This paper introduces an interactive visualization interface with a machine learning consensus analysis that enables the researchers to explore the impact of atmospheric and socioeconomic factors on COVID-19 clinical severity by employing multiple Recurrent Graph Neural Networks. We designed and implemented a visualization interface that leverages coordinated multi-views to support exploratory and predictive analysis of hospitalizations and other socio-geographic variables at multiple dimensions, simultaneously. By harnessing the strength of geometric deep learning, we build a consensus machine learning model to include knowledge from county-level records and investigate the complex interrelationships between global infectious disease, environment, and social justice. Additionally, we make use of unique NASA satellite-based observations which are not broadly used in the context of climate justice applications. Our current interactive interface focus on three US states (California, Pennsylvania, and Texas) to demonstrate its scientific value and presented three case studies to make qualitative evaluations.
翻译:本文介绍一个互动可视化界面,与一个机器学习共识分析进行互动可视化界面,使研究人员能够利用多个经常性图像神经网络,探索大气和社会经济因素对COVID-19临床严重性的影响。我们设计和实施了一个可视化界面,利用协调的多视角,同时支持对住院和其他社会地理变量进行多维的探索和预测分析。通过利用几何深学习的力量,我们建立了一个共识机器学习模型,将来自县级记录的知识纳入其中,并调查全球传染病、环境和社会正义之间的复杂相互关系。此外,我们还利用美国航天局独特的卫星观测,这些观测在气候正义应用方面没有广泛使用。我们当前互动界面侧重于三个美国州(加利福尼亚州、宾夕法尼亚州和得克萨斯州),以展示其科学价值,并介绍了三个案例研究,以进行定性评估。