RAPID: Curtailing Nosocomial Amplification of COVID-19

在新出现的流行病期间，受影响最严重的人群之一是一线卫生保健工作者。卫生保健工作者和卫生保健环境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，导致劳动力短缺，而此时对医疗系统的需求可能因疫情而增加。第二个问题是，由于在医院内控制感染非常困难，疫情本身一旦到达医疗系统就会加速，导致病例数量迅速增加。我们称之为“医院放大”的这种现象很常见，包括在COVID-19之前的两次重大冠状病毒流行（SARS和MERS）以及埃博拉病毒。该项目将试图建立模型并了解医院内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。这项工作的成果将为卫生保健系统的行为指南提供信息。这个项目的其他更广泛的影响包括为学生提供培训和专业发展机会。研究人员将采用现有的医院内感染传播模型来适应COVID-19，并将该模型与北卡罗来纳州医疗保健设施的空间明确的基于主体的模型相结合。这种方法既可以代表医疗保健环境，也可以代表最初出现病例的社区、医疗保健工作者可能感染或被其社区感染的社区等。它还允许对州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卫生保健工作者都面临重大感染风险的环境中最好地保护他们的健康的问题，而且个人防护装备等关键物资不一定是无限的。同时，他们将从SHEA研究网络的医院收集数据，了解COVID-19给人员配备和临床实践带来的变化，例如护士与患者的比例是否发生了变化，以及确定在医院决策中使用模型的程度，以及模型在该角色中是否有用。因此，该项目将拥有一个强大而复杂的模型，用于医院与周围社区之间的微观互动，以及来自各种医院的及时参数估计，了解COVID-19如何改变他们的做法，以及如何更好地定制模型，为COVID-19和未来流行病的控制提供信息。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。

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.