RAPID: Curtailing Nosocomial Amplification of COVID-19

在新出现的疫情中，受影响最严重的人群之一是一线医护人员。医护人员和医疗机构中的感染产生了两个独立但相关的挑战。首先，也是最明显的是，生病或垂死的医护人员无法照顾病人，在疫情可能导致对医疗系统的需求增加之际，导致劳动力短缺。第二个问题是，由于在医院内控制感染是多么困难，一旦到达医疗系统，疫情本身就会加速，导致病例数量迅速增加。这种现象的例子很常见，我们称之为“医院内放大”，包括新冠肺炎之前的重大冠状病毒疫情(非典型肺炎和中东呼吸综合征)以及埃博拉病毒。这个项目将寻求建立模型并了解医院内部的哪些因素可以防止这种情况发生，以增加医疗保健系统的弹性。这一努力的结果将为医疗保健系统的行为指南提供信息。该项目的其他更广泛的影响包括为学生提供培训和职业发展机会。研究人员将使现有的医院内感染传播模型适应新冠肺炎，并将该模型与北卡罗来纳州医疗机构的空间显式基于代理的模型相结合。这种方法既可以代表医疗保健环境，也可以代表最初感染病例的社区，医护人员可以在那里感染他们的社区，等等。它还允许对州一级决策的设施级影响进行建模，并使我们能够解决如何在患者和医护人员都面临巨大感染风险且PPE等关键用品不一定是无限的环境中最好地保护患者和医护人员的健康的问题。同时，他们将从SHEA研究网络的医院收集新冠肺炎给人员配备和临床实践带来的变化的数据，例如护士与患者的比例是否发生了变化，以及确定建模在医院决策中的使用程度，以及它在这一角色中是否有用。因此，这个项目将拥有一个稳健而复杂的模型，用于细粒度的医院与周围社区之间的互动，以及来自不同医院的及时参数估计，以说明新冠肺炎如何改变他们的做法，以及建模如何可能更好地为新冠肺炎和未来疫情的控制提供信息。该奖项反映了美国国家科学基金会的法定使命，并已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。

One of the populations hardest hit during an emerging epidemic are frontline healthcare workers. Infections in healthcare workers and in healthcare settings produce two separate but related challenges. The first, and most obvious, is that sick or dying healthcare workers cannot care for patients, causing labor shortages right at the moment when demand on a healthcare system is likely increasing due to the epidemic. The second problem is that, because of how difficult it is to control infections within hospitals, the epidemic itself can accelerate once it reaches the healthcare system, causing a rapid increase in the number of cases. Examples of this phenomena, which we call “nosocomial amplification”, are common, including both previous major coronavirus epidemics before COVID-19 (SARS and MERS) as well as Ebola. This project will seek to model and understand what factors within a hospital can prevent this from happening, to increase the resilience of healthcare systems. Outcomes from this effort will be informing behavioral guidelines for healthcare systems. Other broader impacts from this project include training and professional development opportunities for students. The researchers will adapt an existing model of within-hospital infection transmission to COVID-19 and combine this model with a spatially explicit agent-based model of the healthcare facilities in the state of North Carolina. This approach allows for the representation both of the healthcare environment, as well as the community where initial cases are seeded, where healthcare workers can infect – and be infected by – their community, etc. It also allows for the modeling of facility-level impacts of state-level decisions and allows us to address the question of how to best protect the health of both patients and healthcare workers in an environment where both are at significant risk of infection and critical supplies such as PPE are not necessarily unlimited. Simultaneously, they will collect data from hospitals in the SHEA Research Network on the changes brought on to staffing and clinical practice from COVID-19, such as whether or not the ratio of nurses to patients has changed, as well as ascertaining to what extent modeling has been used in hospital decision making, and whether or not it has been useful in that role. This project will thus have both a robust and sophisticated model for the interaction between hospitals at a granular level and the surrounding community, as well as timely parameter estimates from a diverse array of hospitals on how COVID-19 has changed their practices, as well as how modeling might be better tailored to inform the control of both COVID-19 and future epidemics.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.