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

开发更好的建模推理工具，利用流行病学和病原体遗传信息为牛结核病的疾病控制提供信息。

• 批准号: BB/W007290/1
• 负责人: Rowland Kao
• 金额: $ 48万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FW007290%2F1
• 关键词: Developing; better; modelling; inference; tools

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.

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) 的流行产生了大量数据，可用于生成详细的预测过程模型并评估其性能。最近，病原体序列数据的利用对我们对流行病的理解产生了变革性的影响。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，从而使模型能够重新创建观察到的流行病的关键特征。其中许多方法是针对具有一致进化时钟、感染单一宿主物种的快速进化病毒而开发的。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播途径。一个关键问题是，对所有相关宿主种群的观察往往不平衡，很难获得一个或多个重要宿主的数据。最近，我们使用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征，尽管没有对獾种群或獾感染进行任何观察，但这些方法需要在各个领域进行验证不同的场景，并在两个宿主物种的数据均可用的场景中进行了测试。此外，虽然我们的近似方法已经证明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。与此同时，我们还开发了基于可能性的方法来解决单一宿主系统中 FMD 传播的更简单问题，以及对深入研究的獾流行病进行流行病学分析。在这个项目中，我们将生成一套场景（地方病与流行病、每个人群中持续存在、仅一个人群或仅在两个人群中）和不同的接触网络关系，以识别跨不同场景传播的信号，以及提出解决根本问题的新指标。我们将测试这些结果，我们将使用现有的牛支原体传播数据集，其中包含平衡的牛和獾信息以及非常不同的传播模式。我们将考虑此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指标是否足以表征流行病（足以实现建模控制的总体目标）和其次，模型是否足以描述与选择疾病控制方案相关的过程。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爆发的真实数据进行比较，并使用它来制定更相关指标的建议（并在模型拟合中使用这些指标）。第二，我们将根据流行病学见解提出最多三种不同的模型流程和结构（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），使用这些来生成合成数据集，这些数据集将使用第一部分中提出的不同指标拟合基线模型，然后证明模型拟合这些不同指标的相对能力，以拟合合成数据并预测控制结果。因此，我们都应开发方法来考虑细节可概括多宿主系统动力学模型，并解决重要疾病问题管理的关键问题，从而促进更适合的方法来控制 bTB 和其他多宿主疾病。