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

arXiv: https://arxiv.org/abs/2110.11584 · HTML: https://mlsanity.com/paper/2110.11584/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 63 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction](https://mlsanity.com/paper/1803.01254/) (2018)
- [Graph Convolutional Encoders for Syntax-aware Neural Machine Translation](https://mlsanity.com/paper/1704.04675/) (2017)
- [Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2007.03113/) (2020)
- [Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network](https://mlsanity.com/paper/2110.04038/) (2021)
- [Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic](https://mlsanity.com/paper/2008.12238/) (2020)
- [MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation](https://mlsanity.com/paper/2004.09750/) (2020)
- [Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19](https://mlsanity.com/paper/2009.11407/) (2020)
- [Integrating LSTMs and GNNs for COVID-19 Forecasting](https://mlsanity.com/paper/2108.10052/) (2021)
- [STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization](https://mlsanity.com/paper/2012.04747/) (2020)
- [Predicting COVID-19 Spread from Large-Scale Mobility Data](https://mlsanity.com/paper/2106.00356/) (2021)
- [COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model](https://mlsanity.com/paper/2007.10286/) (2020)
- [C-Watcher: A Framework for Early Detection of High-Risk Neighborhoods Ahead of COVID-19 Outbreak](https://mlsanity.com/paper/2012.12169/) (2020)
- [Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting](https://mlsanity.com/paper/2010.14491/) (2020)
- [Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression](https://mlsanity.com/paper/2006.16942/) (2020)
- [Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior](https://mlsanity.com/paper/1906.07770/) (2019)
- [Scaling of contact networks for epidemic spreading in urban transit systems](https://mlsanity.com/paper/2002.03564/) (2020)
- [Syndromic surveillance using search query logs and user location information from smartphones against COVID-19 clusters in Japan](https://mlsanity.com/paper/2004.10100/) (2020)
- [Providing early indication of regional anomalies in COVID19 case counts in England using search engine queries](https://mlsanity.com/paper/2007.11821/) (2020)
- [condLSTM-Q: A novel deep learning model for predicting Covid-19 mortality in fine geographical Scale](https://mlsanity.com/paper/2011.11507/) (2020)

## Built on by (most cited first, 3 of 3)

- [MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting](https://mlsanity.com/paper/2401.09261/) (2024)
- [Mixture-of-Experts for Personalized and Semantic-Aware Next Location Prediction](https://mlsanity.com/paper/2505.24597/) (2025)
- [Dimensional Balance Improves Large Scale Spatiotemporal Prediction Performance](https://mlsanity.com/paper/2605.18793/) (2026)

## Abstract (arXiv)

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.