  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.
