Analysing group testing algorithms for COVID-19 surveillance and case identification in UK schools, universities, and health and social care settings

分析英国学校、大学以及卫生和社会护理机构中用于 COVID-19 监测和病例识别的群体测试算法

• 批准号: EP/W000032/1
• 负责人: David Ellis
• 金额: $ 10.88万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW000032%2F1
• 关键词: Analysing; group; testing; algorithms; COVID

In the context of a pandemic disease such as Covid-19, group testing (also known as 'pooled testing' or 'batch testing') provides a way of identifying infected individuals in a large group, by pooling samples together in different ways and testing the pooled samples. Group-testing algorithms can yield large efficiency gains over individual testing, when the number of infected individuals (i.e., the prevalence) is relatively low. This project will analyse whether and how strategies based on group-testing algorithms may be effectively used for monitoring Covid-19 prevalence, and for case-identification (i.e., screening of asymptomatics to identify infected individuals), in UK schools, universities, care homes and health care settings. Close attention will be paid to all the practically relevant issues: the impacts on public health and education-loss, resource requirements/costs, the practical ease or difficulty of implementing different strategies in situations of changing (and geographically varying) prevalence, the feasibility of incorporating a pooling step into existing UK testing systems, and ethical/anonymity requirements. As well as producing technical preprints and papers, we will produce non-technical reports aimed at policymakers and written in a way accessible to those without mathematical or scientific training, to aid decision-makers in determining which protocols to implement. Our initial focus will be on schools and universities, and later we will extend our study to health and social care settings.

在新冠肺炎等大流行性疾病的背景下，群体检测（也称为“混合检测”或“批量检测”）提供了一种在大群体中识别受感染个体的方法，方法是以不同的方式将样本汇集在一起，并对汇集的样本进行检测。当受感染个体的数量（即，患病率）相对较低。该项目将分析基于群体测试算法的策略是否以及如何有效地用于监测Covid-19流行率和病例识别（即，无症状筛查，以确定感染的个人），在英国的学校，大学，护理院和卫生保健机构。将密切关注所有实际相关的问题：对公共卫生和教育损失的影响，资源需求/成本，在不断变化的情况下（和地理位置不同）的患病率，将汇集步骤纳入现有的英国测试系统的可行性，和道德/匿名要求，实施不同的战略的实际容易或困难。除了制作技术预印本和论文外，我们还将制作针对政策制定者的非技术报告，并以那些没有数学或科学培训的人可以访问的方式编写，以帮助决策者确定实施哪些协议。我们最初的重点将是学校和大学，后来我们将扩大我们的研究，以健康和社会保健设置。

项目成果

Pooled testing to isolate infected individuals

汇总测试以隔离感染者

• DOI: 10.1109/ciss50987.2021.9400313
• 发表时间: 2021
• 作者: Aldridge M

Pandemics: Insurance and Social Protection

流行病：保险和社会保障

• DOI: 10.1007/978-3-030-78334-1_11
• 发表时间: 2022
• 作者: Aldridge M