Award Abstract #2028612

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

See grant description on NSF site

Program Manager:

Georgia-Ann Klutke

Active Dates:

Awarded Amount:



Erick C Jones

Awardee Organization:

University of Texas at Arlington


Engineering (ENG)


The COVID-19 pandemic has proven to be particularly deadly in the nation's densely populated cities. This EArly-concept Grant for Exploratory Research (EAGER) will investigate methods that integrate Artificial Intelligence (AI), data science, and automatic data capture technologies to design supply mechanisms that effectively deliver therapeutic medicines to underserved urban communities that are particularly vulnerable to this this disease. While few reliable therapeutic treatments are currently available, as medicines and ultimately a vaccine are developed, the challenge will be to create an effective delivery mechanism to support urban communities. Because hospitals are expected to operate at capacity treating only the most severe cases, these new supply chains will focus on the home as point-of-care. The project represents a collaboration between the PI and the City of Houston Department of Health and Human Services (HDHHS) which currently supports the city's hospital districts, the veterans’ administration, and neighborhood healthcare centers. The supply chain models investigated in this project are expected to have wide applicability to similar large urban environments across the nation.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.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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