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

传染病动态必然跨越生物尺度：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，寄主-病原体相互作用中的功能变化，如那些影响病原体生命率或宿主免疫反应的变化，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率的差异产生了选择压力，这些压力反馈到塑造病原体群体遗传学。因此，在生物尺度上将病原体动态联系起来对于了解宿主-病原体系统的进化轨迹至关重要，也是疾病生态学中的一个中心挑战。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种群尺度来应对这一挑战。近年来，发展数学工具来连接在非常不同的时间和空间尺度上运行的动态过程一直是传染病模型中的一个活跃的焦点。到目前为止，这些理论创新还没有与经验数据生成相匹配，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综合数据流，这些数据流在组织规模上一致收集。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，根据变异的遗传或表型特征预测新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，我们将使用一组口蹄疫病毒(FMDV)作为其水库宿主非洲水牛的模型系统来研究从基因组到景观尺度的病毒动力学。口蹄疫病毒是一种高传染性病毒，会在家养有蹄类动物中引起临床疾病，而野生动物宿主中的地方性感染往往是亚临床感染。口蹄疫病毒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式系统：(I)它们在普通野生宿主中普遍存在；(Ii)病毒多样性很高，三种不同的血清型基本上独立地在水牛种群中传播，每种血清型都记录了分化良好的谱系；(Iii)突变迅速增加，为分子追踪和系统动力学分析提供了高分辨率；(Iv)由于口蹄疫病毒作为牲畜病原体的重要性，该系统易于处理，建立了用于病毒培养、实验挑战、诊断和免疫反应量化的成熟方法。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，将跨组织规模的病毒动态联系起来，以测试是否以及如何根据病毒谱系之间的表型差异来预测宿主、种群和景观规模的动态。我们将在细胞、宿主内、种群和景观尺度上使用数学模型研究病毒动力学，这些模型来自实验、观察性实地研究和系统动力学分析。在我们的模型中，我们将通过允许较小尺度上的动力学来确定较大尺度上感染过程的参数来建立跨尺度的显式联系。

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.