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.

翻译：近年来，图卷积神经网络（GCNs）在应对数据密集型挑战方面展现出巨大潜力。特别是，已有研究尝试通过纳入元种群间的人口流动数据，并利用图方法估计相应超参数，以改进易感-感染-恢复（SIR）模型的预测效果。近期学者发现，在日本辖区级数据上应用的混合GCN-SIR方法优于现有建模方案。本研究将该方法扩展至美国大陆采集的数据集，针对差异化的流动模式与政策响应进行调整。我们进一步开发了实时连续估计基本再生数的策略，并评估了模型对整体人口及各州个体的预测精度。本文还探讨了GCN-SIR方法作为疾病动力学建模工具的潜在优势与局限性。