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

Abstract

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.

arXiv comment17 pages, 22 figures, 5 tables

References in corpus (2)

• Semi-Supervised Classification with Graph Convolutional Networks
• End-To-End Memory Networks

Cited by in corpus (6)

• Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
• TDEFSI: Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information
• STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using Real World Evidence
• Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting
• Inter-Series Attention Model for COVID-19 Forecasting
• A Spatial-Temporal Graph Based Hybrid Infectious Disease Model with Application to COVID-19