标题： 使用图卷积神经网络与元人口SIR模型结合模拟美国COVID-19传播 显示英文标题

标题： Modeling COVID-19 spread in the USA using metapopulation SIR models coupled with graph convolutional neural networks

摘要： 图卷积神经网络（GCNs）近年来在应对数据密集型挑战方面展现出巨大潜力。 特别是，一些尝试通过结合元种群间的人类流动性和使用图方法来估计相应的超参数，从而改进了易感-感染-恢复（SIR）模型的预测。 最近，研究人员发现，当这种混合GCN-SIR方法应用于日本按辖区收集的数据时，其表现优于现有方法。 在我们的研究中，我们将这种方法扩展到从美国本土收集的数据，并根据不同的流动性模式和政策反应进行了调整。 我们还开发了一种实时连续估算再生数的策略，并研究了模型对总体人口以及各州预测的准确性。 讨论了GCN-SIR方法的优势与局限性，将其作为建模疾病动态的一种潜在候选方案。 显示英文摘要

摘要： 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.