RAPID: Campus Coronavirus Response

这一新冠肺炎快速研究项目将在美国应对全球大流行时帮助美国各地的医院和急救人员。随着新冠肺炎病例继续给有限的医院资源带来压力，宿舍大部分空置的大学校园处于独特的地位，可以为医院提供更多床位和设施。宿舍可以被重新用作替代医院的场所，以容纳康复患者或其他非重症护理患者，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位相匹配。该项目将产生重大影响，减轻医院的负担，最初是在马萨诸塞州和美国东北部，然后扩展到美国各地。所开发的工具有可能立即有用，以及在未来可能出现的第二波新冠肺炎感染。此外，模型设计和可视化工具也适用于其他应急工作。这个项目开发的工具有可能在任何时候支持决策，只要一种类型的机构需要求助于第二种类型的机构来扩大其能力。项目团队将他们的方法主要作为一个交通问题来制定，他们通过一个基于最小化旅行时间的混合整数规划来解决这个问题。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。这一灾难援助项目背后的研究工作包括研究如何使用各种正则化来促进线性整数规划中的稀疏匹配。较稀疏的配对将确保每家医院只与一所或少数几所大学合作，每所大学将只接收一所或少数几所医院的病人/工作人员。额外的改进可能会考虑到拥有共享规划或后勤基础设施的医院，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。这一快速奖项由整合活动办公室的融合加速器计划颁发，并与融合加速器轨道A：开放知识网络相关。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.