用图卷积网络分析移动数据，发现住院死亡数据更关键
Enhancing Epidemic Forecasting: Evaluating the Role of Mobility Data and Graph Convolutional Networks
- 用两阶段方法评估移动数据与图卷积网络的作用
- 加入住院和死亡数据使预测准确率显著提升
- 空间图谱与封城令相关，可作疫情敏感指标
准确预测传染病爆发对决策至关重要。本研究针对机器学习模型在流行病学应用中表现不佳的问题，指出主流算法在基准数据上表现优异，但在真实数据中因难以融入移动信息而效果下降。采用两阶段方法：首先通过试点研究评估移动数据的重要性，再在Transformer骨干网络上测试图卷积网络（GCNs）的影响。结果表明，移动数据和GCN模块对预测性能提升不显著，但纳入死亡与住院数据后模型准确率明显提高。此外，对比GCN生成的空间图谱与封城令发现显著相关性，表明空间图谱可作为移动变化的敏感指标。研究为传染病预测中的移动信息建模提供了新视角，有助于决策者更好应对未来疫情。
原文摘要 · Abstract (English)
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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