Multiwave COVID-19 Prediction from Social Awareness using Web Search and Mobility Data

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 our model to predict future pandemic outbreaks in the Tokyo area using its mobility and web search data from April 2020 to May 2021 across four pandemic waves collected by Yahoo Japan Corporation under strict privacy protection rules. Results demonstrate our model outperforms state-of-the-art baselines such as ST-GNN, MPNN, and GraphLSTM. Though our model is not computationally expensive (only 3 layers and 10 hidden neurons), the proposed model enables public agencies to anticipate and prepare for future pandemic outbreaks.