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.

这个COVID-19 RAPID研究项目将帮助美国各地的医院和急救人员应对全球大流行。由于COVID-19病例继续使有限的医院资源紧张，大部分空置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位和设施。宿舍可以被重新利用为替代医院场所，以容纳康复患者或其他非重症监护患者，并可以为医务人员提供临时住宿-所有这些都通过各州的卫生系统和医院网络进行协调。该项目团队已经启动了一个数学模型和绘图工具，以有效地将患者和医务人员与空置的大学宿舍床位相匹配。该项目有望产生有意义的影响，减轻医院的负担，最初在马萨诸塞州和美国东北部，然后扩展到整个美国。开发的工具有可能立即有用，以及在未来潜在的COVID-19感染的“第二波”。此外，模型设计和可视化工具可适用于其他应急响应工作。该项目开发的工具有可能在一种类型的机构需要转向第二种类型的机构以扩大其容量的任何时候支持决策。项目团队已经制定了他们的方法，主要是作为一个交通问题，他们通过基于最小化旅行时间的混合整数规划来解决。他们的模式主要考虑急性护理医院，尽管它可以根据需要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调查如何使用各种正则化来促进线性整数规划中的稀疏匹配。更稀疏的匹配将确保每家医院只与一所或少数几所大学合作，而每所大学只接收来自一所或少数几所医院的病人/工作人员。进一步的改进可能会考虑到具有共享规划或物流基础设施的医院，将它们与相同的大学聚集在一起。该团队已经为马萨诸塞州开发了一个模型，他们计划使用国家数据集并与这些州的适当医院，大学和应急管理合作伙伴合作，将其扩展到所有50个州。该RAPID奖由整合活动办公室的融合加速器计划颁发，与融合加速器轨道A相关：开放知识网络。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进行评估，被认为值得支持。