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 influenza and SARS-CoV-2, has highlighted the urgent need for advanced modeling approaches that can accurately capture heterogeneous disease dynamics and outcomes at the national scale, thereby enhancing the effectiveness of resource allocation and decision-making. In this talk I will first describe Epicast 2.0, an agent-based model that utilizes a highly detailed, synthetic population to simulate respiratory pathogen transmission across the entire United States. I will then demonstrate the model's capabilities using a range of outbreak scenarios, highlighting the model's varied dynamics as well as its extensive support for policy exploration. Finally, I will present a deep-learning method for calibrating Epicast 2.0 to observed outbreaks, enabling the model to inform preparedness for future epidemics in a manner that is strongly grounded in real world data