Konzen Evandro, Delahay Richard J, Hodgson Dave J, McDonald Robbie A, Brooks Pollock Ellen, Spencer Simon E F, McKinley Trevelyan J
University of Exeter Medical School, University of Exeter, Exeter, United Kingdom.
Department of Statistics, University of Warwick, Coventry, United Kingdom.
PLoS Comput Biol. 2024 Nov 19;20(11):e1012592. doi: 10.1371/journal.pcbi.1012592. eCollection 2024 Nov.
Bovine tuberculosis (bTB) has significant socio-economic and welfare impacts on the cattle industry in parts of the world. In the United Kingdom and Ireland, disease control is complicated by the presence of infection in wildlife, principally the European badger. Control strategies tend to be applied to whole populations, but better identification of key sources of transmission, whether individuals or groups, could help inform more efficient approaches. Mechanistic transmission models can be used to better understand key epidemiological drivers of disease spread and identify high-risk individuals and groups if they can be adequately fitted to observed data. However, this is a significant challenge, especially within wildlife populations, because monitoring relies on imperfect diagnostic test information, and even under systematic surveillance efforts (such as capture-mark-recapture sampling) epidemiological events are only partially observed. To this end we develop a stochastic compartmental model of bTB transmission, and fit this to individual-level data from a unique > 40-year longitudinal study of 2,391 badgers using a recently developed individual forward filtering backward sampling algorithm. Modelling challenges are further compounded by spatio-temporal meta-population structures and age-dependent mortality. We develop a novel estimator for the individual effective reproduction number that provides quantitative evidence for the presence of superspreader badgers, despite the population-level effective reproduction number being less than one. We also infer measures of the hidden burden of infection in the host population through time; the relative likelihoods of competing routes of transmission; effective and realised infectious periods; and longitudinal measures of diagnostic test performance. This modelling framework provides an efficient and generalisable way to fit state-space models to individual-level data in wildlife populations, which allows identification of high-risk individuals and exploration of important epidemiological questions about bTB and other wildlife diseases.
牛结核病（bTB）对世界部分地区的养牛业具有重大的社会经济和福利影响。在英国和爱尔兰，野生动物（主要是欧洲獾）感染疾病使疾病控制变得复杂。控制策略往往应用于整个种群，但更好地识别关键传播源（无论是个体还是群体）有助于制定更有效的方法。如果机械传播模型能够充分拟合观测数据，就可以用来更好地理解疾病传播的关键流行病学驱动因素，并识别高风险个体和群体。然而，这是一项重大挑战，尤其是在野生动物种群中，因为监测依赖于不完善的诊断测试信息，而且即使在系统监测努力下（如标记重捕抽样），流行病学事件也只是部分被观察到。为此，我们开发了一个牛结核病传播的随机 compartmental 模型，并使用最近开发的个体前向滤波后向抽样算法将其拟合到来自对2391只獾进行的一项独特的40多年纵向研究的个体水平数据。时空meta种群结构和年龄依赖性死亡率使建模挑战进一步复杂化。我们开发了一种个体有效繁殖数的新型估计器，尽管种群水平的有效繁殖数小于1，但它为超级传播者獾的存在提供了定量证据。我们还推断了宿主种群中随时间变化的隐藏感染负担的度量；竞争传播途径的相对可能性；有效和实际感染期；以及诊断测试性能的纵向度量。这个建模框架提供了一种有效且通用的方法，将状态空间模型拟合到野生动物种群的个体水平数据，从而能够识别高风险个体，并探索关于牛结核病和其他野生动物疾病的重要流行病学问题。
