Forecasting influenza-like illness (ILI) is of prime importance to
epidemiologists and health-care providers. Early prediction of epidemic
outbreaks plays a pivotal role in disease intervention and control. Most
existing work has either limited long-term prediction performance or lacks a
comprehensive ability to capture spatio-temporal dependencies in data. Accurate
and early disease forecasting models would markedly improve both epidemic
prevention and managing the onset of an epidemic. In this paper, we design a
cross-location attention based graph neural network (Cola-GNN) for learning
time series embeddings and location aware attentions. We propose a graph
message passing framework to combine learned feature embeddings and an
attention matrix to model disease propagation over time. We compare the
proposed method with state-of-the-art statistical approaches and deep learning
models on real-world epidemic-related datasets from United States and Japan.
The proposed method shows strong predictive performance and leads to
interpretable results for long-term epidemic predictions.

本篇论文提出了基于跨地区关注机制的图神经网络来学习时序嵌入和位置感知关注，以及将图消息传递框架与关注矩阵相结合建模疾病传播，经真实世界的流行病相关数据集的比较，本文所提出的方法表现出强大的预测性能和长期疫情预测的可解释性结果。