The COVID-19 pandemic has highlighted the global need for reliable models of disease spread. We propose a novel AI-augmented forecast modeling framework that is based on integrating machine-learned mapping of informative covariates into the compartmental models. Via prospective evaluation, we demonstrate that our framework yields very accurate forecasts, outperforming other models, as well as explainable insights into the disease dynamics and what-if simulation capabilities.