Data sharing: A Long COVID perspective, challenges, and road map for the future

Authors

  • Sunday O. Oladejo School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa https://orcid.org/0000-0001-9573-5189
  • Liam R. Watson 1.School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa; 2.David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada https://orcid.org/0000-0002-7016-9229
  • Bruce W. Watson School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa https://orcid.org/0000-0003-0511-1837
  • Kanshukan Rajaratnam School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa https://orcid.org/0000-0001-5916-2723
  • Maritha J. Kotze Division of Chemical Pathology, Department of Pathology, National Health Laboratory Service, Tygerberg Hospital & Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa https://orcid.org/0000-0002-6050-2876
  • Douglas B. Kell 1.Department of Biochemistry and Systems Biology, Faculty of Health and Life Sciences, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; 2.The Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Lyngby, Denmark; 3.Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, South Africa https://orcid.org/0000-0001-5838-7963
  • Etheresia Pretorius 1.Department of Biochemistry and Systems Biology, Faculty of Health and Life Sciences, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; 2.Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, South Africa https://orcid.org/0000-0002-9108-2384

DOI:

https://doi.org/10.17159/sajs.2023/14719

Keywords:

Long COVID, data sharing, data science

Abstract

‘Long COVID’ is the term used to describe the phenomenon in which patients who have survived a COVID-19 infection continue to experience prolonged SARS-CoV-2 symptoms. Millions of people across the globe are affected by Long COVID. Solving the Long COVID conundrum will require drawing upon the lessons of the COVID-19 pandemic, during which thousands of experts across diverse disciplines such as epidemiology, genomics, medicine, data science, and computer science collaborated, sharing data and pooling resources to attack the problem from multiple angles. Thus far, there has been no global consensus on the definition, diagnosis, and most effective treatment of Long COVID. In this work, we examine the possible applications of data sharing and data science in general with a view to, ultimately, understand Long COVID in greater detail and hasten relief for the millions of people experiencing it. We examine the literature and investigate the current state, challenges, and opportunities of data sharing in Long COVID research.

Significance:

Although millions of people across the globe have been diagnosed with Long COVID, there still exist many research gaps in our understanding of the condition and its underlying causes. This work aims to elevate the discussion surrounding data sharing and data science in the research community and to engage data sharing as an enabler to fast-track the process of finding effective treatment for Long COVID.

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Published

2023-05-30

How to Cite

Oladejo, S. O., Watson, L. R., Watson, B. W., Rajaratnam, K., Kotze, M. J., Kell, D. B., & Pretorius, E. (2023). Data sharing: A Long COVID perspective, challenges, and road map for the future. South African Journal of Science, 119(5/6). https://doi.org/10.17159/sajs.2023/14719

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Research Article
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