainties in diagnostic test performance, complex spatial and meta-population structures, and demographic changes over time. We will explore the development of constrained simulation techniques, which have been shown to greatly improve the efficiency of these inference algorithms in small populations, and hence are good candidates for improving efficiency in larger, more complex populations. We will also explore the use of these algorithms to allow for the fitting and comparison of different transmission models, again extending recent work in the field. We will ground our research using the high-profile case study of bovine tuberculosis in badgers, which suffers from all of the system-uncertainty and data-quality issues described above. Additionally, the disease has a direct impact on the livelihoods of UK farmers, major policy decisions that influence voter behaviour, and the conservation and management of UK wildlife. We will use an unprecedented 40+ year longitudinal study of bTB in a natural, wild population of badgers, to provide a unique and powerful insight into the aetiology of the disease. Although we focus on wildlife disease systems in this project, the methodological advances developed will be applicable to a wider range of state-space systems.

野生动物的传染病给野生动物种群带来了巨大的福利和保护成本。例如，壶菌病是一种真菌病原体，导致全球许多两栖动物物种的大规模灭绝，使> 10%的所有脊椎动物物种处于危险之中;而据估计，仅到2012年，白鼻综合征就在北美造成了> 600万蝙蝠死亡。野生动物疾病也会对农业产生重大影响。例如，牛结核病（bTB）是牲畜中的一种应报告疾病，英国政府每年在屠宰动物的检测和赔偿方面花费超过1亿英镑，并且对农民的生计产生巨大影响。该病原体具有广泛的宿主范围，其中包括受保护的物种，如獾，目前，bTB是极具争议的獾扑杀试验的主题，旨在减少疾病对牲畜的传播。野生动物传染病也对人类构成相当大的威胁，埃博拉病毒、寨卡病毒、西尼罗河病毒、艾滋病毒和鼠疫等新出现的人畜共患疾病都证明了这一点。总之，妇女传染病爆发的名单很长，而且还在不断增加，我们迫切需要更好的工具来研究其流行病学。数学建模提供了使我们能够更好地了解传染病动态的工具，因此可以用来帮助制定管理战略。然而，使用数学模型而不与观测数据进行稳健拟合可能会导致模型预测和推理不佳，从而阻碍科学调查并增加做出错误决策的可能性。拟合动态传输模型的观测数据是非常具有挑战性的，因为现有的数据是不完整的，因此标准的统计方法，依赖于估计的似然functions.We不能扩展最近的进展，基于模拟的贝叶斯推理方法，这已经显示出很大的效用，在克服这些困难。这些方法是灵活和易于处理的，但可能需要计算。该项目将扩展该领域的最新进展，以应对关键挑战，包括将这些方法扩大到更大的系统，以及处理典型的野生动物疾病系统的复杂性，例如：个体不完全纵向抽样（即捕获-标记-再捕获）、多种诊断测试的应用、诊断测试性能的不确定性、复杂的空间和元群体结构，以及人口结构随时间的变化。我们将探讨约束模拟技术的发展，这些技术已被证明可以大大提高这些推理算法在小种群中的效率，因此是提高更大，更复杂种群效率的良好候选者。我们还将探索使用这些算法来拟合和比较不同的传输模型，再次扩展该领域的最新工作。我们将使用獾中牛结核病的高调案例研究来进行研究，该案例受到上述所有系统不确定性和数据质量问题的影响。此外，这种疾病对英国农民的生计、影响选民行为的重大政策决定以及英国野生动物的保护和管理都有直接影响。我们将在自然的野生獾种群中进行前所未有的40多年的bTB纵向研究，以提供对该疾病病因学的独特而强大的见解。虽然我们专注于野生动物疾病系统在这个项目中，开发的方法的进步将适用于更广泛的状态空间系统。