Oladejo Sunday O, Watson Liam R, Watson Bruce W, Rajaratnam Kanshukan, Kotze Maritha J, Kell Douglas B, Pretorius Etheresia
School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada.
S Afr J Sci. 2023 May-Jun;119(5-6):73-80. doi: 10.17159/sajs.2023/14719. Epub 2023 May 30.
'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.
“长新冠”是用于描述新冠病毒感染康复患者持续出现长时间新冠病毒症状这一现象的术语。全球数百万人受到长新冠的影响。解决长新冠难题需要借鉴新冠疫情期间的经验教训，在疫情期间，来自流行病学、基因组学、医学、数据科学和计算机科学等不同学科的数千名专家开展合作，共享数据并集中资源，从多个角度攻克这一问题。到目前为止，对于长新冠的定义、诊断和最有效的治疗方法，全球尚未达成共识。在这项工作中，我们总体考察数据共享和数据科学的可能应用，以期最终更详细地了解长新冠，并为数百万受其影响的人加速缓解症状。我们查阅文献，调查长新冠研究中数据共享的现状、挑战和机遇。
