论文标题
一种基于移动蜂窝网络的地理位置数据控制Covid-19的自动接触跟踪方法
An Automated Contact Tracing Approach for Controlling Covid-19 Spread Based on Geolocation Data from Mobile Cellular Networks
论文作者
论文摘要
冠状病毒(Covid-19)由于其连续的结构进化以及这种特定病毒的适当解毒剂而成为最大的挑战。该病毒主要通过密切接触来传播并复制大众之间,不幸的是,这可能以许多不可预测的方式发生。因此,为了减慢这种新型病毒的传播,唯一相关的举措是保持社交距离,进行接触跟踪,使用适当的安全齿轮并采取隔离措施。但是,尽管在理论上可能是可能的,但这些方法在人口稠密的国家和地区很难维持。因此,为了控制病毒传播,研究人员和当局正在考虑使用基于智能手机的移动应用程序(APP)来识别可能受感染的人以及高风险的区域,以维持隔离和锁定措施。但是,这些方法在很大程度上取决于先进的技术特征,并暴露了明显的隐私漏洞。在本文中,我们提出了一种基于移动电话用户的地理位置数据的COVID-19接触跟踪的新方法。提出的方法将帮助当局在不使用基于智能手机的移动应用程序的情况下确定可能受感染的人的数量。此外,提出的方法可以帮助人们做出何时寻求医疗援助的重要决定,让他们知道他们是否已经在暴露的人名单中。数值示例表明,所提出的方法可以显着胜过基于智能手机应用程序的解决方案。
The coronavirus (COVID-19) has appeared as the greatest challenge due to its continuous structural evolution as well as the absence of proper antidotes for this particular virus. The virus mainly spreads and replicates itself among mass people through close contact which unfortunately can happen in many unpredictable ways. Therefore, to slow down the spread of this novel virus, the only relevant initiatives are to maintain social distance, perform contact tracing, use proper safety gears, and impose quarantine measures. But despite being theoretically possible, these approaches are very difficult to uphold in densely populated countries and areas. Therefore, to control the virus spread, researchers and authorities are considering the use of smartphone based mobile applications (apps) to identify the likely infected persons as well as the highly risky zones to maintain isolation and lockdown measures. However, these methods heavily depend on advanced technological features and expose significant privacy loopholes. In this paper, we propose a new method for COVID-19 contact tracing based on mobile phone users' geolocation data. The proposed method will help the authorities to identify the number of probable infected persons without using smartphone based mobile applications. In addition, the proposed method can help people take the vital decision of when to seek medical assistance by letting them know whether they are already in the list of exposed persons. Numerical examples demonstrate that the proposed method can significantly outperform the smartphone app-based solutions.