  We propose a novel approach that integrates machine learning into
compartmental disease modeling to predict the progression of COVID-19. Our
model is explainable by design as it explicitly shows how different
compartments evolve and it uses interpretable encoders to incorporate
covariates and improve performance. Explainability is valuable to ensure that
the model's forecasts are credible to epidemiologists and to instill confidence
in end-users such as policy makers and healthcare institutions. Our model can
be applied at different geographic resolutions, and here we demonstrate it for
states and counties in the United States. We show that our model provides more
accurate forecasts, in metrics averaged across the entire US, than
state-of-the-art alternatives, and that it provides qualitatively meaningful
explanatory insights. Lastly, we analyze the performance of our model for
different subgroups based on the subgroup distributions within the counties.
