Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.
Department of Networks and Communications, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.
Sensors (Basel). 2021 Mar 24;21(7):2282. doi: 10.3390/s21072282.
The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.
COVID-19 疫情在全球范围内对经济、社会、公共卫生系统造成了大量人员伤亡和严重破坏。控制此类疫情需要了解其特征和行为，可以通过收集和分析相关大数据来识别。大数据分析工具在构建决策和预防措施所需的知识方面发挥着至关重要的作用。然而，由于 COVID-19 相关大数据的来源众多，数据量庞大，因此需要审查大数据分析在控制 COVID-19 传播方面的作用，提出 COVID-19 数据分析的主要挑战和方向，并提供一个关于现有相关应用和研究的框架，以促进未来 COVID-19 分析的研究。因此，在本文中，我们进行了文献综述，以突出强调基于 COVID-19 的大数据分析领域的几个研究的贡献。该研究提出了几种用于管理和控制大流行的应用程序的分类法。此外，本研究还讨论了在分析 COVID-19 数据时遇到的几个挑战。本文的研究结果为进一步研究和应用提出了有价值的未来方向。
