InFER: Likelihood-based Inference for Epidemic Risk

这项工作的动机是需要评估英国农业部门传染病传播的风险。我们专注于两个明显不同的背景下：流行病的背景下，疾病的反应需要非常迅速，和地方病的情况下，更长期的控制策略，需要最佳的疾病控制。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。特别令人感兴趣的是家禽业中的口蹄疫和禽流感。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，主要的问题将是数据渲染正式的统计方法的基础上的过渡模型难以进行没有高度优化和应用程序的特定算法。我们将特别关注牛结核病、钩端螺旋体和犬新孢子虫，尽管我们再次预期我们的方法将足够通用，以允许将开发的方法运输到不同的疾病背景。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，随着流行病的发展逐步推断模型内的参数，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。在上述应用程序中，种群由农场组成，其状态通过时间明确建模（通常为易感，感染或未检测，删除或偶尔其他状态）。控制策略的效果可以很容易地在这个人口水平的方法进行调查。模型也将是空间上明确的，允许疾病通过当地机制传播，如直接的当地接触和风传播。然而，还将酌情通过网络结构模拟疾病通过其他接触（可能通过商业联系）传播的情况。在许多情况下，有关网络结构的信息是不完整的，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有关其最近动向和接触者（所谓的危险接触者）的信息。我们项目的一项重要任务是开发一种统计方法，用于评估这一信息的重要性，并利用它来改进风险评估和控制。我们非常重视将我们的工作及其成果尽可能广泛地传播给大众。为此，我们将开发一个可视化软件包，与我们的统计分析结果结合使用。这将使我们的研究结果的影响，在风险预测，经济影响和控制策略的影响，很容易观察。我们的统计方法将完全贝叶斯，并将通过强大的马尔可夫链蒙特卡罗（及相关）技术进行。计算效率和速度将是使该方法实际有用的关键部分，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群中实时使用（例如在口蹄疫病例中约为10万人）。

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).