Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks

Abstract

In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data. In contrast to existing time series forecasting models, the proposed approach learns from a single large-scale spatio-temporal graph, where nodes represent the region-level human mobility, spatial edges represent the human mobility based inter-region connectivity, and temporal edges represent node features through time. We evaluate this approach on the US county level COVID-19 dataset, and demonstrate that the rich spatial and temporal information leveraged by the graph neural network allows the model to learn complex dynamics. We show a 6% reduction of RMSLE and an absolute Pearson Correlation improvement from 0.9978 to 0.998 compared to the best performing baseline models. This novel source of information combined with graph based deep learning approaches can be a powerful tool to understand the spread and evolution of COVID-19. We encourage others to further develop a novel modeling paradigm for infectious disease based on GNNs and high resolution mobility data.

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Cited by in corpus (13)

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• Automated Machine Learning on Graphs: A Survey
• Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders
• Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic Modeling with Human Mobility
• Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data
• Analysis of the Spatio-temporal Dynamics of COVID-19 in Massachusetts via Spectral Graph Wavelet Theory
• Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany
• High-resolution Spatio-temporal Model for County-level COVID-19 Activity in the U.S
• STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using Real World Evidence
• Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting
• Improving COVID-19 Forecasting using eXogenous Variables
• Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Network
• Predicting COVID-19 Spread from Large-Scale Mobility Data