COVID-19 AI-Enabled Supply Chain Optimization

By Erick C. Jones Jr.

July 19, 2020

EAGER: AI-Enabled Optimization of the COVID-19 Therapeutics Supply Chain to Support Community Public Health

NSF Award Number 2028612

This EAGER award supports fundamental research in technology-enabled supply chain design to effectively deliver therapeutics to at risk populations in an urban setting. The research has three primary objectives: 1) investigate the Automated Data Capture and Artificial Intelligence needed to automate the COVID-19 Healthcare Supply Chain; 2) model the COVID-19 Supply Chain from manufacture to home delivery that addresses the needs of at risk populations and communities; and 3) identify the readiness and the societal cost benefit of this model for use when medicine and supplies become ready for the COVID-19 outbreak Available data from HDHHS on location of vulnerable individuals and their social determinants of health will be integrated in an optimization-driven AI engine to target, map and assist health departments to prioritize their limited resources for response planning and to adapt their tactics to the needs of neighborhoods and communities.

UTA News Release

NSF Award

Arc GIS Map Presentation


Jones, E. C., Azeem, Gohar, Jones, Erick C., and Jefferson, F., “Impacting at Risk Communities using AI to optimize the COVID-19 Pandemic Therapeutics Supply Chain”, International Supply Chain Technology Journal (ISCTJ), Vol. 6, No. 9 September 2020. DOI:

Posted on:
July 19, 2020
1 minute read, 209 words
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