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

Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction