Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction
This work addresses the need for accurate and early epidemic forecasting for epidemiologists and healthcare providers, though it appears incremental as it builds on existing graph neural network methods.
The paper tackles the problem of long-term influenza-like illness (ILI) prediction by proposing a cross-location attention-based graph neural network (Cola-GNN) to capture spatio-temporal dependencies, resulting in strong predictive performance and interpretable results on real-world datasets from the United States and Japan.
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.