  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.
