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
Links:
- Podcast by the Institute for Operations Research & the Management Sciences (INFORMS)
- Key Contact Partitioning Questionnaire: Am I a key contact individual, a sheltered high-risk individual, or an unrestricted low-risk individual?
- Advice for Schools
- Advice for Businesses and Organizations
- CC19LP C3.ai Online Tool. Contact Dr. Chen for more information (vchen@uta.edu).
- IMSE Blogpost July 17, 2020
- IMSE Blogpost August 2, 2020
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.
- Chen, V. C. P., Y. Zhou, A. Fallahi, A. Viswanatha, J. Yang, Y. Ghasemi, N. Ohol, J. M. Rosenberger, F. Liu, X. Ju, and J. B. Guild (2020). An Optimization Framework to Study the Balance Between Expected Fatalities due to COVID-19 and the Re-opening of U.S. Communities. COSMOS Technical Report 20-02A (May 14, 2020). The University of Texas at Arlington, Arlington, TX.
- Chen, V. C. P., Y. Zhou, A. Fallahi, A. Viswanatha, J. Yang, Y. Ghasemi, N. Ohol, J. M. Rosenberger, F. Liu, X. Ju, and J. B. Guild (2020). An Optimization Framework to Study the Balance Between Expected Fatalities due to COVID-19 and the Re-opening of U.S. Communities. COSMOS Technical Report 20-02B (June 16, 2020). The University of Texas at Arlington, Arlington, TX.
- Chen, V. C. P., Y. Zhou, A. Fallahi, A. Viswnatha, J. Yang, Y. Ghasemi, N. Ohol, J. M. Rosenberger, F. Liu, X. Ju, and J. B. Guild (2020). An Optimization Framework to Study the Balance Between Expected Fatalities due to COVID-19 and the Re-opening of U.S. Communities, medRxiv, DOI 10.1101/2020.07.16.20152033v1.
