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Data challenges for international health emergencies: lessons learned from ten international COVID-19 driver projects.

The COVID-19 pandemic highlighted the importance of international data sharing and access to improve health outcomes for all. The International COVID-19 Data Alliance (ICODA) programme enabled 12 exemplar or driver projects to use existing health-related data to address major research questions relating to the pandemic, and developed data science approaches that helped each research team to overcome challenges, accelerate the data research cycle, and produce rapid insights and outputs.

These approaches also sought to address inequity in data access and use, test approaches to ethical health data use, and make summary datasets and outputs accessible to a wider group of researchers. This Health Policy paper focuses on the challenges and lessons learned from ten of the ICODA driver projects, involving researchers from 19 countries and a range of health-related datasets.

The ICODA programme reviewed the time taken for each project to complete stages of the health data research cycle and identified common challenges in areas such as data sharing agreements and data curation. Solutions included provision of standard data sharing templates, additional data curation expertise at an early stage, and a trusted research environment that facilitated data sharing across national boundaries and reduced risk.

These approaches enabled the driver projects to rapidly produce research outputs, including publications, shared code, dashboards, and innovative resources, which can all be accessed and used by other research teams to address global health challenges.

You can find the full text for the publication below.

Boylan S, Arsenault C, Barreto M, Bozza FA, Fonseca A, Forde E, Hookham L, Humphreys GS, Ichihara MY, Le Doare K, Liu XF, McNamara E, Mugunga JC, Oliveira JF, Ouma J, Postlethwaite N, Retford M, Reyes LF, Morris AD, Wozencraft A

Lancet Digit Health. 2024 May;6(5):e354-e366

doi: 10.1016/S2589-7500(24)00028-1

PMID: 38670744

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