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Background COVID-19 data have been generated across the United Kingdom as a by-product of clinical care and public health provision, as well as numerous bespoke and repurposed research endeavors. Analysis of these data has underpinned the United Kingdom’s response to the pandemic, and informed public health policies and clinical guidelines. However, these data are held by different organizations, and this fragmented landscape has presented challenges for public health agencies and researchers as they struggle to find relevant data to access and interrogate the data they need to inform the pandemic response at pace. Objective We aimed to transform UK COVID-19 diagnostic data sets to be findable, accessible, interoperable, and reusable (FAIR). Methods A federated infrastructure model (COVID - Curated and Open Analysis and Research Platform [CO-CONNECT]) was rapidly built to enable the automated and reproducible mapping of health data partners’ pseudonymized data to the Observational Medical Outcomes Partnership Common Data Model without the need for any data to leave the data controllers’ secure environments, and to support federated cohort discovery queries and meta-analysis. Results A total of 56 data sets from 19 organizations are being connected to the federated network. The data include research cohorts and COVID-19 data collected through routine health care provision linked to longitudinal health care records and demographics. The infrastructure is live, supporting aggregate-level querying of data across the United Kingdom. Conclusions CO-CONNECT was developed by a multidisciplinary team. It enables rapid COVID-19 data discovery and instantaneous meta-analysis across data sources, and it is researching streamlined data extraction for use in a Trusted Research Environment for research and public health analysis. CO-CONNECT has the potential to make UK health data more interconnected and better able to answer national-level research questions while maintaining patient confidentiality and local governance procedures.

Gerry Reilly FBCS, Technologist in Residence and Susheel Varma, Director of Engineering at HDR UK, share the build process for the new HDR UK Innovation Gateway and explain why open source matters.

Since the emergence of the novel coronavirus SARS-CoV-2 in Wuhan, China in 2019 [1], the resulting COVID-19 disease has rapidly transitioned into a global pandemic with over 31 000 deaths and 215 000 infections in the UK as of 10th May 2020 [2]. Confirmation of SARS-CoV-2 infection requires reverse-transcriptase polymerase chain reaction (RT-PCR) testing [3]. Chest radiographic and/or computed tomography imaging is also central to diagnosis and management [4]., The National COVID-19 Chest Imaging Database (NCCID) is a repository of chest X-Ray, CT and MRI images and clinical data from COVID-19 patients across the UK, to support research and development of AI technology that may proffer insights into the disease.

Project Aligning approach to Trusted Research Environments Share this page Data Safe Havens are an essential way of managing risk of unauthorised re-identification of individuals from de-identified data. This risk heightens with increased data linkage across a wider range of data sets. To date, most of the focus of Data Safe Havens has been on the people, processes and technology required to deliver effective Information Security. Our aim is for Alliance Trusted Research Environments (TRE) to be more than Data Safe Havens by meeting researchers’ technical and functionality requirements both now and in the future, whilst providing confidence to data custodians and the public through implementing a rigorous system of data access based on an evolution of the Five Safes model. This evolution is required to meet research needs for Machine Learning capability, High Performance Computing or High Throughput Computing e.g., for analysis of genetic and imaging data, and also to support the convergence of research and care to enable a future of personalised medicine.&nbsp

The increasing global prevalence of dementia and the lack of disease-modifying treatments give rise to the need for early detection of dementia-causing diseases to enable the development and targeted administration of preventative interventions. However, current methods that have potential for the early detection of dementia-causing diseases, such as positron emission tomography or cerebrospinal fluid sampling, are invasive and costly, which constitutes a barrier to the large-scale assessment of dementia risk. The Early Detection of Neurodegenerative diseases (EDoN) initiative was established by Alzheimer’s Research UK to address this challenge. As part of EDoN, digital data and low-burden clinical measures, such as blood tests, will be collected in thousands of people to create machine learning models that can detect specific dementia-causing diseases decades before noticeable cognitive symptoms arise. This effort will be supported by the development of a data platform and a digital toolkit (likely consisting of wearables and smartphone applications) that will collect active and passive physiological and behavioural measures (e.g. cognition, mood, heart rate, gait, sleep, and navigation). After extensive testing, EDoN aims to introduce the digital toolkit into annual health checks, which will allow for the early detection of dementia-causing diseases in a cost-effective, low-burden manner on a population-wide scale. Moreover, the information derived from the digital data will be used to inform lifestyle changes and to triage and stratify individuals into clinical trials or further targeted medical testing. As such, the outputs of the EDoN initiative will benefit the public, patients, carers, and clinicians, as well as the broader healthcare system and the pharmaceutical industry.