A longitudinal model for the spread of bovine tuberculosis

牛结核病传播的纵向模型

• 批准号: BB/I013482/1
• 负责人: Gareth Roberts
• 金额: $ 5.36万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FI013482%2F1
• 关键词: longitudinal; model; spread; bovine; tuberculosis

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.

牛结核病（BTB）是对英国具有重大社会经济影响的牛和badge的重要疾病，目前每年的监视和赔偿金额超过1亿英镑，也导致了农民的昂贵运动和贸易限制。尽管进行了密集的控制，但疾病的发病率仍在增加。目前，通过屠宰场监视和常规的皮肤测试对疾病进行监测。单个牛群的常规测试频率是基于该疾病的局部事件，该疾病是感染风险的代理，但不能考虑单个群级特征或牛运动。最近的BTB研究重点是检查为此的潜在原因，包括环境污染（例如，当地野生动植物储层重新感染），对监视测试的不敏感以及大规模牛运动的影响。该提案的目的是将我们最近的工作扩展到识别单个牛群中感染的持久性的标志物中，以量化GB国家群中GB National Herd中传播的机制，并测试我们结果的实用性，以帮助基于风险的监视。 BTB感染的传播动力学可以由偶然过程驱动的传输模型表示，其观察过程受不完美的测试程序（或屠宰场对可见病变的识别）的控制，从而导致部分隐藏的感染。包含一个或多个反应堆的牛群被归类为分解，然后进行运动限制和更严格的测试，直到牛群测试清除。测试和牛运动信息可通过多个大型国家数据集获得。最近使用这些数据开发了最近的数学建模方法，尽管这些方法将提供有关人口级参数的有用信息，但它们平均提供了一些在单个群级上可用的详细信息。而且，它们的设计并不是为了预测单个赫德水平的疾病复发。在这里，我们建议基于连续的监视数据构建动态，统计，个人HERD级别模型，我们将使用基于似然的方法适合数据。主要方法论上的挑战是应对隐藏状态（感染）和牛群之间动物的运动。统计方法的最新进展，例如“数据提升”和“可逆的跳跃”马尔可夫链蒙特卡洛允许与关键感染相关参数同时估算所观察到的和隐藏状态的联合分布。我们将探索一种令人兴奋的替代方案，称为“顺序过滤”。主要的挑战是这些统计技术在计算上是密集的，尤其是考虑到数据集的大规模（约130,000个前提）和长时间框架（6年以上）。但是，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，为解决此问题提供了一种令人兴奋且具有成本效益的方法。这里的重点是BTB，但是这些模型以及我们将要解决的估计问题与广泛的传染病系统有关，该项目中开发的方法将适用于各种疾病系统。该项目的目的是使用强大的统计方法从测试观察结果中获取有关系统隐藏状态的信息，以此使我们能够根据过去的感染史以及局部事件以及与其他前提的局部事件以及与其他场所的联系来识别高风险群。这些信息将在针对更严格或更常规的测试的特定牛群方面具有实际用途。