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.

定量模型是投射疾病控制选择结果的有用工具，因此在它们之间进行选择。牛结核病的流行（TB）产生了大量数据，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，病原体序列数据的剥削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，传输途径的详细表示可以大大提高我们推断模型参数值的能力，从而使模型可以重新创建观察到的流行病的关键特征。这些方法中的许多是针对快速发展的病毒，并具有一致的进化时钟，并感染了单个宿主物种。但是，仍然需要开发更多的通用方法来推断多主宿主系统中的传输途径。一个关键的问题是，对所有相关宿主人群的观察通常是不平衡的，其中一个或多个重要的主机的数据难以获得。最近，我们使用了一种基于仿真的方法来考虑爱尔兰牛和badge虫中结核病的传播，并确定了重要的流行病学特性，尽管没有对the子种群或the感染的任何观察结果，但是这些方法仍需要在不同的场景中验证这些方法，并在两种宿主种群中都可以在各种情况下进行验证。此外，尽管我们的近似方法证明了在不同的badge贡献方案之间进行选择的能力，但该方法仍需要验证以使其在不同方案中有用。同时，我们还开发了基于似然的方法，以解决单个宿主系统中FMD传播的简单问题，以及对深入研究的Badger流行病的流行病学分析。在这个项目中，我们将生成一系列场景（流行与流行病，在每个人群中持续存在，只有一个人群，或者仅在两者中）和不同的接触网络关系，以确定在不同场景中传播的信号，并提出了解决潜在问题的新指标。我们将测试这些结果，我们将使用现存的数据集用于M. Bovis传输，并具有平衡的牛和badge信息以及非常不同的传输模式。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和充分的似然方法，我们将询问近似方法中的指标是否足以表征流行病的表征（足以与建模控制的整体目标），其次，是否足以描述与疾病控制选项之间相关的过程。在第一部分中，我们将使用现有的拟合方法与疾病暴发的真实数据进行比较模型输出，并使用它来开发更相关的指标的建议（并将其用于模型拟合中）。在第二届中，我们将根据流行病学的见解（例如，超级示威者的潜在作用或标准测试能力的变化来检测受感染的牛的潜在作用），使用这些模型来生成合成数据集，这些数据将适合于基线模型，然后使用拟议中的相对能力，并将其适合于一部分的模型，并将其拟合到了一部分，并将相对的模型拟合在一起，我们将提出多达三个不同的模型过程和结构。合成数据并预测控制的结果。因此，我们既应开发用于详细考虑可概括的多宿主系统动力学模型的方法，并解决了对重要疾病问题管理的关键问题，从而促进了更量身定制的方法来控制BTB和其他多宿主疾病。