  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.
