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Recurring outbreaks of COVID-19 have posed enduring effects on globalsociety, which calls for a predictor of pandemic waves using various data withearly availability. Existing prediction models that forecast the first outbreakwave using mobility data may not be applicable to the multiwave prediction,because the evidence in the USA and Japan has shown that mobility patternsacross different waves exhibit varying relationships with fluctuations ininfection cases. Therefore, to predict the multiwave pandemic, we propose aSocial Awareness-Based Graph Neural Network (SAB-GNN) that considers the decayof symptom-related web search frequency to capture the changes in publicawareness across multiple waves. Our model combines GNN and LSTM to model thecomplex relationships among urban districts, inter-district mobility patterns,web search history, and future COVID-19 infections. We train our model topredict future pandemic outbreaks in the Tokyo area using its mobility and websearch data from April 2020 to May 2021 across four pandemic waves collected byYahoo Japan Corporation under strict privacy protection rules. Resultsdemonstrate our model outperforms state-of-the-art baselines such as ST-GNN,MPNN, and GraphLSTM. Though our model is not computationally expensive (only 3layers and 10 hidden neurons), the proposed model enables public agencies toanticipate and prepare for future pandemic outbreaks.

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