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

Yuzhou Chen, Sotiris Batsakis, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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 (US)
Title of host publicationICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728163277
DOIs
StatePublished - 2023
Event48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece
Duration: Jun 4 2023Jun 10 2023

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2023-June
ISSN (Print)1520-6149

Conference

Conference48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Country/TerritoryGreece
CityRhodes Island
Period6/4/236/10/23

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • COVID-19
  • Deep Learning
  • Simplicial Complex

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