Policy makers that prepare responses to emergency situations need to have an understanding of the populations’ values, preferences and decision-making processes to implement effective interventions. Understanding the role of human behavior in epidemic outcomes has long been recognized as important, and is seen in the case of the COVID-19 pandemic. We will use a combination of data fusion techniques, large-scale agent-based simulation models, and inverse reinforcement learning to construct a framework that allows them to learn behaviors and responses to interventions so they can reshape responses and preferences to accomplish shared goals, including saving lives and maintaining a sustainable economy. This GIDI seed grant will allow us to generate a framework that will answer these questions. In the pilot work, synthetic populations will be used to prepare a calibrated, analytics and simulation platform; in follow-up work, surveys and sources such as social media data will be used to fine-tune models.
Machine Learning Efficient Behavioral Interventions for Novel Epidemics
Henning Mortveit, Ph.D., Associate Professor of Engineering Systems and Environment
Peter Beling, Ph.D., Professor and Associate Chair for Research of Engineering Systems and Environment