Analysis of the Spatio-Temporal Dynamics of COVID-19 in Massachusetts Via Spectral Graph Wavelet Theory
IEEE Transactions on Signal and Information Processing over Networks
; : 1-14, 2022.
Article
in English
| Scopus | ID: covidwho-1985507
ABSTRACT
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. IEEE
COVID-19; Graph Attention Neural Network; Graph Signal Processing; Mathematical models; Pandemics; Sociology; Spatio-temporal Dynamic Model; Spectral Graph Wavelet Transform; Transportation; Urban areas; Wavelet coefficients; Data visualization; Graph theory; Public administration; Signal processing; Urban transportation; Visualization; Wavelet transforms; Dynamics models; Graph wavelets; Neural-networks; Pandemic; Signal-processing; Spatio-temporal dynamics; Wavelet coefficient; Wavelets transform
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
IEEE Transactions on Signal and Information Processing over Networks
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS