Background: COVID-19 data have been generated across the UK as a by-product of clinical care and public health provision, and numerous bespoke and repurposed research endeavours. Analysis of these data has underpinned the UK's response to the pandemic and informed public health policies and clinical guidelines. However, these data are held by different organisations and this fragmented landscape has presented challenges for public health agencies and researchers as they struggle to find, navigate permissions to access and interrogate the data they need to inform the pandemic response at pace.

Objective: To transform UK COVID-19 diagnostic datasets to be Findable, Accessible, Interoperable and Reusable (FAIR).

Methods: A federated infrastructure model was rapidly built to enable the automated and reproducible mapping of health Data Partners' pseudonymised data to the OMOP 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: 56 datasets from 19 organisations are being connected to the federated network. The data includes research cohorts and COVID-19 data collected through routine health care provision linked to longitudinal healthcare records and demographics. The infrastructure is live, supporting aggregate level querying of data across the UK.

Conclusions: CO-CONNECT was developed by a multidisciplinary team enabling rapid COVID-19 data discovery, instantaneous meta-analysis across data sources, and is researching streamlined data extraction for egress into a Trusted Research Environment (TRE) 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 whilst maintaining patient confidentiality and local governance procedures.


This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Cite as

Jefferson, E., Cole, C., Mumtaz, S., Cox, S., Giles, T., Adejumo, S., Urwin, E., Lea, D., McDonald, C., Best, J., Masood, E., Milligan, G., Johnston, J., Horban, S., Birced, I., Hall, C., Jackson, A., Collins, C., Rising, S., Dodsley, C., Hampton, J., Hadfield, A., Santos, R., Tarr, S., Panagi, V., Lavagna, J., Jackson, T., Chuter, A., Beggs, J., Martinez-Queipo, M., Ward, H., von Ziegenweidt, J., Burns, F., Martin, J., Sebire, N., Morris, C., Bradley, D., Baxter, R., Ahonen-Bishop, A., Shoemark, A., Valdes, A., Ollivere, B., Manisty, C., Eyre, D., Gallant, S., Joy, G., McAuley, A., Connell, D., Northstone, K., Jeffery, K., Di Angelantonio, E., McMahon, A., Walker, M., Semple, M., Sims, J., Lawrence, E., Davies, B., Baillie, J., Tang, M., Leeming, G., Power, L., Breeze, T., Gilson, N., Murray, D., Orton, C., Pierce, I., Hall, I., Ladhani, S., Whitaker, M., Shallcross, L., Seymour, D., Varma, S., Reilly, G., Morris, A., Hopkins, S., Sheikh, A. & Quinlan, P. 2022, 'Co-connect: A hybrid architecture to facilitate rapid discovery and access to UK wide data in the response to the COVID-19 pandemic', Journal of Medical Internet Research, 24(12), article no: e40035. https://doi.org/10.2196/40035

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Last updated: 02 November 2023
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