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

事实证明，新冠肺炎大流行在美国人口稠密的城市尤其致命。这项探索性研究（EAGER）早期概念资助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，有效地向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。虽然目前可靠的治疗方法很少，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，以支持城市社区。由于预计医院将满负荷运转，只治疗最严重的病例，这些新的供应链将把重点放在家庭作为护理点。该项目代表了PI和休斯敦市卫生与人类服务部（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保健中心。本项目研究的供应链模型有望在全国类似的大型城市环境中具有广泛的适用性。该奖项支持技术支持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。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数据捕获和人工智能；2)建立从制造到送货上门的COVID-19供应链模型，以满足风险人群和社区的需求；3)确定该模型的准备情况和社会成本效益，以便在准备好应对COVID-19疫情的药品和用品时使用。HDHHS关于弱势个人及其健康的社会决定因素的现有数据将被整合到一个优化驱动的人工智能引擎中，以确定目标、绘制地图并协助卫生部门优先考虑其有限的资源，以进行应对规划，并根据社区和社区的需求调整其策略。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知识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.