BLUAI, Athens, Greece.
École Polytechnique, Palaiseau, France.
Sci Rep. 2023 Mar 31;13(1):5235. doi: 10.1038/s41598-023-31222-6.
The pandemic of COVID-19 is undoubtedly one of the biggest challenges for modern healthcare. In order to analyze the spatio-temporal aspects of the spread of COVID-19, technology has helped us to track, identify and store information regarding positivity and hospitalization, across different levels of municipal entities. In this work, we present a method for predicting the number of positive and hospitalized cases via a novel multi-scale graph neural network, integrating information from fine-scale geographical zones of a few thousand inhabitants. By leveraging population mobility data and other features, the model utilizes message passing to model interaction between areas. Our proposed model manages to outperform baselines and deep learning models, presenting low errors in both prediction tasks. We specifically point out the importance of our contribution in predicting hospitalization since hospitals became critical infrastructure during the pandemic. To the best of our knowledge, this is the first work to exploit high-resolution spatio-temporal data in a multi-scale manner, incorporating additional knowledge, such as vaccination rates and population mobility data. We believe that our method may improve future estimations of positivity and hospitalization, which is crucial for healthcare planning.
新冠疫情无疑是现代医疗保健面临的最大挑战之一。为了分析新冠疫情的时空传播方面，技术帮助我们追踪、识别和存储有关各市镇实体的阳性病例和住院情况的信息。在这项工作中，我们提出了一种通过新颖的多尺度图神经网络预测阳性病例和住院病例数量的方法，该方法整合了来自几千居民的精细地理区域的信息。通过利用人口流动数据和其他特征，该模型利用消息传递来模拟区域之间的相互作用。我们提出的模型在预测任务中表现优于基线和深度学习模型，在两个预测任务中都表现出较低的误差。我们特别指出了我们在预测住院方面的贡献的重要性，因为在疫情期间医院成为了关键基础设施。据我们所知，这是首次以多尺度方式利用高分辨率时空数据并结合疫苗接种率和人口流动数据等额外知识的工作。我们相信，我们的方法可以改进未来对阳性病例和住院情况的估计，这对医疗保健规划至关重要。
