Publications

その他 (国際) Multiwave COVID-19 Prediction via Social Awareness-Based Graph Neural Networks using Mobility and Web Search Data

Jiawei Xue (Purdue University), Takahiro Yabe (Purdue University), Kota Tsubouchi, Jianzhu Ma (Purdue University), Satish V. Ukkusuri (Purdue University)

arXiv.org

2021.10.25

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 \textbf{S}ocial \textbf{A}wareness-\textbf{B}ased \textbf{G}raph \textbf{N}eural \textbf{N}etwork (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 February 2020 to June 2021 across four pandemic outbreak waves collected by Yahoo Japan Corporation under strict privacy protection rules. Results show our model reaches a certain accuracy (RMSE=XXX) and outperforms several other baselines including XXX and YYY. Though our model is not complicated (only XX layers and YY hiddens), it has a significant social impact that enables public agencies to anticipate and prepare for future pandemic outbreaks.

Paper : Multiwave COVID-19 Prediction via Social Awareness-Based Graph Neural Networks using Mobility and Web Search Data (外部サイト)