21-EEID US-UK Collab: Multi-scale infection dynamics from cells to landscapes: foot-and-mouth disease viruses in African buffalo

传染病动力学必须在生物尺度上运作：病原体在宿主细胞和组织内复制；它们在宿主之间和宿主种群之间传播。因此，宿主-病原体相互作用的功能变化，例如那些影响病原体生命率或宿主免疫反应的功能变化，可以产生从分子到景观尺度的级联效应。同样，在更大范围内，病原体成功的变化会产生选择压力，这种压力会反馈给病原体群体遗传学。因此，跨生物尺度连接病原体动力学对于理解宿主-病原体系统的进化轨迹至关重要，并且代表了疾病生态学的核心挑战。传染病动力学的多尺度模型试图通过将代表宿主-病原体相互作用的机制模型从细胞尺度连接到群体尺度来解决这一挑战。近年来，开发数学工具来连接在不同时间和空间尺度上运行的动态过程一直是传染病建模的一个积极焦点。到目前为止，这些理论创新还没有与经验数据生成相匹配，提供了记录同一宿主-病原体系统中感染过程的综合数据流，这些数据流在整个组织范围内一致收集。这种不匹配限制了理论和数据之间的迭代相互作用。因此，多尺度疾病模型的全部潜力尚未实现。如果成功，这些方法可以提供工具来预测新的病原体变异在自然宿主群体中的传播和持久性，这是一个迫在眉睫的问题。在这个项目中，我们将研究从基因组到景观尺度的病毒动力学，使用一套口蹄疫病毒（fmdv）在其宿主非洲水牛中作为模型系统。fmdv是一种高传染性病毒，可在家养有蹄类动物中引起临床疾病，而其野生动物宿主中的地方性感染往往是亚临床感染。fmdv为融合多尺度疾病过程的理论和数据提供了一个很好的模型系统：(i)它们在一个共同的野生宿主中普遍存在；（ii）病毒多样性高，水牛种群中有三种不同的血清型，基本上是独立传播的，每种血清型中都记录了分化良好的谱系；（iii）突变积累迅速，为分子追踪和系统动力学分析提供高分辨率；（iv）由于口蹄疫病毒作为家畜病原体的重要性，该系统可通过完善的病毒培养、实验挑战、诊断和定量免疫反应方法进行处理。在我们之前在南非克鲁格国家公园（KNP）工作的基础上，我们将创建一个数据驱动的数学框架，将跨组织尺度的病毒动力学联系起来，以测试是否以及如何从病毒谱系的表型变异中预测宿主、种群和景观尺度上的动态。我们将利用实验、实地观察研究和系统动力学分析得出的数学模型，在细胞、宿主、种群和景观尺度上研究病毒动力学。在我们的模型中，我们将通过允许在较小尺度上的动力学来确定在较大尺度上感染过程的参数，从而建立跨尺度的明确联系。

Infectious disease dynamics necessarily operate across biological scales: pathogens replicate within host cells and tissues; they transmit among hosts, and across host populations. As such, functional changes in host-pathogen interactions, such as those affecting pathogen vital rates or host immune responses, can generate cascading effects from molecular to landscape scales. Similarly, variation in pathogen success at larger scales generates selective pressures that feed back to shape pathogen population genetics. Linking pathogen dynamics across biological scales is thus critical to understanding evolutionary trajectories of host-pathogen systems and represents a central challenge in disease ecology. Multi-scale models of infectious disease dynamics seek to address this challenge by linking mechanistic models representing host-pathogen interactions from cellular to population scales. Developing the mathematical tools for connecting dynamic processes operating at vastly different temporal and spatial scales has been an active focus in infectious disease modelling over recent years. These theoretical innovations have so far not been matched by empirical data generation, providing integrated data streams documenting infection processes in the same host-pathogen system, collected consistently across organizational scales. This mismatch has limited the iterative interplay between theory and data. As such, the full potential of multi-scale disease models has not been realized. If successful, these approaches could provide tools for predicting the spread and persistence of new pathogen variants in natural host populations from variant genetic or phenotypic traits - a question of immediate urgency. In this project we will investigate viral dynamics from genomic to landscape scales using a suite of foot-and-mouth disease viruses (FMDVs) in their reservoir host, African buffalo, as a model system. FMDVs are highly contagious viruses that cause clinical disease in domestic ungulates, while endemic infections in their wildlife reservoir tend to be subclinical. FMDVs present an excellent model system for merging theory and data on multi-scale disease processes: (i) they are ubiquitous in a common wild host; (ii) viral diversity is high, with three distinct serotypes circulating in the buffalo population essentially independently, and well-differentiated lineages documented within each serotype; (iii) mutations accrue rapidly, providing high resolution for molecular tracing and phylodynamic analysis; and (iv) due to FMDV's importance as a livestock pathogen, the system is tractable with well established methods for virus culture, experimental challenges, diagnostics and quantifying immune responses. Building on our previous work in South Africa's Kruger National Park (KNP), we will create a data-driven mathematical framework linking viral dynamics across organizational scales, to test if and how dynamics within hosts, at population and landscape scales can be predicted from phenotypic variation among viral lineages. We will investigate viral dynamics at cellular, within-host, population and landscape scales using mathematical models informed by experiments, observational field studies and phylodynamic analysis. In our models, we will establish explicit linkages across scales by allowing dynamics at the smaller scales to determine parameters for infection processes at larger scales.