Simulator Development for Realism

Background: Deep learning models for infectious disease forecasting require substantial training data, which is often unavailable for a novel pathogen at the onset of an outbreak.

Question / Future Work: Further work should investigate developing simulators sophisticated enough to generate training data with sufficient realism to support the scaling of deep learning models for emerging disease forecasting. This includes developing simulators that can model complex real-world data features.