EAGER: Data-Driven Susceptible-Exposed-Infected-Recovered-Infected (SEIRI) Modeling and Hospital Planning and Operations for COVID-19 Pandemic

这一早期概念探索性研究拨款(AGERGE)项目将为应对新冠肺炎大流行的医疗保健运营决策支持提供新的方法。由于冠状病毒具有高度传染性，在感染者中导致严重疾病的比例相对较高，预计在疫情最严重的时候，医疗资源，特别是医院病床和基本人员，将达到或超过负荷。该项目开发定量方法，以评估通过远程医疗进行筛查和监测以及非现场测试和诊断等干预措施如何减轻医院人员的负担。预计开发的模型将提高医院决策者预测设备需求的能力，以确保关键医院人员得到保护并能够提供有效的住院治疗。这一热切的奖项支持将susceptible-exposed-infected-recovered-infected(SEIRI)病毒传播模型与医院病床、人员配备和个人防护设备(PPE)的风险规避顺序操作规划模型相结合的方法的基础研究。业务规划模型包括一个随机优化模型，其中包括部署远程医疗进行初始筛查和监测，为诊断进行免下车测试，以及为患有严重疾病的人提供住院护理。该项目代表了PIS和梅奥诊所杰克逊维尔(MCJ)之间的紧密合作。模型参数将使用MCJ数据作为试点项目的一部分进行动态更新，一旦经过测试，决策支持系统将在线提供给国内和世界上的其他医院使用。这一奖项反映了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 EArly-concept Grant for Exploratory Research (EAGER) project will provide new methods for decision-support of healthcare operations in response to the COVID-19 pandemic. Because the coronavirus is highly contagious and results in severe disease in a relatively high percentage of those infected, healthcare resources, particularly hospital beds and essential personnel, are expected to be at or beyond capacity at the height of the epidemic. This project develops quantitative methods to estimate how interventions such as screening and surveillance via telemedicine and offsite testing and diagnosis can reduce the load on hospital personnel. The models developed are expected to improve the ability of hospital decision makers to predict equipment needs to ensure critical hospital personnel are protected and able to provide effective in-patient treatment.This EAGER award supports fundamental research in methods to integrate a susceptible-exposed-infected-recovered-infected (SEIRI) virus transmission model with a risk-averse sequential operational planning model for patient beds, staffing and personal protective equipment (PPE) in hospitals. The operational planning model comprises a stochastic optimization model that includes deployment of telemedicine for initial screening and surveillance, drive-through testing for diagnosis, and in-patient hospital care for those with severe disease. The project represents a tight collaboration between the PIs and Mayo Clinic Jacksonville (MCJ). The model parameters will be updated dynamically using MCJ data as part of a pilot project, and once tested, the decision-support system will be made available online for other hospitals in the nation and the world to use.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.