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

事实证明，新冠肺炎疫情在中国人口稠密的城市尤为致命。这项早期概念探索性研究补助金(AGER)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，以设计有效地向服务不足的城市社区提供治疗药物的供应机制，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，挑战将是创建一种有效的提供机制来支持城市社区。由于医院预计将只治疗最严重的病例，因此这些新的供应链将把重点放在家庭作为护理地点。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，HDHHS目前支持该市的医院区、退伍军人管理局和社区医疗中心。本项目中研究的供应链模型有望广泛适用于全国类似的大城市环境。这一热切的奖项支持基于技术的供应链设计的基础研究，以有效地向城市环境中的高危人群提供治疗。这项研究有三个主要目标：1)调查自动数据捕获和人工智能，以实现新冠肺炎医疗供应链的自动化；2)建立从制造到送货上门的新冠肺炎供应链模型，以满足高危人群和社区的需求；以及3)确定当药品和用品准备好应对新冠肺炎疫情时使用此模型的准备情况和社会成本效益。HDHHS提供的有关易感人群位置及其健康的社会决定因素的数据将被集成到一个优化驱动的人工智能引擎中，以针对、绘制地图并协助卫生部门优先考虑其有限的资源以进行响应规划，并根据社区和社区的需求调整他们的策略。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.