InFER: Likelihood-based Inference for Epidemic Risk

这项工作的动机是需要评估传染病在英国农业部门传播的风险。我们重点关注两种截然不同的情况：流行病情况，需要极其迅速地对疾病作出反应；地方病情况，需要采取更长期的控制战略以实现最佳的疾病控制。在流行病背景下，风险评估需要足够迅速，以便有效地为控制战略提供信息。特别关注的是家禽业中的口蹄疫和禽流感。然而，所开发的方法将不局限于这些环境，该项目将构建能够由专家用户快速适应其他情况的软件。在地方性环境中，主要问题将是数据的偏袒，使得基于转换模型的正式统计方法在没有高度优化和特定于应用的算法的情况下难以执行。我们将特别关注牛结核病、硬钩端螺旋体和犬新孢子虫，尽管我们再次预计我们的方法将足够通用，以允许将开发的方法应用于不同的疾病环境。该项目将为传染病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，以便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。在上面的应用程序中，种群由农场组成，这些农场的状态是通过时间明确建模的（通常是易感的、有或没有检测到的传染性、移除的，或者偶尔是其他状态）。控制策略的效果可以很容易地在这种人口水平的方法中进行调查。模型还将在空间上明确，允许疾病通过当地机制传播，如当地直接接触和风传播。然而，通过其他接触（也许是通过商业联系）传播疾病的情况也将酌情通过网络结构进行模拟。在许多情况下，关于网络结构的信息将是局部的，项目将开发统计方法来处理这一点。特别是在流行病的早期阶段，政府当局（如Defra）从受感染农场获取有关其最近活动和接触者（所谓危险接触者）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来评估这些信息的重要性，并使用它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。为此目的，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方面很容易观察到。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，该项目将调查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，在口蹄疫的情况下，大约有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).