A longitudinal model for the spread of bovine tuberculosis

牛结核病（bTB）是牛和獾的一种重要疾病，在英国具有重大的社会经济影响，目前每年花费超过1亿英镑用于监测和补偿，并导致农民的流动和贸易限制代价高昂。尽管加强了控制，但疾病发病率仍在增加。目前通过屠宰场监测和定期皮肤测试对畜群进行疾病监测。对单个牛群进行常规检测的频率是基于疾病的局部发病率，这是感染风险的代表，但不考虑单个牛群水平的特征或牛的运动。最近的bTB研究侧重于检查造成这种情况的潜在潜在原因，包括环境污染（例如，来自当地野生动物水库的再次感染）、对监测测试不敏感以及大规模牲畜流动的影响。本提案的目的是将我们最近的工作扩展到单个畜群中感染持续性的标记物，以动态纵向框架来量化英国国家畜群中的传播机制，并测试我们的结果作为基于风险的监测的辅助工具的效用。结核分枝杆菌感染的传播动态可以用一个模型来表示，该模型的传播由偶然过程驱动，观察过程由不完善的测试程序（或屠宰场对可见病变的识别）控制，导致部分隐藏感染。包含一个或多个反应堆的反应堆群被归类为故障，然后对其进行行动限制，并进行更严格的测试，直到群体测试结束。通过几个大型国家数据集可以获得检测和牛的移动信息。利用这些数据开发了最近的数学建模方法，虽然这些方法将提供关于种群水平参数的有用信息，但它们平均了在单个畜群水平上可获得的一些详细信息。此外，它们也不是用来预测个体-群体水平上的疾病复发。在这里，我们建议建立一个动态的、统计的、个体-群体水平的模型，基于连续的监测数据，我们将使用基于似然的方法来拟合数据。主要的方法挑战将是处理隐藏状态（感染）和动物在畜群之间的运动。统计方法的最新进展，如“数据增强”和“可逆跳跃”马尔可夫链蒙特卡罗方法，允许同时估计观察状态和隐藏状态的联合分布以及关键感染相关参数。我们将探索一种令人兴奋的替代方案，称为“顺序滤波”。主要的挑战是，这些统计技术是计算密集型的，特别是考虑到大规模(大约。130,000个处所)和数据集的长时间框架（6年以上）。然而，计算机处理技术的进步，例如在图形卡上并行运行算法的体系结构，为解决这个问题提供了一种令人兴奋且经济有效的方法。这里的重点是bTB，但我们将解决的这些模型和评估问题与广泛的传染病系统相关，本项目开发的方法将适用于一系列疾病系统。该项目的目的是利用稳健的统计方法从测试观察中获得有关系统隐藏状态的信息，从而使我们能够根据过去的感染历史，以及局部发病率和与其他场所的联系来识别高风险畜群。这些信息在针对特定畜群进行更严格或更定期的检测方面具有实际用途。

Bovine tuberculosis (bTB) is an important disease of cattle and badgers with substantial socio-economic impact in the UK, currently costing the exchequer over £100 million per year in surveillance and compensation and also resulting in costly movement and trade restrictions for farmers. Despite intensive controls, disease incidence is still increasing. Currently herds are monitored for the disease through slaughterhouse surveillance and through regular skin testing. The frequency of routine testing for an individual herd is based on localised incidence of the disease, which acts as a proxy for risk of infection, but does not account for individual herd-level characteristics or cattle movements. Recent bTB research has focussed on examining potential underlying causes for this, including environmental contamination (e.g. re-infection from local wildlife reservoirs), insensitivity to the surveillance test and the impact of large-scale cattle movements. It is the purpose of this proposal to extend our recent work identifying markers for the persistence of infection in individual herds into a dynamic longitudinal framework in order to quantify the mechanisms of transmission in the GB national herd and to test the utility of our results as an aid to risk-based surveillance. The dynamics of transmission of bTB infection can be represented by a model with transmission driven by chance processes, with an observation process that is governed by an imperfect test procedure (or slaughterhouse identification of visible lesions), leading to partially hidden infection. Herds that contain one or more reactors are classified as breakdowns, which then have movement restrictions and more rigorous testing imposed until the herd tests clear. Testing and cattle movement information is available through several large national datasets. Recent mathematical modelling approaches have been developed using these data and, while these will provide useful information on population-level parameters, they average out some detailed information available at the individual herd level. Also, they were not designed to predict disease recurrence at the individual-herd level. Here we propose to build a dynamic, statistical, individual-herd level model, based on continuous surveillance data, which we will fit to the data using a likelihood-based approach. The main methodological challenge will be to deal with the hidden states (infection) and the movement of animals between the herds. Recent advances in statistical methodology, such as 'data-augmented' and 'reversible-jump' Markov chain Monte Carlo allow the joint distribution of the observed and hidden states to be estimated simultaneously along with key infection related parameters. We will explore an exciting alternative called 'sequential filtering'. The main challenge is that these statistical techniques are computationally intensive, especially given the large scale (approx. 130,000 premises) and long time frame (6+ years) of the datasets. However, advances in computer processing technology, such as architectures for running algorithms in parallel on graphics cards, provide an exciting and cost-effective way to approach this problem. The focus here is on bTB, but these sorts of models and the estimation issues that we will address are relevant to a wide range of infectious disease systems, and the methodology developed in this project would be applicable to a range of disease systems. It is the aim of this project to elicit information about the hidden states of the system from the test observations using robust statistical methodology, in a way that allows us to identify high-risk herds based on the past history of infection, as well as on localised incidence and connectedness to other premises. This information would have a practical use in terms of targeting specific herds with more stringent or more regular testing.