CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting

Keyphrases

• Graph Neural Network 100%
• Spatiotemporal 100%
• Epidemic Forecasting 100%
• Epidemiological Context 75%
• Recent Past 25%
• Deep Learning 25%
• COVID-19 Pandemic 25%
• Neural Network Model 25%
• Learning Framework 25%
• Mechanistic Model 25%
• COVID-19 25%
• Graph Embedding 25%
• Graph-based 25%
• Ordinary Differential Equations 25%
• Learning Model 25%
• Data Graph 25%
• Learning Mechanisms 25%
• U.S. Counties 25%
• Nonlinear Transformation 25%
• County Level 25%
• Spatio-temporal Data 25%
• Causal Mechanisms 25%
• Latent Space 25%
• Quality Forecasting 25%
• Attention-based 25%
• US States 25%
• Forecast Performance 25%
• Node Embedding 25%
• Disease Dynamics 25%
• Infectious Disease Forecasting 25%
• Daily New Cases 25%
• Produce Quality 25%
• Spatiotemporal Embedding 25%
• Cross-region 25%
• Feature Context 25%

Computer Science

• Graph Neural Network 100%
• And-States 25%
• Learning Framework 25%
• Neural Network Model 25%
• Learning Mechanism 25%
• Deep Learning Model 25%
• Nonlinear Transformation 25%
• Spatiotemporal Data 25%
• Parameter Size 25%
• Node Embedding 25%
• Causal Mechanism 25%

Neuroscience

• Neural Network 100%