Interpretable sequence learning for COVID-19 forecasting
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This document proposes a novel approach that integrates machine learning into
compartmental disease modeling to predict the progression of COVID-19. Our model
is explainable by design because it explicitly shows how different compartments
evolve and it uses interpretable encoders to incorporate covariates and improve
performance. Explainability is valuable to help 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 we demonstrate it for states and counties
in the United States. We show that our model provides more accurate forecasts
than state-of-the-art alternatives and that it provides qualitatively meaningful
explanatory insights.
Overview
This document outlines the following:
Review proposed compartmental model for COVID-19.
Understand the model design choices made in order to use the covariates
needed to accurately predict COVID-19.
Discuss the learning mechanisms developed to improve generalization
while learning from limited training data.
Review several experiments to compare our model to other publicly
available COVID-19 models.
Understand the potential limitations and failure cases of our model to
guide those who might use the techniques to build forecasting systems that
can affect public health decisions.