A theory of how epidemic dynamics shape pathogen phylogenies

下一代测序技术现在使许多生物体的基因组，包括许多传染性病原体，能够快速和廉价地测序。大量的序列数据正在产生的重要传染病，包括病毒，如艾滋病毒和流感，现在也为一些细菌，包括C。difficile，S. aureus和M.结核这些数据将被用来了解病原体是如何传播和进化的，包括它们是如何适应疫苗的引入，以及耐药性是如何、在哪里以及以多快的速度出现的。虽然流行病学的数学理论是研究感染的主要建模工具，但它主要集中在理解单一静态病原体的传播动力学，例如，根据其基本繁殖数（定义为在完全易感人群中由单个感染病例引起的继发感染的数量）。但这一理论对病原体的进化几乎没有提供什么，更不用说如何预测、建模和解释大量可用的遗传数据了。虽然有一些数学模型关注性状进化，但这些模型并不关注复杂的流行动力学，也没有明确预测序列数据的特征。相比之下，数学群体遗传学有着悠久的传统，旨在回答诸如等位基因频率如何维持等问题，但这些问题并没有解释当病原体群体受到人类宿主中流行动力学的约束时出现的各种结构。在这个建议中，我的目标是弥合这一差距，并发展一个理论，病原体序列是如何与潜在的流行动力学。描述流行动力学和病原体序列数据之间的关系需要新的数学，首先是描述两者的模型的构建和分析，然后是将流行动力学的结构与用于总结序列数据的系统发育树的定量特征相关联的定理。随着基因组数据的日益可用，这些理论工具将至关重要。该提案提供了一种方法，通过四个核心目标在这一方向上取得实质性进展，从几个不同的，固定的病原体菌株之间的竞争，从而产生的病原体，明确建模宿主传播和病原体进化之间的关系。我还将开发更好的定量措施，以确定不同数据集上系统发育树之间的相似性。这项工作将提供一个理论基础连接流行病的人口动态序列数据，并将有广泛的应用，除了其新的理论发展。目前，许多病原体的序列数据正在迅速生成，对这些数据的分析预计不仅有助于我们了解病原体的演变，而且有助于我们干预公共卫生的能力。例如，英国CRC现代化医学微生物学联盟正在使用全基因组测序技术与基于人群的采样相结合，重点关注M。tb、诺如病毒、C.梭茵和鼠伤寒沙门金黄色。我还被要求为一项以社区为基础的研究提供建模专业知识，在这项研究中，艾滋病毒序列数据将与个人的性网络数据一起收集，为将传播模式与病毒序列联系起来提供独特的数据集。我在这项资助下的工作将为这些数据对这些病原体如何传播的意义提供新的见解，这反过来将为研究人员和卫生政策制定者提供信息，以设计改进的预防措施。

Next-generation sequencing technology is now enabling the genomes of many organisms, including many infectious agents, to be sequenced quickly and inexpensively. Vast amounts of sequence data are being generated for important infectious diseases, including viruses such as HIV and influenza and now also for a number of bacteria, including C. difficile, S. aureus and M. tuberculosis. These data will be used to understand how pathogens are spreading and evolving, including how they are adapting to the introduction of vaccines and how, where, and how quickly drug resistance is emerging.While there is a mathematical theory of epidemiology which is the primary modelling tool for the study of infections, it has largely been focussed on understanding the spreading dynamics of a single, static pathogen, for example in terms of its basic reproduction number (defined as the number of secondary infections caused by a single infectious case in a fully susceptible population). But this theory offers very little about pathogen evolution, and still less about how to predict, model and interpret the vast quantities of genetic data that are becoming available. While there are mathematical models focussing on trait evolution, these do not focus on complex epidemic dynamics, and also do not explicitly predict features of sequence data. In contrast, there is a long tradition of mathematical population genetics aiming to answer questions such as how allele frequency is maintained, but these do not account for the kinds of structure that arise when the pathogen population is constrained by epidemic dynamics in human hosts. In this proposal, I aim to bridge this gap, and develop a theory of how pathogen sequences are related to the underlying epidemic dynamics. Characterising the relationship between epidemic dynamics and pathogen sequence data will require new mathematics, first in the form of the construction and analysis of models describing both, and then in the form of theorems relating the structure of epidemic dynamics to quantitative features of the phylogenetic trees used to summarise sequence data. These theoretical tools will be crucial as genomic data become increasingly available. This proposal provides an approach to making substantial progress in this direction through four core Objectives, moving from relating competition between several distinct, fixed pathogen strains to the resulting phylogenies, to explicitly modelling the relationship between host transmission and pathogen evolution. I will also develop better quantitative measures to identify similarities between phylogenetic trees on different datasets. This work will provide a theoretical underpinning linking epidemic population dynamics to sequence data, and will have a wide range of applications in addition to its new theoretical developments. Sequence data for many pathogens are currently being generated rapidly, and the analysis of these data is expected to benefit not only our understanding of pathogen evolution, but our ability to intervene for the benefit of public health. For example, the UK CRC Modernising Medical Microbiology Consortium is using whole-genome sequencing technology combined with population-based sampling, focusing on M. tb, norovirus, C. difficile and S. aureus. I have also been asked to provide modelling expertise for a community-based study in which HIV sequence data will be collected alongside individuals' sexual network data, providing a unique dataset for linking spreading patterns to viral sequences. My work under this grant would provide new insights into what these data mean for how these pathogens are spreading, which in turn will provide researchers and health policy makers with information to design improved prevention measures.