Enhancing Understanding of Long COVID Using Novel Mathematical Clustering Techniques

使用新颖的数学聚类技术增强对长期新冠肺炎的理解

• 批准号: 2738361
• 金额: --
• 依托单位: Plymouth University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2738361
• 关键词: Enhancing; Understanding; Long; COVID; Using

The COVID pandemic continues to have a detrimental impact on lives. As of January 2022, the ONS estimated that there are 1.5 million people in the UK experiencing Long COVID. Around 65% of these have symptoms which lead to an adverse effect on day-to-day activities.Understanding of Long COVID is however still in its early stages and there are many unanswered questions. Can we group people by severity of their symptoms and how long they persist? Are different variants of the COVID-19 virus related to different symptoms? Can we identify factors associated with more debilitating forms of Long COVID?This intradisciplinary project will bring together techniques from pure mathematics, statistics and data science to address these questions, in full collaboration with clinicians and patients. It will make use of data from cohort studies and electronic records.The project will develop ideas from graph theory and topological data analysis to produce groups of similar patients called clusters. These will allow the identification of Long COVID sufferers and of factors indicating an increased Long COVID risk. The project will also provide easy-to-use computer tools to implement and visualize the methodology. Appropriate training in pure mathematics, statistics or computing will be provided.The project's results will benefit the NHS, allowing treatments and rehabilitation to be targeted at the most affected people.

新型冠状病毒疫情继续对生活造成不利影响。截至2022年1月，英国国家统计局估计，英国有150万人经历了长期的COVID。其中约65%的人有症状，导致对日常活动产生不利影响。然而，对Long COVID的了解仍处于早期阶段，有许多未解答的问题。我们能根据症状的严重程度和持续时间对患者进行分组吗？COVID-19病毒的不同变种是否与不同症状有关？我们能否确定与更令人衰弱的新型冠状病毒相关的因素？这个跨学科项目将汇集纯数学、统计学和数据科学的技术，与临床医生和患者充分合作，解决这些问题。该项目将利用来自队列研究和电子记录的数据，从图论和拓扑数据分析中发展思想，产生类似患者的组群，称为聚类。这些将允许识别长期COVID患者和表明长期COVID风险增加的因素。该项目还将提供易于使用的计算机工具，以执行和形象化该方法。将提供纯数学、统计学或计算方面的适当培训，该项目的成果将使国民保健服务受益，使受影响最严重的人能够得到治疗和康复。