WELD: Integrated Cyber-Infrastructure for Scalable Data-Driven Research into COVID-19

WELD：用于可扩展数据驱动的 COVID-19 研究的集成网络基础设施

• 批准号: EP/W015153/1
• 负责人: David De Roure
• 金额: $ 153.05万
• 依托单位: University of Oxford
• 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%2FW015153%2F1
• 关键词: WELD; Integrated; Cyber; Infrastructure; Scalable

The project will implement a unifying cyberinfrastructure based on automated machine-actionable policies that will enable data-driven clinical and medical research across the four nations and produce evidence essential for making rapid decisions on the UK's response to the COVID-19 outbreak. The research explores the development of integrated computing, modelling, simulation, and information technologies as the basis of cross-disciplinary research for collaborating teams investigating COVID-19 and SARS-CoV-2. The application will implement a "cloud native" software infrastructure for federating distributed healthcare data across the Trusted Research Environments and include methods for working with ensemble collections of data. The result will quantifiably improve existing model- and statistically- based methodologies for evaluating vaccine efficacy and risk prediction using live NHS data in clinical research environments. A unique contribution will be "policy-based" data federation techniques that allow each devolved nation or Trusted Research Environment to control its use of a shared UK-wide data collection, across regulatory boundaries, while guarding against data exfiltration. This capability, which has never been addressed, is fundamental to providing a secure evidence base for effective data analysis and scientific discovery of COVID-19 at UK-wide levels. The methodology draws on state-of-the-art in data abstractions and virtualisation levels, provides greater flexibility for automation and re-use across the lifecycle, and addresses the need for a more systematic, community-based approach to data and metadata as part of a shared solution. The architectural model will instruct HDR-UK strategic decision-making responding to COVID-19 and future pandemics that require an improved technology readiness level.

该项目将基于自动化机器可操作的政策实施统一的网络基础设施，这将使四个国家的数据驱动的临床和医学研究成为可能，并为英国应对COVID-19疫情做出快速决策提供必要的证据。该研究探讨了综合计算、建模、模拟和信息技术的发展，作为合作团队调查COVID-19和SARS-CoV-2的跨学科研究的基础。该应用程序将实现一个“云原生”软件基础设施，用于在可信研究环境中联合分布式医疗数据，并包括使用数据集合的方法。该结果将量化地改进现有的基于模型和统计的方法，用于在临床研究环境中使用实时NHS数据评估疫苗有效性和风险预测。一个独特的贡献将是“基于政策”的数据联合技术，允许每个下放国家或可信的研究环境，以控制其使用共享的英国范围内的数据收集，跨越监管边界，同时防止数据泄露。这种能力从未得到解决，对于在英国范围内为有效的COVID-19数据分析和科学发现提供安全的证据基础至关重要。该方法借鉴了最先进的数据抽象和虚拟化水平，为整个生命周期的自动化和重用提供了更大的灵活性，并满足了对数据和元数据的更系统化，基于社区的方法的需求，作为共享解决方案的一部分。该架构模型将指导HDR-UK应对COVID-19和未来需要提高技术准备水平的流行病的战略决策。