Decision support for epidemic preparedness and management: simulating respiratory pathogen outbreaks with Epicast 2.0

The recent history of respiratory pathogen epidemics, including those caused by influenzaand SARS-CoV-2, has highlighted the urgent need for advanced modeling approaches thatcan accurately capture heterogeneous disease dynamics and outcomes at the nationalscale, thereby enhancing the effectiveness of resource allocation and decision-making. Inthis talk I will first describe Epicast 2.0, an agent-based model that utilizes a highlydetailed, synthetic population to simulate respiratory pathogen transmission across theentire United States. I will then demonstrate the model's capabilities using a range ofoutbreak scenarios, highlighting the model's varied dynamics as well as its extensivesupport for policy exploration. Finally, I will present a deep-learning method for calibratingEpicast 2.0 to observed outbreaks, enabling the model to inform preparedness for futureepidemics in a manner that is strongly grounded in real world data