Lyu Hanjia, Imtiaz Arsal, Zhao Yufei, Luo Jiebo
Department of Computer Science, University of Rochester, Rochester, NY, United States.
Front Big Data. 2023 Apr 6;6:1099182. doi: 10.3389/fdata.2023.1099182. eCollection 2023.
Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups-using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.
自世界卫生组织(WHO)于2020年3月将新冠病毒病(COVID-19)定性为大流行病以来,截至2022年10月,全球已有超过6亿例COVID-19确诊病例,死亡人数超过600万。COVID-19大流行与人类行为之间的关系错综复杂。一方面,人们发现人类行为会影响疾病的传播。另一方面,大流行几乎在各个方面都对人类行为产生了影响甚至改变。为了全面理解人类行为与COVID-19大流行之间的复杂相互作用,研究人员一直在运用大数据技术,如自然语言处理、计算机视觉、音频信号处理、频繁模式挖掘和机器学习。在本研究中,我们概述了利用大数据技术研究COVID-19大流行时期人类行为的现有研究。具体而言,我们将这些研究分为三组,分别是利用大数据来衡量、建模和利用人类行为。相应地总结了相关任务、数据和方法。为了更深入了解如何抗击COVID-19大流行及未来的全球灾难,我们进一步讨论了挑战和潜在机遇。