MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting
| Autor: | Qiu, Mingjie, Tan, Zhiyi, Bao, Bing-kun |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Data Min Knowl Disc (2024) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1007/s10618-024-01035-w |
| Popis: | Infectious disease forecasting has been a key focus and proved to be crucial in controlling epidemic. A recent trend is to develop forecast-ing models based on graph neural networks (GNNs). However, existing GNN-based methods suffer from two key limitations: (1) Current models broaden receptive fields by scaling the depth of GNNs, which is insuffi-cient to preserve the semantics of long-range connectivity between distant but epidemic related areas. (2) Previous approaches model epidemics within single spatial scale, while ignoring the multi-scale epidemic pat-terns derived from different scales. To address these deficiencies, we devise the Multi-scale Spatio-temporal Graph Neural Network (MSGNN) based on an innovative multi-scale view. To be specific, in the proposed MSGNN model, we first devise a novel graph learning module, which directly captures long-range connectivity from trans-regional epidemic signals and integrates them into a multi-scale graph. Based on the learned multi-scale graph, we utilize a newly designed graph convolution module to exploit multi-scale epidemic patterns. This module allows us to facilitate multi-scale epidemic modeling by mining both scale-shared and scale-specific pat-terns. Experimental results on forecasting new cases of COVID-19 in United State demonstrate the superiority of our method over state-of-arts. Further analyses and visualization also show that MSGNN offers not only accurate, but also robust and interpretable forecasting result. Comment: 29 pages |
| Databáze: | arXiv |
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