Wang Lijing, Ben Xue, Adiga Aniruddha, Sadilek Adam, Tendulkar Ashish, Venkatramanan Srinivasan, Vullikanti Anil, Aggarwal Gaurav, Talekar Alok, Chen Jiangzhuo, Lewis Bryan, Swarup Samarth, Kapoor Amol, Tambe Milind, Marathe Madhav
University of Virginia.
Google.
medRxiv. 2020 Dec 15:2020.12.13.20248129. doi: 10.1101/2020.12.13.20248129.
Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines.
在诸如新冠疫情这样的大流行期间，疾病动态、人员流动和公共政策是共同演变的。理解动态的人员流动变化和空间交互模式对于理解和预测新冠疫情动态至关重要。我们引入了一种新颖的基于图的神经网络（GNN），纳入全球汇总的流动数据，以便更好地理解人员流动对新冠疫情动态的影响，并更好地预测疾病动态。我们提出了一种循环消息传递图神经网络，用于嵌入时空疾病动态和人员流动动态，以预测每日州级新确诊病例数。这项工作是关于使用GNN预测新冠疫情发病动态的早期论文之一，我们的方法与现有方法相比具有竞争力。我们表明，与基线相比，图神经网络利用的时空动态流动图能够实现更好的长期预测性能。
