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

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Abstract

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.

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Notes

  1. https://covid19forecasthub.org.

  2. https://github.com/CSSEGISandData/COVID-19.

  3. https://covid19forecasthub.org/eval-reports/#Incident_Case_Forecasts_(state)

  4. https://covid19forecasthub.org/eval-reports/#Incident_Case_Forecasts_(county)

  5. https://zoltardata.com/project/44.

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Funding

This work was supported by National Key Research and Development Project (No. 2020AAA0106200), the National Nature Science Foundation of China under Grants (No.62325206, 619360005), Key Research and Development Program of Jiangsu Province under Grant BE2023016-4, and the Natural Science Foundation of Jiangsu Province under Grant BK20210595.

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  1. Nan**g University of Posts and Telecommunications, Nan**g, Jiangsu, China

    Mingjie Qiu, Zhiyi Tan & Bing-Kun Bao

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  1. Mingjie Qiu
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Qiu, M., Tan, Z. & Bao, BK. MSGNN: Multi-scale Spatio-temporal Graph Neural Network for epidemic forecasting. Data Min Knowl Disc (2024). https://doi.org/10.1007/s10618-024-01035-w

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