The rapid spread of COVID-19 disease has had a significant impact on the world. In this paper, we study COVID-19 data interpretation and visualization using open-data sources for 351 cities and towns in Massachusetts from December 6, 2020 to September 25, 2021. Because cities are embedded in rather complex transportation networks, we construct the spatio-temporal dynamic graph model, in which the graph attention neural network is utilized as a deep learning method to learn the pandemic transition probability among major cities in Massachusetts. Using the spectral graph wavelet transform (SGWT), we process the COVID-19 data on the dynamic graph, which enables us to design effective tools to analyze and detect spatio-temporal patterns in the pandemic spreading. We design a new node classification method, which effectively identifies the anomaly cities based on spectral graph wavelet coefficients. It can assist administrations or public health organizations in monitoring the spread of the pandemic and developing preventive measures. Unlike most work focusing on the evolution of confirmed cases over time, we focus on the spatio-temporal patterns of pandemic evolution among cities. Through the data analysis and visualization, a better understanding of the epidemiological development at the city level is obtained and can be helpful with city-specific surveillance.

中文翻译：

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通过谱图小波理论分析马萨诸塞州 COVID-19 的时空动态

COVID-19 疾病的迅速传播对世界产生了重大影响。在本文中，我们使用开放数据源研究了 2020 年 12 月 6 日至 2021 年 9 月 25 日期间马萨诸塞州 351 个城镇的 COVID-19 数据解释和可视化。由于城市嵌入相当复杂的交通网络中，我们构建了空间-时间动态图模型，其中使用图注意力神经网络作为深度学习方法来学习马萨诸塞州主要城市之间的大流行转移概率。使用谱图小波变换 (SGWT)，我们在动态图上处理 COVID-19 数据，这使我们能够设计有效的工具来分析和检测大流行传播中的时空模式。我们设计了一种新的节点分类方法，基于谱图小波系数有效识别异常城市。它可以帮助政府或公共卫生组织监测大流行的传播并制定预防措施。与大多数关注确诊病例随时间演变的工作不同，我们关注城市之间大流行演变的时空模式。通过数据分析和可视化，可以更好地了解城市层面的流行病学发展，并有助于针对城市的监测。我们关注城市之间流行病演变的时空模式。通过数据分析和可视化，可以更好地了解城市层面的流行病学发展，并有助于针对城市的监测。我们关注城市之间流行病演变的时空模式。通过数据分析和可视化，可以更好地了解城市层面的流行病学发展，并有助于针对城市的监测。