Enhancing Epidemic Forecasting: Evaluating the Role of Mobility Data and Graph Convolutional Networks

Accurate prediction of contagious disease outbreaks is vital for informed decision-making. Our study addresses the gap between machine learning algorithms and their epidemiological applications, noting that methods optimal for benchmark datasets often underperform with real-world data due to difficulties in incorporating mobility information. We adopt a two-phase approach: first, assessing the significance of mobility data through a pilot study, then evaluating the impact of Graph Convolutional Networks (GCNs) on a transformer backbone. Our findings reveal that while mobility data and GCN modules do not significantly enhance forecasting performance, the inclusion of mortality and hospitalization data markedly improves model accuracy. Additionally, a comparative analysis between GCN-derived spatial maps and lockdown orders suggests a notable correlation, highlighting the potential of spatial maps as sensitive indicators for mobility. Our research offers a novel perspective on mobility representation in predictive modeling for contagious diseases, empowering decision-makers to better prepare for future outbreaks.

@article{guo2025_2506.11028,
  title={ Enhancing Epidemic Forecasting: Evaluating the Role of Mobility Data and Graph Convolutional Networks },
  author={ Suhan Guo and Zhenghao Xu and Furao Shen and Jian Zhao },
  journal={arXiv preprint arXiv:2506.11028},
  year={ 2025 }
}