In this paper, we introduce Temporal Multiresolution Graph Neural Networks (TMGNN), the first architecture that both learns to construct the multiscale and multiresolution graph structures and incorporates the time-series signals to capture the temporal changes of the dynamic graphs. We have applied our proposed model to the task of predicting future spreading of epidemic and pandemic based on the historical time-series data collected from the actual COVID-19 pandemic and chickenpox epidemic in several European countries, and have obtained competitive results in comparison to other previous state-of-the-art temporal architectures and graph learning algorithms. We have shown that capturing the multiscale and multiresolution structures of graphs is important to extract either local or global information that play a critical role in understanding the dynamic of a global pandemic such as COVID-19 which started from a local city and spread to the whole world. Our work brings a promising research direction in forecasting and mitigating future epidemics and pandemics.

翻译：在本文中,我们引入了时间多分辨率图神经网络(TMGNN),这是第一个既学会建立多尺度和多分辨率图结构,又结合时间序列信号以捕捉动态图的时间变化的架构。我们根据从几个欧洲国家实际的COVID-19大流行和天花大流行中收集的历史时间序列数据,运用了我们提议的模型来预测流行病和流行病未来蔓延的任务,并与其他最先进的时间结构以及图表学习算法相比取得了竞争性结果。我们已经表明,获取多尺度和多分辨率图结构对于提取当地或全球信息非常重要,这些信息在理解诸如COVID-19等全球流行病的动态方面发挥着至关重要的作用。 COVID-19从一个地方城市开始,传播到整个世界。我们的工作为预测和减轻未来流行病和流行病带来了充满希望的研究方向。