A Novel Multi-scale Spatiotemporal Graph Neural Network for Epidemic Prediction
Paper
Xu, Zenghui, Li, Mingzhang, Yu, Ting, Hou, Linlin, Zhang, P., Kiran, Rage Uday, Li, Zhao and Zhang, Ji. 2024. "A Novel Multi-scale Spatiotemporal Graph Neural Network for Epidemic Prediction." A.M., Strauss C.Amagasa T.Manco G.Kotsis G.Khalil I.Tjoa (ed.) 35th International Conference on Database and Expert Systems Applications (DEXA 2024). Naples, Italy 26 - 28 Aug 2024 Switzerland . Springer. https://doi.org/10.1007/978-3-031-68312-1_21
Paper/Presentation Title | A Novel Multi-scale Spatiotemporal Graph Neural Network for Epidemic Prediction |
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Presentation Type | Paper |
Authors | Xu, Zenghui, Li, Mingzhang, Yu, Ting, Hou, Linlin, Zhang, P., Kiran, Rage Uday, Li, Zhao and Zhang, Ji |
Editors | A.M., Strauss C.Amagasa T.Manco G.Kotsis G.Khalil I.Tjoa |
Journal or Proceedings Title | Proceedings of 35th International Conference on Database and Expert Systems Applications (DEXA 2024) |
Journal Citation | 14911, pp. 272-287 |
Number of Pages | 16 |
Year | 2024 |
Publisher | Springer |
Place of Publication | Switzerland |
ISBN | 9783031683114 |
9783031683121 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-031-68312-1_21 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-031-68312-1_21 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-031-68312-1 |
Conference/Event | 35th International Conference on Database and Expert Systems Applications (DEXA 2024) |
Event Details | 35th International Conference on Database and Expert Systems Applications (DEXA 2024) Parent International Conference on Database and Expert Systems Applications Delivery In person Event Date 26 to end of 28 Aug 2024 Event Location Naples, Italy |
Abstract | Predicting epidemics is of vital significance for safeguarding human life, health, and safety. Spatio-temporal graph neural networks have been successfully employed in epidemic forecasting, as they can extract information from both the temporal and spatial dimensions of the transmission process. However, the current approaches of using graph neural networks for epidemic forecasting only consider single variate infectious disease data and overlook the relationships between diffusion patterns at the micro level and macro level in terms of spatial information. Incorporating the relationships between diffusion patterns at different spatial scales is crucial for accurate epidemic forecasting, as it captures the complex dynamics of disease transmission across various levels of granularity. These limitations significantly affect the accuracy and rationality of prediction results. In this paper, we propose a Multi-Scale Spatio-Temporal graph neural network (MSST) that incorporates multivariate infectious disease data for epidemic prediction. This multi-scale structure aligns the predictive model more closely with the characteristics of infectious disease transmission and can better capture the hierarchical nature of disease transmission, from local clusters to regional and global spread. The experimental results demonstrate that our model effectively extracts predictive information and integrates it across multiple scales, leading to improved epidemic forecasting accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. |
Keywords | Epidemic prediction |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Series | Lecture Notes in Computer Science |
Byline Affiliations | Zhejiang Lab, China |
Jilin University, China | |
Guangzhou University, China | |
University of Aizu, Japan | |
Hangzhou Yugu Technology, China | |
University of Southern Queensland |
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