UT Arlington Faculty: Victoria Chen, Yuan Zhou, Jay Rosenberger

Collaborators: Alireza Fallahi, Feng Liu, Nilabh Ohol, Xinglong Ju, Jeffrey Guild, Norman Edelman

Ph.D. Students: Amith Viswanatha, Jingmei Yang, Yasaman Ghasemi

Topics: COSMOS COVID-19 Linear programming (CC19LP) framework, Agent-based model for studying COVID-19 control strategies

Description: This project proposes a control strategy framework for re-opening U.S. communities during the COVID-19 pandemic that partitions the population in the three groups: (1) COVID-19 key contact individuals, (2) Protected high-risk individuals, and (3) Unrestricted low-risk individuals.  The key contact individuals form a protective boundary for high-risk individuals.  Key contacts are high-risk or low-risk individuals that free of the COVID-19 disease and are unable to avoid close contact with both the unrestricted low-risk group and the protected high-risk group.  Examples could include caregivers, healthcare workers, and sworn-in law enforcement  officers.  A linear programming framework (CC19LP) has been developed to study the balance between the expected fatality rate and the compliance level of key contact individuals.  A CC19LP online tool is being provided for public use by decision-makers.  An agent-based modeling (ABM) simulation is employed to study proposed control strategies.  A methodology that integrates ABM within two-stage stochastic programming is being developed.