RAPID: Campus Coronavirus Response

RAPID：校园冠状病毒应对

• 批准号: 2029788
• 负责人: Moon Duchin
• 金额: $ 13.39万
• 依托单位: Tufts University
• 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: 2020-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029788&HistoricalAwards=false
• 关键词: RAPID; Campus; Coronavirus; Response

This COVID-19 RAPID research project will assist hospitals and first responders throughout the United States as the country responds to the global pandemic. As COVID-19 cases continue to strain limited hospital resources, college campuses with largely vacated dormitories are in a unique position to provide additional beds and facilities to hospitals. Dormitories can be repurposed as alternative hospital sites to house recovering patients, or other non-critical-care patients, and can offer temporary lodging to medical personnel – all coordinated through health systems and hospital networks across each state. The project team has initiated a mathematical model and mapping tool to efficiently match patients and medical personnel to vacant college dormitory beds. This project is poised to make a meaningful impact mitigating the burdens on hospitals, initially in Massachusetts and the Northeastern U.S. and then expanding across the U.S. The tools developed have potential to be useful immediately as well as in potential future “second waves” of COVID-19 infections. In addition, the model design and the visual tools are adaptable to other emergency response efforts. The tools this project develops have the potential to support decision-making any time one type of institution needs to turn to a second type of institution to expand its capacity.The project team has formulated their approach as a transport issue primarily, which they solve through a mixed integer program based on minimizing travel time. Their model primarily considers acute-care hospitals, although it could expand to include additional types of hospitals based on needs. The research effort behind this disaster assistance project includes investigating how various kinds of regularizations can be used to promote sparser matching in the linear integer program. Sparser matching would ensure that each hospital would collaborate with only one or a small number of universities and each university would receive patients/staff from only one or a small number of hospitals. Additional refinements may take hospitals with shared planning or logistics infrastructure into account, clustering them with the same universities. The team already developed a model for Massachusetts which they plan to scale to all 50 states using national datasets and engaging with appropriate hospital, university, and emergency management partners in those states.This RAPID award is made by the Convergence Accelerator program in the Office of Integrative Activities and is associated with the Convergence Accelerator Track A: Open Knowledge Networks.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.

这项COVID-19-Rapid Research项目将帮助整个美国的医院和急救人员，因为该国对全球大流行的反应。随着COVID-19案件继续限制有限的医院资源，具有腾空宿舍的大学校园处于独特的位置，可以为医院提供额外的床和设施。可以将宿舍重新用于替代医院现场，以容纳康复的患者或其他非关键护理患者，并可以向医务人员提供临时住宿 - 所有这些都通过每个州的卫生系统和医院网络进行了协调。该项目团队启动了一种数学模型和映射工具，以有效地将患者和医务人员与空置的大学宿舍床相匹配。该项目被毒死，以产生有意义的影响，以减轻伯恩斯对医院的影响，最初是在马萨诸塞州和美国东北部的，然后在美国扩展，该工具有可能立即有用，以及在Covid-19的潜在潜在的“第二波”感染中。此外，模型设计和视觉工具适用于其他紧急响应工作。该项目开发的工具有可能在任何一种类型的机构都需要转向第二类机构以扩大其能力的任何时候支持决策。项目团队已将其方法作为运输问题主要，他们通过最小化旅行时间的混合整数计划来解决。他们的模型主要考虑了急性护理医院，尽管它可以根据需求扩展到包括其他类型的医院。这个灾难援助项目背后的研究工作包括研究如何使用各种规范的措施来促进线性整数计划中的稀疏匹配。较少的匹配将确保每家医院仅与一所大学或少数大学合作，并且每所大学只会从一家或少数医院接受患者/员工。其他细化可能会考虑拥有共同计划或物流基础设施的医院，并将其与同一大学聚集。该团队已经开发了马萨诸塞州的模型，他们计划使用国家数据集将其扩展到所有50个州，并与这些州的适当医院，大学和紧急管理合作伙伴互动。这是由整合活动办公室中的融合加速器计划在整合活动办公室中颁发的，并与convergence Accelerator曲目A：Open Indown Internution awartion the Internution dection the Inderion dection dection dection dection dection dection the dection dection dection dection dection dest nsf nes fration dection dectory nsf nes nsf的批准相关。智力优点和更广泛的影响审查标准。