Developing better modelling inference tools to inform disease control for bovine Tuberculosis using epidemiological and pathogen genetic information.

定量模型是预测疾病控制方案结果的有用工具，因此可以在它们之间进行选择。牛结核病(TB)的流行产生了丰富的数据，可以利用这些数据来生成详细的预测过程模型并评估其性能。最近，病原体序列数据的开发对我们对流行病的理解产生了革命性的影响。在数学建模的背景下，传播途径的详细表示可以极大地提高我们推断模型参数值的能力，从而使模型能够重现观察到的流行病的关键特征。这些方法中的许多都是为快速进化的病毒开发的，这些病毒具有一致的进化时钟，感染单一宿主物种。然而，仍然需要开发更通用的方法来推断多主机系统中的传输路径。一个关键问题是，对所有相关宿主群体的观察往往是不平衡的，很难获得一个或多个重要宿主的数据。最近，我们使用了一种基于模拟的方法来考虑结核病在爱尔兰牛和獾中的传播，并确定了重要的流行病学特性，尽管没有对獾的种群或獾的感染进行任何观察，但这些方法需要在不同的情况下进行验证，并在可获得两个宿主物种的数据的情况下进行测试。此外，虽然我们的近似方法已经演示了在不同的獾贡献场景之间进行选择的能力，但该方法仍有待验证，以使其在不同的场景中有用。同时，我们还开发了基于可能性的方法来解决在单一宿主系统中的口蹄疫传播这一更简单的问题，以及对深入研究的獾疫情进行流行病学分析。在这个项目中，我们将生成一套情景(地方病与流行病、在每个人群中持续存在、仅在一个人群中、或仅在两个人群中一起)和不同的联系网络关系，以确定要在不同情景中传输的信号，并为解决潜在问题提出新的衡量标准。我们将测试这些结果，我们将使用现有的牛分枝杆菌传播数据集，平衡牛和獾的信息，并采用非常不同的传播模式。我们将考虑这一过程的两个关键方面--首先，通过比较我们开发的近似方法和完全似然方法，我们将询问近似方法中的度量标准是否足以描述流行病的特征(足以描述建模控制的总体目标)；第二，模型是否足以描述与疾病控制方案之间的选择相关的过程。在第一部分中，我们将使用现有的拟合方法将模型输出与疾病暴发的真实数据进行比较，并使用这一点来制定更相关的衡量标准的建议(并在模型拟合中使用这些建议)。在第二部分中，我们将基于流行病学的洞察力提出最多三种不同的模型过程和结构(例如，超级脱落者的潜在作用，或标准测试检测受感染牛的能力的变化)，使用这些方法来生成合成数据集，该合成数据集将使用第一部分中提出的不同度量来拟合基线模型，然后展示适合这些不同度量的模型对合成数据和控制结果进行预测的相对能力。因此，我们将开发两种方法来详细考虑可推广的多宿主系统动力学模型，并解决管理重要疾病问题的关键问题。从而促进采取更有针对性的方法来控制结核和其他多宿主疾病。

Quantitative models are useful tools for projecting the outcome of disease control options, and therefore choosing between them. The epidemic of bovine Tuberculosis (TB) has generated a wealth of data which can be exploited to generate detailed predictive process models and evaluate their performance. Recently, the exploitation of pathogen sequence data has had a transformative impact on our understanding of epidemic diseases. In the context of mathematical modelling, the detailed representation of transmission pathways can greatly improve our ability to infer the values of model parameters that allows the models to recreate key characteristics of observed epidemics. Many of these methods have been developed for of rapidly evolving viruses with consistent evolutionary clocks, infecting a single host species. However, there remains a need to develop more general methods to infer transmission pathways in multi-host systems. A critical issue is that observations on all relevant host populations are often unbalanced, with data on one or more important hosts difficult to obtain. Recently we have used a simulation-based approach for considering the transmission of TB in Irish cattle and badgers, and identifies important epidemiological properties, despite the absence of any observations on the badger populations or infection in the badgers however these approaches need to be validated across different scenarios, and tested in scenarios where data across both host species are available. Further, while our approximate approach has demonstrated the ability to select between different badger contribution scenarios, the approach remains to be validated to make it useful across different scenarios. In parallel, we have also developed likelihood-based approaches for the simpler problem of FMD transmission in a single host system, as well as for the epidemiological analysis of an intensively studied badger epidemic. In this project, we shall generate a suite of scenarios (endemic vs. epidemic, persistent in each population, only one population, or only in the two together) and different contact network relationships, to identify signals for transmission across the different scenarios, and propose new metrics for solving the underlying problems. We shall test these outcomes, we shall use extant datasets for M. bovis transmission with balanced cattle and badger information and very different transmission patterns. We shall consider two critical aspects of this process - first, by comparing the approximate and full likelihood methods we develop, we shall ask if the metrics in the approximate method are adequate for characterising the epidemic (sufficiently to the overall objective of modelling control) and second, if the model adequate for describing the processes relevant to choosing between disease control options. In the 1st part, we shall compare model outputs using the existing fitting approaches to the real data on disease outbreaks, and use this to develop recommendations of more relevant metrics (and using these in model fitting). In the 2nd, we shall propose up to three different model processes and structures based on epidemiological insight (e.g. the potential role of supershedders, or variation in the ability of the standard test to detect infected cattle), use these to generate synthetic datasets which will be fitted to the baseline model using the different metrics proposed in part one, and then demonstrate the relative ability of the model fitted to these different metrics to fit the synthetic data and predict to outcome of control.Therefore we shall both developing methods to consider in detail generalisable multi-host phylodynamic models, & address key issues for the management of an important disease problem, thereby facilitating more tailored approaches to control of bTB and other multi-host diseases.