TL;DR
This paper introduces SAB-GNN, a graph neural network model that predicts multiple COVID-19 waves by integrating social awareness from web search data and mobility patterns, outperforming existing models.
Contribution
The paper presents a novel multiwave COVID-19 prediction model combining GNN and LSTM that accounts for changing public awareness and mobility across pandemic waves.
Findings
SAB-GNN outperforms state-of-the-art baselines in predicting COVID-19 waves.
The model effectively captures the decay of symptom-related web searches over time.
It is computationally efficient with only 3 layers and 10 hidden neurons.
Abstract
Recurring outbreaks of COVID-19 have posed enduring effects on global society, which calls for a predictor of pandemic waves using various data with early availability. Existing prediction models that forecast the first outbreak wave using mobility data may not be applicable to the multiwave prediction, because the evidence in the USA and Japan has shown that mobility patterns across different waves exhibit varying relationships with fluctuations in infection cases. Therefore, to predict the multiwave pandemic, we propose a Social Awareness-Based Graph Neural Network (SAB-GNN) that considers the decay of symptom-related web search frequency to capture the changes in public awareness across multiple waves. Our model combines GNN and LSTM to model the complex relationships among urban districts, inter-district mobility patterns, web search history, and future COVID-19 infections. We train…
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Taxonomy
MethodsGraph Neural Network · Message Passing Neural Network · Tanh Activation · Sigmoid Activation · Long Short-Term Memory
