计算机科学
利用
大流行
图形
比例(比率)
2019年冠状病毒病(COVID-19)
数据科学
人口
人工神经网络
机器学习
人工智能
数据挖掘
地理
地图学
医学
计算机安全
理论计算机科学
传染病(医学专业)
疾病
环境卫生
病理
作者
Konstantinos Skianis,Giannis Nikolentzos,B. Gallix,Rodolphe Thiébaut,Georgios Exarchakis
标识
DOI:10.1038/s41598-023-31222-6
摘要
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.
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