Genomic epidemiology of infectious disease outbreaks

病原体全基因组测序的进步和日益普及为传染病流行病学提供了一个机会，以扩大目前用于对抗传染病暴发的工具集。由于基因组包含可以获得的关于分离株之间关系的完整信息，因此围绕基因组开发的方法可以使我们深入了解疫情的历史，帮助我们分析正在发生的疫情，并能够发现隐藏的疫情，例如与特定病原体菌株对抗生素产生耐药性有关的疫情。本博士课程将致力于解决三个主要主题。发现疫情并快速量化缓解策略对病原体种群的影响是传染病流行病学中的一个重要问题。第一个主题将侧重于局部系统动力学。系统动力学方法使用凝聚过程和其他随机过程从基因组数据样本中推断病原体的种群大小历史。然而，几乎所有现有的系统动力学方法都假定整个种群遵循相同的动态历史。该主题将侧重于开发模型和统计方法，以推断和检测病原体种群中不同亚群的变化。从统计学的角度来看，现有的贝叶斯系统动力学方法往往效率低下，而且没有以任何方式得出将机械模型考虑在内的先验。第二个主题将集中于调查统计上有效的贝叶斯系统动力学方法，以及流行病学模型如何帮助为系统动力学提供更好和更有原则的先验。新的推理方法（例如：粒子MCMC）将被应用于改进现有的系统动力学工具。在传染病流行病学中，考虑空间和时间信息往往是很重要的。遗传数据本身并不固有地编码任何此类信息，而是直接受其影响。第三个主题将涉及将病原体的基因测序数据与空间统计数据相结合，以便更好地重建病原体在不同地点之间传播的地理路线。研究背景-基因组数据越来越多，具有补充流行病学数据的强大潜力，有助于我们了解和控制传染病。然而，由于缺乏将基因组数据整合到流行病学框架中的方法，这种潜力目前尚未实现。研究的目的和目标-该项目的目的是开发分析传染病基因组数据的新方法。这包括对过去种群规模的推断，具有不同系统动力学特性的谱系的检测，以及传播的地理路线的重建。研究方法的新颖性-该项目基于新颖的系统动力学模型，并利用最新的方法来推断这些模型下的参数。潜在的影响，应用和好处-这些方法将与我们的外部合作伙伴PHE合作应用于多个数据集。这些方法将作为开源软件实现并发布，这将对越来越多从事基因组流行病学领域工作的科学家有用。研究与职权范围的关系——该项目是高度跨学科的，利用最新的数学、统计和计算方法揭示传染病流行病学和公共卫生方面的见解。研究领域;医疗保健技术，数学科学外部合作伙伴- PHE/NIHP

Advances and the growing accessibility of whole genome sequencing of pathogens presents an opportunity for infectious disease epidemiology to expand the toolset currently used to combat infectious disease outbreaks. As the genome contains the complete information that can be obtained about the relationships between isolates, methods developed around the genome can provide us with insight into the history of an outbreak, help us analyse ongoing outbreaks, and enable the detection of hidden outbreaks, associated for example with a particular strain of a pathogen gaining resistance to an antibiotic. This PhD will aim to address three main themes.Detecting outbreaks, and rapidly quantifying the effect of a mitigation strategy on a pathogen population is an important problem in infectious disease epidemiology. The first theme will focus on local phylodynamics. Phylodynamic methods uses the coalescent process and other stochastic processes to infer the population size history of a pathogen from a sample of genomic data. However, almost all existing phylodynamic methods assume that the whole population follows the same dynamical history. This theme will focus on developing models and statistical methodologies to infer and detect changes within separate subsets of a pathogen population.From a statistical point of view, existing Bayesian phylodynamic methods are often inefficient, and the priors not elicited in any way that would take a mechanistic model into consideration. The second theme will focus on investigating statistically efficient Bayesian phylodynamic methodologies, as well as how epidemiologic models can help provide better and more principled priors for phylodynamics. New methods of inference (e.g.: particle MCMC) will be applied to improve upon existing phylodynamic tools.In infectious disease epidemiology, it is often important to consider spatial as well as temporal information. Genetic data itself does not inherently encode any such information itself but is directly affected by it. The third theme will involve integrating genetic sequencing data of pathogens with spatial statistics to enable a better reconstructing of the geographical routes by which a pathogen spread between locations.The context of the research - Genomic data is increasingly available and has strong potential to complement epidemiological data to help us understand and control infectious disease. However, this potential is currently unrealised due to a lack of methodology that integrate genomic data into an epidemiological framework.The aims and objectives of the research - The aims of this project are to develop new methods of analysis for genomic data of infectious diseases. This includes the inference of past population sizes, the detection of lineages with different phylodynamic properties, and the reconstruction of geographical routes of spread.The novelty of the research methodology - The project is based on novel phylodynamic models, and makes use of the latest methods for the inference of parameters under these models.The potential impact, applications, and benefits - The methods will be applied to several datasets in collaboration with our external partner, PHE. The methods will be implemented and released as open source software which will be useful for the increasingly large number of scientists working in the field of genomic epidemiology.How the research relates to the remit - The project is highly interdisciplinary, making use of the latest mathematical, statistical and computational methods to reveal insights in infectious disease epidemiology and public health.Research areas; Healthcare technologies, Mathematical SciencesExternal Partner - PHE/NIHP