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

EAGER：人工智能支持优化 COVID-19 治疗供应链以支持社区公共卫生

基本信息

批准号：
2028612
负责人：
Erick Jones
金额：
$ 20万
依托单位：
University of Texas at Arlington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2023-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028612&HistoricalAwards=false
关键词：
EAGER AI Enabled Optimization COVID

项目摘要

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.

事实证明，在该国人口稠密的城市中，Covid-19-19尤其是致命的。这项探索性研究的早期概念赠款（急切）将调查整合人工智能（AI），数据科学和自动数据捕获技术的方法，以设计供应机制，这些供应机制有效地为服务不足的城市社区提供治疗性药物，这些社区特别容易受到这种疾病。尽管目前很少有可靠的治疗治疗方法，但由于药物和最终开发了一种疫苗，挑战将是创建一种有效的送货机制来支持城市社区。由于预计医院只能治疗最严重的病例，因此这些新的供应链将集中在房屋上。该项目代表了PI与休斯顿市卫生与公共服务部（HDHHS）之间的合作，目前支持该市医院地区，退伍军人政府和邻里医疗保健中心。预计该项目中调查的供应链模型将在全国各地的类似大型城市环境中具有广泛的可应用性。该渴望的奖项支持在技术支持供应链设计中的基本研究，以在城市环境中有效地将理论传递给风险人群。该研究具有三个主要目标：1）研究自动化Covid-19医疗保健供应链所需的自动数据捕获和人工智能； 2）建模从制造业到送货上门的Covid-19供应链，以满足风险人群和社区的需求； and 3) identify the readiness and the social cost benefits 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 determiners 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任务，并通过评估使用基金会的知识分子和更广泛的影响审查标准，被认为是宝贵的支持。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Understanding the Last Mile Transportation Concept Impacting Underserved Global Communities to Save Lives During COVID-19 Pandemic

了解最后一英里交通概念影响服务欠缺的全球社区以在 COVID-19 大流行期间拯救生命

DOI：
10.3389/ffutr.2021.732331
发表时间：
2021
期刊：
Frontiers in Future Transportation
影响因子：
0
作者：
Jones, Erick C.;Azeem, Gohar;Jones, Erick C.;Jefferson, Felicia;Henry, Marcia;Abolmaali, Shannon;Sparks, Janice
通讯作者：
Sparks, Janice

AI Based Remotely monitoring Web User Interface to capture patient temperature and medicine consumption

基于人工智能的远程监控网络用户界面，以捕获患者体温和药物消耗量