Before revision:  As a case study, we compare these two types of models for COVID-19 forecasting and notice that physics-based  models significantly outperform deep learning models. We present a hybrid approach, AutoODE-COVID, which combines a novel compartmental model with automatic differentiation.
After revision:  As a case study, we compare these two types of models for COVID-19 forecasting and notice that physics-based models significantly outperform deep learning .