  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.

Machine Learning;Applications