Interpretable Sequence Learning for Covid-19 Forecasting

Chun-Liang Li
Arkady Epshteyn
Shashank Singh
Martin Nikoltchev
Yash Kumar Sonthalia
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We propose a novel model that integrates machine learning into compartmental disease modeling to predict the progression of Covid-19. Our model incorporates explainable encoding of information-bearing covariates to improve performance. The motivation to maintain explainability is two-fold: the behavior of the resulting model will be credible with epidemiologists, and will instill confidence in the intended end-users - policy makers and healthcare institutions. The proposed model can be applied at different geographic resolutions, and we demonstrate it for United States' states and counties. We show that the forecasting accuracy of our model is significantly better than the alternatives, and the explanatory insights from it are qualitatively meaningful.

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