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.

COVID - 19大流行继续对人们的生活产生不利影响。截至2022年1月，英国国家统计局估计，英国有150万人患有长冠肺炎。其中约65%的人会出现对日常活动产生不利影响的症状。然而，对新冠肺炎的了解仍处于早期阶段，还有许多未解决的问题。我们能根据症状的严重程度和持续时间对人们进行分类吗？不同的COVID-19病毒变体与不同的症状相关吗？我们能否确定与更使人衰弱的长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来解决这些问题，与临床医生和患者充分合作。它将利用来自队列研究和电子记录的数据。该项目将从图论和拓扑数据分析中发展思想，以产生称为集群的相似患者组。这些将有助于识别长冠患者以及表明长冠风险增加的因素。该项目还将提供易于使用的计算机工具来实施和可视化该方法。将提供纯数学、统计学或计算机方面的适当培训。该项目的成果将使NHS受益，使治疗和康复能够针对受影响最严重的人群。