大流行期间接触者追踪和流动模式检测——基于轨迹聚类的方法

N. A., Sajimon Abraham
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引用次数: 0

摘要

目前已有大量技术可以应对大流行病局势带来的挑战。由于这类疾病通过人与人之间的接触或任何其他方式传播,世界卫生组织建议将地点跟踪和追踪感染者或与患者接触的人作为标准作业程序之一,并概述了事件管理规程。政府机构使用不同的输入,如智能手机信号和受访者的详细信息,来准备患者的旅行日志。他们追踪的每一个事件,如停留点、重访地点和会面点都很重要。传统的接触者追踪系统需要更多训练有素的工作人员和工具。在患者数量不断上升的情况下,可能不可能有时间限制地追踪主要和次要接触者,而且也有可能出现人为错误。在这种情况下,本文的目的是提出一种称为SemTraClus-Tracer的算法,这是一种计算个人移动并分析流行病传播可能性和地点脆弱性的有效方法。设计/方法/方法:流行病将世界推向生存危机。在此背景下,本文提出了一种称为SemTraClus-Tracer的算法,这是一种计算个体移动并分析流行病传播可能性和地点脆弱性的有效方法。该系统通过探索公众的日常流动性和活动,识别与感染者有关的多层次接触,并通过考虑可能导致病毒传播的重要因素提取语义信息。它根据一种称为“参与权重”的衡量标准对不同的地理位置进行分级,以便容易识别出脆弱的位置。本文给出了使用时空聚合查询提取社会流动一般特征的优势。该系统还通过梳理患者的医疗报告,方便了各种信息的生成。研究发现,运动的环境是重要的;因此,考虑停留点、接触点存在、主接触点停留时间和路点严重程度四个重要因素,对现有的SemTraClus算法进行了改进。可以根据权限的兴趣重新配置优先级。这种方法减少了追踪接触者的繁重任务。并对系统提供的不同功能进行了说明。由于没有真实的数据集,所以用类似的数据进行了实验,并给出了不同地理位置的不同类型的旅程的结果。该方法通过结合轨迹的各种相关语义,有效地处理计算运动和活动分析。在模型中加入基于集群的聚合查询,解决了处理整个移动数据的计算难题。研究的局限性/意义由于无法获得患者的轨迹,作者使用标准数据集进行实验,以达到目的。原创性/价值本文提出了一个框架基础设施,使应急响应团队能够根据跟踪的患者移动细节获取多种信息,并为减轻流行病的各种活动提供空间,例如预测热点、确定停留地点和建议主要和次要接触者的可能位置、创建热点集群和确定附近的医疗援助。该系统通过计算人们的流动性和识别人们旅行的地理位置的特征,提供了一种有效的活动分析方法。在制定框架时,作者审查了许多不同的实施计划和协议,并得出结论,所遵循的核心战略或多或少是相同的。为了作为参考模型,我们采用印度场景来定义这些概念。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Contact tracing and mobility pattern detection during pandemics - a trajectory cluster based approach
Purpose A wide number of technologies are currently in store to harness the challenges posed by pandemic situations. As such diseases transmit by way of person-to-person contact or by any other means, the World Health Organization had recommended location tracking and tracing of people either infected or contacted with the patients as one of the standard operating procedures and has also outlined protocols for incident management. Government agencies use different inputs such as smartphone signals and details from the respondent to prepare the travel log of patients. Each and every event of their trace such as stay points, revisit locations and meeting points is important. More trained staffs and tools are required under the traditional system of contact tracing. At the time of the spiralling patient count, the time-bound tracing of primary and secondary contacts may not be possible, and there are chances of human errors as well. In this context, the purpose of this paper is to propose an algorithm called SemTraClus-Tracer, an efficient approach for computing the movement of individuals and analysing the possibility of pandemic spread and vulnerability of the locations. Design/methodology/approach Pandemic situations push the world into existential crises. In this context, this paper proposes an algorithm called SemTraClus-Tracer, an efficient approach for computing the movement of individuals and analysing the possibility of pandemic spread and vulnerability of the locations. By exploring the daily mobility and activities of the general public, the system identifies multiple levels of contacts with respect to an infected person and extracts semantic information by considering vital factors that can induce virus spread. It grades different geographic locations according to a measure called weightage of participation so that vulnerable locations can be easily identified. This paper gives directions on the advantages of using spatio-temporal aggregate queries for extracting general characteristics of social mobility. The system also facilitates room for the generation of various information by combing through the medical reports of the patients. Findings It is identified that context of movement is important; hence, the existing SemTraClus algorithm is modified by accounting for four important factors such as stay point, contact presence, stay time of primary contacts and waypoint severity. The priority level can be reconfigured according to the interest of authority. This approach reduces the overwhelming task of contact tracing. Different functionalities provided by the system are also explained. As the real data set is not available, experiments are conducted with similar data and results are shown for different types of journeys in different geographical locations. The proposed method efficiently handles computational movement and activity analysis by incorporating various relevant semantics of trajectories. The incorporation of cluster-based aggregate queries in the model do away with the computational headache of processing the entire mobility data. Research limitations/implications As the trajectory of patients is not available, the authors have used the standard data sets for experimentation, which serve the purpose. Originality/value This paper proposes a framework infrastructure that allows the emergency response team to grab multiple information based on the tracked mobility details of a patient and facilitates room for various activities for the mitigation of pandemics such as the prediction of hotspots, identification of stay locations and suggestion of possible locations of primary and secondary contacts, creation of clusters of hotspots and identification of nearby medical assistance. The system provides an efficient way of activity analysis by computing the mobility of people and identifying features of geographical locations where people travelled. While formulating the framework, the authors have reviewed many different implementation plans and protocols and arrived at the conclusion that the core strategy followed is more or less the same. For the sake of a reference model, the Indian scenario is adopted for defining the concepts.
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