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InFER: Likelihood-based Inference for Epidemic Risk

InFER：基于可能性的流行病风险推断

基本信息

批准号：
BB/H00811X/1
负责人：
Gareth Roberts
金额：
$ 75.05万
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：
2010
资助国家：
英国
起止时间：
2010 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FH00811X%2F1
关键词：
InFER Likelihood based Inference Epidemic

项目摘要

The work is motivated by the need to assess the risk due to spread of infectious diseases within the UK farming sector. We focus on two distinctly different contexts: the epidemic context, where disease response is required to be extremely rapid, and the endemic situation in which more long term control strategies are required for optimal disease control. In the epidemic context, risk assessment needs to be sufficiently rapid in order to effectively inform control strategies. A particular interest lies in foot and mouth disease and in Avian Influenza within the poultry industry. However the methodology developed will not be restricted to these contexts, and the project will construct software which can be rapidly adapted to other situations by expert users. Within the endemic context, the main problem will be the partiality of data rendering formal statistical methods based upon transition models difficult to carry out without highly optimised and application specific algorithms. We will have a particular focus on bovine TB, Leptospira hardjo and Neospora caninum, though again we anticipate that our approach will be sufficiently generic to permit the transportation of methodology developed to different disease contexts. The project will build realistic mathematical models for the random evolution of epidemics of infectious diseases. In particular, methodology will be developed for inferring progressively about the parameters within the model as the epidemic progresses with a view to assessing the risk of future disease propagation. Our approach will be explicitly population-based. In the applications above, the population consists of farms whose status is modelled explicitly through time (typically as susceptible, infectious with or without detection, removed, or occasionally other states). The effect of control strategies can be easily investigated within this population-level approach. Models will also be spatially explicit, allowing for the spread of the disease through local mechanisms such as direct local contact and wind-borne spread. However the spread of the disease through other contacts (perhaps through commercial links) will also also be modelled through network structures where appropriate. In many cases, information about network structure will be partial and the project will develop statistical methods for dealing with this. Particularly in the early stages of an epidemic, governmental authorities (such as Defra) acquire information from infected farms on their recent movements and contacts (so-called dangerous contacts). An important task in our project will be to develop a statistical methodology for assessing the importance of this information and using it to refine risk assessment and control. We place great emphasis on the dissemination of our work and its results to as general audience as possible. To this end, we shall develop a visualisation package to be used in conjunction with the output from our statistical analysis. This will permit the implications of our findings, in terms of risk prediction, economic impact and the implications of control strategies to be easily observed. Our statistical approach will be fully Bayesian, and will be carried out through powerful Markov chain Monte Carlo (and related) techniques. Computational efficiency and speed will be a crucial part of making the methodology practically useful, and the project will investigate new algorithmic and computational advances in order to ensure that the approach can be used in real-time within populations of medium size (for instance around 100 000 in the case of foot and mouth disease).

这项工作的动机是需要评估由于传染病在英国农业部门传播而造成的风险。我们关注两种截然不同的情况：流行病情况，疾病反应要求极其迅速，以及流行情况，在这种情况下，最佳疾病控制需要更长期的控制战略。在流行病方面，风险评估需要足够迅速，以便有效地为控制战略提供信息。家禽养殖业对口蹄疫和禽流感特别感兴趣。然而，开发的方法将不限于这些情况，该项目将构建可由专家用户迅速适应其他情况的软件。在流行的背景下，主要问题将是基于过渡模型的数据呈现正式统计方法的偏好性，如果没有高度优化的和特定于应用的算法，很难实施。我们将特别关注牛结核病、哈德霍型钩端螺旋体和犬新孢子虫，但我们再次预计，我们的方法将足够通用，以允许针对不同疾病背景开发的方法学的传播。该项目将为传染病流行的随机演变建立现实的数学模型。特别是，将制定方法，随着疫情的进展逐步推断模型中的参数，以评估未来疾病传播的风险。我们的方法将明确以人口为基础。在上面的应用中，种群由农场组成，其状态随时间显式建模(通常为易受感染、有或没有检测到的传染性、已移除或偶尔处于其他状态)。在这种总体水平的方法中，可以很容易地研究控制策略的效果。模型在空间上也将是明确的，允许通过当地直接接触和风媒传播等当地机制传播疾病。然而，通过其他接触传播疾病(可能通过商业联系)也将酌情通过网络结构进行模拟。在许多情况下，有关网络结构的信息将是不全面的，该项目将开发处理这一问题的统计方法。特别是在流行病的早期阶段，政府当局(如Defra)从受感染的农场获得关于他们最近的活动和接触(所谓的危险接触)的信息。我们项目中的一项重要任务将是开发一种统计方法，以评估这些信息的重要性，并利用这些信息来改进风险评估和控制。我们非常重视向尽可能广泛的受众传播我们的工作及其成果。为此，我们将开发一个可视化程序包，与我们的统计分析结果一起使用。这将使我们能够很容易地观察到我们的发现在风险预测、经济影响和控制战略方面的影响。我们的统计方法将完全是贝叶斯的，并将通过强大的马尔可夫链蒙特卡罗(及相关)技术来实现。计算效率和速度将是使方法实用的关键部分，该项目将研究新的算法和计算进展，以确保该方法可以在中等规模的人口中实时使用(例如，在口蹄疫情况下约有10万人)。