MSGNN: Multi-scale Spatio-temporal Graph Neural Network for epidemic forecasting

• Published in: Data mining and knowledge discovery Vol. 38; no. 4; pp. 2348 - 2376
• Main Authors: Qiu, Mingjie, Tan, Zhiyi, Bao, Bing-Kun
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Data Mining and Knowledge Discovery; Disease control; Epidemics; Forecasting; Graph neural networks; Graph theory; Infectious diseases; Information Storage and Retrieval; Machine learning; Modules; Neural networks; Physics; Semantics; Statistics for Engineering; Graph structure learning; Graph neural networks; Epidemic forecasting; Multi-scale modeling; Spatio-temporal forecasting
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-024-01035-w

Infectious disease forecasting has been a key focus and proved to be crucial in controlling epidemic. A recent trend is to develop forecasting 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 insufficient 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 patterns 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 patterns. 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. Code is available at https://github.com/JashinKorone/MSGNN .