Higher-Order Spatio-Temporal Neural Networks for Covid-19 Forecasting

Yuzhou Chen, Sotirios Batsakis, Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Coronavirus Disease 2019 (COVID-19) pneumonia started in December 2019 and cases have been reported in 240 countries/regions with more than 570 million confirmed cases and more than 6 million deaths which caused large casualties and huge economic losses. To enhance the understanding of the levels of COVID-19 transmission and infection, and the effects of treatments and interventions, high-quality spatio-temporal COVID-19 datasets and accurate multivariate time-series forecasting models for COVID-19 case prediction play crucial roles. In this paper, we present the COVID-19 spatio-temporal graph (COV19-STG) datasets, i.e., spatio-temporal United States COVID-19 graph datasets on the county-level. By using these datasets, we propose Higher-order Spatio-temporal Neural Networks (HOST-NETs) to further improve the accuracy of predicting COVID-19 trends. Specifically, we incorporate higher-order structure to build a simplicial complex representation learning module, and integrate it into a spatio-temporal neural network architecture, thus leveraging both global and local topological information. Experimental results show that our model consistently outperforms previous state-of-the-art models.
Original languageEnglish
Title of host publication2023 IEEE International Conference on Acoustics, Speech and Signal Processing
Subtitle of host publicationICASSP 2023
Number of pages5
ISBN (Electronic)9781728163277
ISBN (Print)9781728163284
Publication statusPublished - 5 May 2023
EventIEEE International Conference on Acoustics, Speech and Signal Processing 2023 - Rhodes Island, Greece
Duration: 4 Jun 202310 Jun 2023


ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing 2023
Abbreviated titleICASSP 2023
CityRhodes Island
Internet address

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