Deep Poisson process pathogen phylodynamics to accelerate understanding in disease transmission

深度泊松过程病原体系统动力学加速了解疾病传播

• 批准号: EP/X038440/1
• 负责人: Oliver Ratmann
• 金额: $ 10.23万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX038440%2F1
• 关键词: Deep; Poisson; process; pathogen; phylodynamics

The discovery of deciphering the human genetic code has been a landmark scientific achievement, leading to the development of personalized medicine, gene therapies, or modern vaccines. Today, the genetic codes of major organisms, and viral or bacterial pathogens that compromise human health are identified (or sequenced) at low cost and at industrial scale, including for the purpose of reconstructing how infectious diseases spread in human populations, and how to stop spread. The genetic relationships of pathogen variants provide objective data about who infected who, information that is otherwise hard to obtain. The mathematical and statistical theory that underlies the analysis of these data is called 'phylodynamics'. This theory has made possible to reconstruct and quantify how anti-microbial resistant pathogens have spread worldwide, in communities, or in hospital wards, or how novel COVID-19 variants emerge and replace each other. This EPSRC project aims to develop a novel class of statistical phylodynamic theory, grounded in deep Poisson point processes, that are substantially more flexible and computationally faster than existing methods. Our preliminary findings indicate this approach has the potential to unlock the analysis of important questions about the age, behavioural characteristics, locations, mobility patterns or other characteristics of population groups that are the sources of pathogenic spread, and which to date are very challenging or impossible to address. We will develop the statistical theory and provide open-access and computationally scalable code for flexible and reproducible analyses. This project will benefit from close ties to the Machine Learning & Global Health network (development of deep non-parametric methods), the international PANGEA-HIV consortium (access to large-scale, rich and globally important data collected over the past 10 years), the UK Health Security Agency (aiming to use our methods in the UK) and to Oxford Nanopore (transitional industry impact).

破译人类遗传密码的发现是一项具有里程碑意义的科学成就，导致了个性化医疗，基因疗法或现代疫苗的发展。今天，危害人类健康的主要生物体和病毒或细菌病原体的遗传密码以低成本和工业规模进行鉴定（或测序），包括为了重建传染病如何在人群中传播以及如何阻止传播。病原体变异的遗传关系提供了关于谁感染了谁的客观数据，这些信息很难获得。分析这些数据所依据的数学和统计理论被称为“非线性动力学”。这一理论使得重建和量化抗微生物耐药病原体如何在全球、社区或医院病房中传播，或者新的COVID-19变体如何出现并相互取代成为可能。这个EPSRC项目旨在开发一类新的统计非线性动力学理论，基于深泊松点过程，比现有方法更灵活，计算速度更快。我们的初步研究结果表明，这种方法有可能解开有关年龄，行为特征，地点，流动模式或人口群体的其他特征的重要问题的分析，这些问题是病原体传播的来源，迄今为止非常具有挑战性或不可能解决。我们将开发统计理论，并提供开放访问和计算可扩展的代码，用于灵活和可重复的分析。该项目将受益于与机器学习和全球健康网络（深度非参数方法的开发），国际PANGEA-HIV联盟（获得过去10年收集的大规模，丰富和全球重要的数据），英国卫生安全局（旨在在英国使用我们的方法）和牛津纳米孔（过渡行业影响）的密切联系。