As multiple adverse events in 2021 illustrated, virtually all aspects of our societal functioning -- from water and food security to energy supply to healthcare -- more than ever depend on the dynamics of environmental factors. Nevertheless, the social dimensions of weather and climate are noticeably less explored by the machine learning community, largely, due to the lack of reliable and easy access to use data. Here we present a unique not yet broadly available NASA's satellite dataset on aerosol optical depth (AOD), temperature and relative humidity and discuss the utility of these new data for COVID-19 biosurveillance. In particular, using the geometric deep learning models for semi-supervised classification on a county-level basis over the contiguous United States, we investigate the pressing societal question whether atmospheric variables have considerable impact on COVID-19 clinical severity.

翻译：正如2021年的多重不利事件所表明的那样,我们社会功能的几乎所有方面 -- -- 从水和粮食安全、能源供应到保健 -- -- 都比以往更加取决于环境因素的动态。然而,机器学习界对天气和气候的社会层面的探索明显较少,这主要是因为缺乏可靠和容易使用的数据。这里我们提出了一个独特的、尚未广泛获得的美国航天局关于气溶胶光学深度、温度和相对湿度的卫星数据集,并讨论了这些新数据对COVID-19生物监视的效用。特别是,我们利用几何深深学模型,在州一级对毗连的美国进行半监督分类,我们调查了大气变量是否对COVID-19临床严重性产生巨大影响的紧迫社会问题。