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.

Competing Interests: Declaration of interests All coauthors (except EF) received funding from the Bill & Melinda Gates Foundation and Minderoo Foundation for the ICODA initiative. KLD received grant funding for the main project from the European and Developing Countries Clinical Trials Partnership (EDCTP) 2 programme supported by the European Union (RIA2020EF-2926 periCOVID Africa). LFR received grants from Pfizer and Merck Sharp & Dohme; consulting fees from Merck Sharp & Dohme, Pfizer, and GSK; payment for lectures and presentations from Merck Sharp & Dohme and GSK; and payment for expert testimony from Merck Sharp & Dohme, Pfizer, and GSK. MYI received grant funding from the Ministry of Health (decentralised executive term, process number 25000200517/2019-46), National Institute for Health and Care Research (NIHR), and the Wellcome Trust; and payment to participate in the International Population Data Linkage Network Conference from the NIHR Global Health Research Program Award. MB received grants from the London School of Economics, The Rockefeller Foundation, Global Challenges Research Fund Global Multimorbidity, and Google; has received consultancy fees from the Singapore Institute of Management; and has a patent from the Institute of Electrical and Electronics Engineers Standards Association. AF has received grants from the Ministry of Health, NIHR, and the Wellcome Trust. GSH received funding from the Gates Foundation contract for contract work unrelated to the contents of the manuscript; consultancy fees from Vivli clinical data sharing platform and ClinicalStudyDataRequest.com clinical data sharing platform; and fees from the National Institute on Ageing to present at their data sharing workshop. All other authors declare no competing interests.

(Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)