MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting

TL;DR

The paper introduces MSGNN, a multi-scale spatio-temporal graph neural network that effectively captures long-range and multi-scale epidemic patterns, improving COVID-19 forecasting accuracy and interpretability.

Contribution

It proposes a novel multi-scale graph learning and convolution framework to better model epidemic spread across different spatial scales.

Findings

Outperforms state-of-the-art methods in COVID-19 case forecasting.

Provides robust and interpretable epidemic predictions.

Demonstrates effectiveness on real-world US COVID-19 data.

Abstract

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.