论文标题
与COVID-19的应用程序有关的迁移数据的异常检测到情境意识
Anomaly Detection of Mobility Data with Applications to COVID-19 Situational Awareness
论文作者
论文摘要
这项工作引入了一个实时异常检测系统，用于在区域尺度上收集的高频和高维数据，例如移动定位数据的原始目的地矩阵。要考虑到来自不同来源的数据的时间和空间的不同粒度，该系统旨在简单而强大，对数据多样性进行了强大的态度，目的是检测出对特定区域的流动性突然增加以及运动的突然下降。该方法旨在帮助政策制定者或从业者，并可以看到异常情况，并估算Covid-19相关遏制或提升措施的影响，以其对人类流动性的影响以及与大型聚会相关的现场潜在疫情。
This work introduces a live anomaly detection system for high frequency and high-dimensional data collected at regional scale such as Origin Destination Matrices of mobile positioning data. To take into account different granularity in time and space of the data coming from different sources, the system is designed to be simple, yet robust to the data diversity, with the aim of detecting abrupt increase of mobility towards specific regions as well as sudden drops of movements. The methodology is designed to help policymakers or practitioners, and makes it possible to visualise anomalies as well as estimate the effect of COVID-19 related containment or lifting measures in terms of their impact on human mobility as well as spot potential new outbreaks related to large gatherings.
