Data-driven analytics of disease spread under close contact for optimal testing and mitigation.

对密切接触下疾病传播进行数据驱动分析，以实现最佳检测和缓解。

• 批准号: 2272545
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2272545
• 关键词: Data; driven; analytics; disease; spread

Outbreaks of infection in close contact settings can have a major impact on the population involved. Examples include: blood-borne diseases in prisons; gastrointestinal illness at a major sporting event or on a cruise ship; and scabies in care homes.Enhanced techniques for early detection, and the deployment thereof, may enable better responses to such outbreaks. This project will work with multiple sources of data to build models for an exemplar set of diseases and contexts, and in doing so develop a general software framework for modelling tool creation to enable scenario planning for close-contact outbreaks.The work will be carried out in collaboration with Danaher (manufacturers of the GeneXpert System that is commonly known as the "Cepheid Machine"), who will ensure that the latest information on current and future fast diagnostic technologies is available to the student, and who will benefit from the availability of a modelling tool that can determine in which contexts faster diagnostics are going to be most beneficial to patients, healthcare and care systems, which will inform product development and marketing.The student will join a lively group of mathematical epidemiologists in the School of Mathematics at the University of Manchester (3 Faculty - House, Hall and Pellis, 3 Postdoctoral, 8 PhD) and will have the opportunity to collaborate with researchers in this group funded by the Alan Turing Institute for Data Science and Artificial Intelligence working on complementary projects. Dr. Ustianowski will also provide access to a rich collection of clinical research organisations in Greater Manchester and beyond (e.g. North Manchester General Hospital research, Greater Manchester Comprehensive Research Network, NIHR Infection National Specialty Group).Skills to be developed and trained during the project include mathematical modelling, statistical inference, working with efficient, modern computational algorithms, incorporation of economic, industrial and clinical aspects into epidemiological models.

在密切接触环境中爆发感染可对相关人群产生重大影响。示例包括：加强早期检测技术及其部署，可以更好地应对此类疾病的爆发。该项目将利用多个数据来源，为一组典型疾病和背景建立模型，并在此过程中开发一个通用软件框架，用于创建建模工具，以便为密切接触疫情的爆发进行情景规划。（GeneXpert系统（通常称为“Cepheid机器”）的制造商），谁将确保最新的信息，目前和未来的快速诊断技术是提供给学生，以及谁将受益于建模工具的可用性，该建模工具可以确定在哪些情况下更快的诊断将对患者最有益，医疗保健和护理系统，这将为产品开发和营销提供信息。该学生将加入曼彻斯特大学数学学院的数学流行病学家活跃小组（3名教师-众议院，大厅和佩利斯，3博士后，8博士），并将有机会与该小组的研究人员合作，该小组由艾伦图灵数据科学和人工智能研究所资助，致力于互补项目。Ustianowski博士还将提供访问大曼彻斯特及其他地区丰富的临床研究组织的机会（例如，北曼彻斯特总医院研究，大曼彻斯特综合研究网络，NIHR感染国家专业组）。在项目期间开发和培训的技能包括数学建模，统计推断，使用高效的现代计算算法，将经济，工业和临床方面的流行病学模型。