Enhancing Epidemic Forecasting: Evaluating the Role of Mobility Data and Graph Convolutional Networks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911002803568640 |
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| author | Guo, Suhan Xu, Zhenghao Shen, Furao Zhao, Jian |
| author_facet | Guo, Suhan Xu, Zhenghao Shen, Furao Zhao, Jian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11028 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Epidemic Forecasting: Evaluating the Role of Mobility Data and Graph Convolutional Networks Guo, Suhan Xu, Zhenghao Shen, Furao Zhao, Jian Machine Learning Artificial Intelligence 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. |
| title | Enhancing Epidemic Forecasting: Evaluating the Role of Mobility Data and Graph Convolutional Networks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.11028 |