NeuralCODE: Neural Compartmental Ordinary Differential Equations Model with AutoML for Interpretable Epidemic Forecasting

In order to prevent the re-emergence of an epidemic, predicting its trend while gaining insight into the intrinsic factors affecting it is a key issue in urban governance. Traditional SIR-like compartment models provide insight into the explanatory parameters of an outbreak, and the vast majority of existing deep learning models can predict the course of an outbreak well, but neither performs well in the other’s domain. Simultaneously, studying the commonalities and diversities in the causes of outbreaks among different countrywide regions is also a way to interrupt outbreaks. To address the issues of outbreak intrinsic relationships and prediction, we propose the Neural Compartmental Ordinary Differential Equations (NeuralCODE) model to study the relationship between population movements and outbreak development in different regions. Furthermore, to incorporate the commonalities and diversities in causes among different regions into the prediction and intrinsic inquiry problem, we propose an AutoML framework. Our results found that simply using the NeuralCODE algorithm could obtain better prediction and insight capabilities within different regions. With the introduction of AutoML, it became possible to explore the factors inherent in the epidemic’s development across regions and further improve the original algorithm’s predictive performance.