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Details of Grant 

EPSRC Reference: EP/W015153/1
Title: WELD: Integrated Cyber-Infrastructure for Scalable Data-Driven Research into COVID-19
Principal Investigator: De Roure, Professor D
Other Investigators:
Parsons, Professor M thompson, Mr s Watry, Professor P
Davies, Professor J Baxter, Dr R
Researcher Co-Investigators:
Project Partners:
Department: Engineering Science
Organisation: University of Oxford
Scheme: Standard Research
Starts: 01 August 2021 Ends: 31 January 2023 Value (£): 1,201,172
EPSRC Research Topic Classifications:
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:  
Summary on Grant Application Form
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.
Key Findings
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Summary
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Organisation Website: http://www.ox.ac.uk