标题： 利用图神经网络和移动数据进行新冠疫情预测 显示英文标题

标题： Leveraging graph neural networks and mobility data for COVID-19 forecasting

摘要： 截至现在，新冠疫情已使超过700万人受害，促使了各种研究工作的开展。 结合移动数据与机器学习的时空模型在疾病预测中引起了关注。 在此，我们探讨图卷积循环网络（GCRN）和图卷积长短期记忆网络（GCLSTM），它们将图神经网络（GNN）的力量与处理序列数据的传统架构相结合。 目的是通过利用人类移动网络来预测巴西和中国未来的新冠病例数，其中网络的节点代表地理位置，链接表示车辆或人员的流动。 我们表明，采用骨干提取来过滤移动网络中不重要的连接可以提高预测的稳定性。 比较回归和分类任务表明，二分类产生了更平滑、更易解释的结果。 有趣的是，通过引入可变大小的滑动窗口和预测时间范围，我们观察到巴西和中国数据集有定性等效的结果。 与之前的研究相比，引入滑动窗口和网络骨干提取策略使均方根误差提高了约80%。 显示英文摘要

摘要： The COVID-19 pandemic has victimized over 7 million people to date, prompting diverse research efforts. Spatio-temporal models combining mobility data with machine learning have gained attention for disease forecasting. Here, we explore Graph Convolutional Recurrent Network (GCRN) and Graph Convolutional Long Short-Term Memory (GCLSTM), which combine the power of Graph Neural Networks (GNN) with traditional architectures that deal with sequential data. The aim is to forecast future values of COVID-19 cases in Brazil and China by leveraging human mobility networks, whose nodes represent geographical locations and links are flows of vehicles or people. We show that employing backbone extraction to filter out negligible connections in the mobility network enhances predictive stability. Comparing regression and classification tasks demonstrates that binary classification yields smoother, more interpretable results. Interestingly, we observe qualitatively equivalent results for both Brazil and China datasets by introducing sliding windows of variable size and prediction horizons. Compared to prior studies, introducing the sliding window and the network backbone extraction strategies yields improvements of about 80% in root mean squared errors.