  This study proposed a novel framework for COVID-19 severity prediction, which
is a combination of data-centric and model-centric approaches. First, we
propose a data-centric pre-training for extremely scare data scenarios of the
investigating dataset. Second, we propose two hybrid convolution-attention
neural architectures that leverage the self-attention from the Transformer and
the Dense Associative Memory (Modern Hopfield networks). Our proposed approach
achieves significant improvement from the conventional baseline approach. The
best model from our proposed approach achieves $R^2 = 0.85 \pm 0.05$ and
Pearson correlation coefficient $\rho = 0.92 \pm 0.02$ in geographic extend and
$R^2 = 0.72 \pm 0.09, \rho = 0.85\pm 0.06$ in opacity prediction.
