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. SAB-GNN 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 _ANONYMOUS_COMPANY_ under strict privacy protection rules. Results show our model outperforms other baselines including ST-GNN and MPNN+LSTM. 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.
翻译:由于美国和日本的证据表明,不同波的移动模式与感染病例的波动有着不同的关系,因此,为了预测多波流感,我们提议建立一个基于社会意识的图像神经网络(SAB-GNN),根据严格的隐私保护规则,考虑症状相关网络搜索频率的衰落,以捕捉多波公众意识的变化。结果显示我们的模型比其他基线(ST-GNN和MP+LSTM)高,包括ST-GNN和MP+LSTM),但模型为未来10级的公众疫情做准备。