Nisar Shibli, Wakeel Abdul, Tahir Wania, Tariq Muhammad
Department of Electrical EngineeringMilitary College of SignalsNational University of Sciences and Technology (NUST) Rawalpindi 46000 Pakistan.
Department of Electrical EngineeringBalochistan University of Information Technology, Engineering and Management Sciences (BUITEMS) Quetta 87300 Pakistan.
IEEE Sens J. 2022 Apr 25;23(2):922-932. doi: 10.1109/JSEN.2022.3170521. eCollection 2023 Jan.
Coronavirus (COVID-19) pandemic has incurred huge loss to human lives throughout the world. Scientists, researchers, and doctors are trying their best to develop and distribute the COVID-19 vaccine throughout the world at the earliest. In current circumstances, different tracking systems are utilized to control or stop the spread of the virus till the whole population of the world gets vaccinated. To track and trace patients in COVID-19 like pandemics, various tracking systems based on different technologies are discussed and compared in this paper. These technologies include, cellular, cyber, satellite-based radio navigation and low range wireless technologies. The main aim of this paper is to conduct a comprehensive survey that can overview all such tracking systems, which are used in minimizing the spread of COVID-19 like pandemics. This paper also highlights the shortcoming of each tracking systems and suggests new mechanisms to overcome such limitations. In addition, the authors propose some futuristic approaches to track patients in prospective pandemics, based on artificial intelligence and big data analysis. Potential research directions, challenges, and the introduction of next-generation tracking systems for minimizing the spread of prospective pandemics, are also discussed at the end.
冠状病毒（COVID-19）大流行给全世界人类生命造成了巨大损失。科学家、研究人员和医生正在尽最大努力尽早在全球研发和分发COVID-19疫苗。在当前情况下，人们利用不同的追踪系统来控制或阻止病毒传播，直到全世界所有人都接种疫苗。为了在类似COVID-19的大流行中追踪患者，本文讨论并比较了基于不同技术的各种追踪系统。这些技术包括蜂窝技术、网络技术、基于卫星的无线电导航技术和低范围无线技术。本文的主要目的是进行一项全面调查，概述所有此类用于尽量减少类似COVID-19大流行传播的追踪系统。本文还强调了每个追踪系统的缺点，并提出了克服这些限制的新机制。此外，作者基于人工智能和大数据分析，提出了一些未来在潜在大流行中追踪患者的方法。最后还讨论了潜在的研究方向、挑战以及为尽量减少潜在大流行传播而引入的下一代追踪系统。
