Public transportation network scan for rapid surveillance
Public transportation network scan for rapid surveillance
复制标题
公共交通网络扫描快速监控
DOI：
10.1080/24709360.2022.2065628
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Matsuura Kentaro
中科院分区：
文献类型：
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作者：
Tanoue Yuta;Yoneoka Daisuke;Kawashima Takayuki;Uryu Shinya;Nomura Shuhei;Eguchi Akifumi;Makiyama Koji;Matsuura Kentaro
随着人们使用火车和飞机等公共交通网络四处移动，预计新出现的传染病将在网络上传播。扫描统计方法已被频繁应用于识别高风险部位，其结果被广泛用于及时做出临床决策。然而，它们不是为模拟传播而设计的最佳方案，并且在计算时间至关重要的紧急情况下可能无法有效工作。提出了一种新的公交网络扫描统计方法PTNS（Public Transportation Network Scan）。PTNS利用现有的网络结构来构建潜在的候选聚类，因此它可以很好地工作，特别是在公共交通是感染传播的主要媒介的情况下。此外，它是为快速监测而设计的。最后，PTNS被推广到检测空间-时间集群通过定制潜在的集群创建的迭代。使用与真实的铁路网络生成的模拟数据，我们表明，PTNS优于传统的方法，包括圆形和柔性扫描方法，在检测性能方面，而计算时间是可行的。
As people move around using public transportation networks such as train and airplanes, it is expected that emerging infectious diseases will spread on the network. The scan statistics approach has been frequently applied to identify high-risk locations, and the results are widely used for making a clinical decision in a timely manner. However, they are not optimally designed for modeling the spread and might not effectively work in the emergency situation where computational time is essentially important. We propose a new scan statistics approach for the public transportation network, called PTNS (Public Transportation Network Scan). PTNS utilizes the available network structure to construct potential candidates of clusters, and thus it can work well especially in situations where public transportation is the main medium of the infection spread. Further, it is designed for rapid surveillance. Lastly, PTNS is generalized to detect space-time clusters by customizing the iteration for potential clusters creation. Using the simulation data generated with a real railway network, we showed that, PTNS outperformed the conventional methods, including Circular- and Flex-scan approaches, in terms of the detection performance, while the computational time is feasible.