| NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
| Recipient: |
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| Initial Amendment Date: | April 17, 2020 |
| Latest Amendment Date: | January 27, 2023 |
| Award Number: | 2028612 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Georgia-Ann Klutke
gaklutke@nsf.gov (703)292-2443 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
| Start Date: | May 1, 2020 |
| End Date: | April 30, 2023 (Estimated) |
| Total Intended Award Amount: | $199,993.00 |
| Total Awarded Amount to Date: | $199,993.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
701 S NEDDERMAN DR ARLINGTON TX US 76019-9800 (817)272-2105 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
416 Yates Street Arlington TX US 76019-0017 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | COVID-19 Research |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.041 |
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The major goal of this project is to 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.
The research had 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.
We were able to develop an optimized Supply Chain model of COVID-19 vaccines for underserved communities. We used a case of City of Houston by collaborating with City of Houston Health Department (HHD) and ran 8 different scenarios using the data related to COVID-19 and demographic data for city of Houston. The results were published in the high impact journal "Frontiers for Future Transportation" and output from our model for all different scenarios were shared with City of Houston Health Department (HHD) that helped them in devising their further strategies.
Next, we developed an AI algorithm using standard and IR cameras to identify when subjects might have COVID based on body temperature and facial inflammation. We also investigated how to use AI to verify if patients took their medicines. The work associated with this task was published in ISCTJ.
The last part of our project was to work on integrating the automated data capturing technologies with the supply chain model to make it more robust and adapt to real life changes. This work if deployed could have informed the City of Houston how to adjust their supply chain model in real time by quickly identifying probably hotspots using the AI data and then deploying resources to combat that. However, fortunately the vaccine was developed while we were working on this and the idea was no longer needed. However, it could be applied to future diseases and was published.
The major goal of this project is to 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.
Intellectual Merit
We identified two big areas of innovation in our work: 1) Incorporating predictive modeling into optimization (prescriptive) models; 2) Balancing non-traditional metrics such as equity, sustainability, and disease spread, and resilience in supply chain models.
Broader Impacts
One of this project’s main objectives was to reduce use technology and existing metrics to reduce the impacts of COVID on underserved populations. Although, COVID is winding down this research clearly can be applied to many supply chains where lack of intentionality can lead to widely inequitable outcomes.
The main products produced by the EAGER grant as of now are as follows:
1) Using Cameras and AI to identify potential disease outbreaks
- Jones, E. C., Henry, M. B., Sadananda, N., & Parsnani, J. (2022). Artificial Intelligence Platform on Mobile Devices to Assess Consumption of Pill in Subjects with Alzheimer. International Supply Chain Technology Journal, 8(04). https://isctj.com/index.php/isctj/article/view/248
2) Incorporating in social science metrics like Social Determinants of Health into a supply chain model
- Jones, E., Azeem, G., II, E. C. J., & Jefferson, F. (2020). Impacting at Risk Communities using AI to optimize the COVID-19 Pandemic Therapeutics Supply Chain. International Supply Chain Technology Journal, 6(9). https://doi.org/doi.org/10.20545/isctj.v06.i09.02
3) Model the Houston Health Department Supply Chain as an optimization model and determine which zip codes should receive resources based on the given parameters.
- Jones, E. C., Azeem, G., Jones, E. C. Jr., Jefferson, F., Henry, M., Abolmaali, S., & Sparks, J. (2021). Understanding the Last Mile Transportation Concept Impacting Underserved Global Communities to Save Lives During COVID-19 Pandemic. Frontiers in Future Transportation, 0, 25. https://doi.org/10.3389/FFUTR.2021.732331
Last Modified: 08/21/2023
Modified by: Erick C Jones
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