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

传染病动力学必然在生物学尺度上运作：病原体在宿主细胞和组织内复制;它们在宿主之间传播，并跨越宿主种群。因此，宿主-病原体相互作用的功能变化，如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，病原体在更大范围内成功的变化产生了选择压力，反馈到塑造病原体群体遗传学。因此，将病原体动力学跨生物尺度联系起来对于理解宿主-病原体系统的进化轨迹至关重要，也是疾病生态学的一个核心挑战。传染病动力学的多尺度模型试图通过将代表从细胞到群体尺度的宿主-病原体相互作用的机制模型联系起来来解决这一挑战。近年来，开发数学工具来连接在截然不同的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。迄今为止，这些理论创新还没有与经验数据生成相匹配，经验数据生成提供了记录同一宿主-病原体系统中感染过程的集成数据流，这些数据流在组织规模上一致地收集。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。如果成功的话，这些方法可以提供工具，用于预测新的病原体变体在自然宿主种群中的传播和持久性，从变异的遗传或表型特征-一个迫在眉睫的问题。在这个项目中，我们将调查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）在其水库主机，非洲布法罗，作为一个模型系统。口蹄疫病毒是一种高度传染性的病毒，在家养有蹄类动物中引起临床疾病，而在其野生动物宿主中的地方性感染往往是亚临床的。口蹄疫病毒提出了一个很好的模型系统，用于合并多尺度疾病过程的理论和数据：（i）它们在常见的野生宿主中普遍存在;（ii）病毒多样性高，在布法罗群体中基本上独立地传播三种不同的血清型，并且在每种血清型中记录了分化良好的谱系;（iii）突变迅速增加，为分子追踪和免疫动力学分析提供了高分辨率;和（iv）由于FMDV作为家畜病原体的重要性，该系统易于使用用于病毒培养、实验挑战、诊断和定量免疫应答的完善方法。在我们之前在南非克鲁格国家公园（KNP）的工作的基础上，我们将创建一个数据驱动的数学框架，将组织规模上的病毒动态联系起来，以测试是否以及如何从病毒谱系之间的表型变异预测宿主内的动态，在人口和景观规模上。我们将研究病毒在细胞，宿主内，人口和景观尺度上的动态，使用数学模型，通过实验，观察性实地研究和病毒动态分析。在我们的模型中，我们将建立明确的跨尺度的联系，允许在较小尺度的动态，以确定在较大尺度的感染过程的参数。

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.