  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.
