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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，COVID-19大流行在美国人口稠密的城市尤其致命。 EARLY概念探索性研究补助金（EAGER）将研究整合人工智能（AI），数据科学和自动数据捕获技术的方法，以设计供应机制，有效地为服务不足的城市社区提供治疗药物，这些社区特别容易受到这种疾病的影响。虽然目前几乎没有可靠的治疗方法，但随着药物和最终疫苗的开发，挑战将是建立一个有效的提供机制来支持城市社区。由于医院预计将满负荷运转，只治疗最严重的病例，这些新的供应链将集中在家庭作为护理点。该项目代表了PI和休斯顿市卫生与公众服务部（HDHHS）之间的合作，该部门目前支持该市的医院区，退伍军人管理局和社区医疗中心。该项目中研究的供应链模型预计将广泛适用于全国类似的大型城市环境。EAGER奖支持技术支持的供应链设计的基础研究，以有效地为城市环境中的风险人群提供治疗。该研究有三个主要目标：1）调查自动化COVID-19医疗保健供应链所需的自动化数据采集和人工智能; 2）模拟从制造到送货上门的COVID-19供应链，以满足风险人群和社区的需求;以及3）确定当药物和供应品为COVID-19爆发做好准备时，该模型的准备程度和社会成本效益来自HDHHS的关于弱势个体位置及其健康社会决定因素的可用数据将被整合到优化驱动的AI引擎中，绘制并协助卫生部门优先考虑有限的资源用于响应规划，并使其策略适应社区和社区的需求。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。

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