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

• URL: http://arxiv.org/abs/2506.11028v1
• Date: Tue, 20 May 2025 12:23:18 GMT
• Title: Enhancing Epidemic Forecasting: Evaluating the Role of Mobility Data and Graph Convolutional Networks
• Authors: Suhan Guo, Zhenghao Xu, Furao Shen, Jian Zhao,
• Abstract summary: This study addresses the gap between machine learning algorithms and their epidemiological applications.<n>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.<n>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.
• Score: 9.460023981858319
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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