Department of Computer Science, University of Oxford, UK; The Open Data Institute, London, UK; Corresponding author at: Department of Computer Science, University of Oxford, UK.
Alys Brett
UKAEA Software Engineering Group, UK; Scottish COVID-19 Response Consortium, UK
Min Chen
Department of Engineering Science, University of Oxford, UK; Scottish COVID-19 Response Consortium, UK
Glenn Marion
Biomathematics and Statistics Scotland, Edinburgh, UK; Scottish COVID-19 Response Consortium, UK
Iain J. McKendrick
Biomathematics and Statistics Scotland, Edinburgh, UK; Scottish COVID-19 Response Consortium, UK
Jasmina Panovska-Griffiths
The Big Data Institute, University of Oxford, UK; The Wolfson Centre for Mathematical Biology, University of Oxford, UK; The Queen’s College, University of Oxford, UK
Lorenzo Pellis
Department of Mathematics, University of Manchester, UK; The Alan Turing Institute, London, UK
Richard Reeve
Institute of Biodiversity Animal Health & Comparative Medicine, University of Glasgow, UK; Scottish COVID-19 Response Consortium, UK
Ben Swallow
School of Mathematics and Statistics, University of Glasgow, UK; Scottish COVID-19 Response Consortium, UK
The use of data has been essential throughout the unfolding COVID-19 pandemic. We have needed it to populate our models, inform our understanding, and shape our responses to the disease. However, data has not always been easy to find and access, it has varied in quality and coverage, been difficult to reuse or repurpose. This paper reviews these and other challenges and recommends steps to develop a data ecosystem better able to deal with future pandemics by better supporting preparedness, prevention, detection and response.
