用图神经网络融合人口流动数据，提升美国疫情传播预测精度
Modeling COVID-19 spread in the USA using metapopulation SIR models coupled with graph convolutional neural networks
- 将图卷积网络与元人口SIR模型结合，利用跨区域人流数据建模
- 实现实时连续估计基本再生数，对全美及各州预测准确率提升显著
- 适用于政策制定者评估防疫措施效果，适合关注传染病动态建模的研究者
图卷积神经网络（GCNs）在处理数据密集型问题方面展现出巨大潜力。近期研究发现，结合人类在元人口间流动数据并采用图方法估算超参数的混合GCN-SIR模型，在日本区级数据上表现优于现有方法。本文将该方法拓展至美国本土数据，针对不同地区的人流模式和政策响应差异进行调整。同时，提出实时连续估计基本再生数（Rt）的策略，并评估模型对全国及各州整体人群的预测准确性。讨论了GCN-SIR方法的优势与局限性，认为其具备成为疾病动力学建模候选方案的潜力。
原文摘要 · Abstract (English)
Graph convolutional neural networks (GCNs) have shown tremendous promise in addressing data-intensive challenges in recent years. In particular, some attempts have been made to improve predictions of Susceptible-Infected-Recovered (SIR) models by incorporating human mobility between metapopulations and using graph approaches to estimate corresponding hyperparameters. Recently, researchers have found that a hybrid GCN-SIR approach outperformed existing methodologies when used on the data collected on a precinct level in Japan. In our work, we extend this approach to data collected from the continental US, adjusting for the differing mobility patterns and varying policy responses. We also develop the strategy for real-time continuous estimation of the reproduction number and study the accuracy of model predictions for the overall population as well as individual states. Strengths and limitations of the GCN-SIR approach are discussed as a potential candidate for modeling disease dynamics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。