We propose a novel approach that integrates machine learning into compartmental disease modeling (e.g., SEIR) 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 we demonstrate it for states and counties in the United States. We show that our model provides more accurate forecasts compared to the alternatives, and that it provides qualitatively meaningful explanatory insights.

Labeled data is scarce. Most statistical machine learning techniques rely on the availability of a large labeled corpus for building robust models for prediction and classification. In this paper we present a Higher Order Collective Classifier (HOCC) based on higher order learning, a statistical machine learning technique that leverages latent information present in co-occurrences of items across records. These techniques violate the IID assumption that underlies most statistical machine learning techniques and have in prior work outperformed first order techniques in the presence of very limited data. We present results of applying HOCC to two different network data sets, first for detection and classification of anomalies in a border gateway protocol dataset and second for building models of users from network file system calls to perform masquerade detection. The precision of our system has been shown to be 30% better than the standard Naive Bayes technique for masquerade detection. These results indicate that HOCC can successfully model a variety of network events and can be applied to solve difficult problems in security using the general framework proposed.