RAPID: Curtailing Nosocomial Amplification of COVID-19

RAPID：减少 COVID-19 的医院内传播

• 批准号: 2110109
• 负责人: Eric Lofgren
• 金额: $ 19.54万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110109&HistoricalAwards=false
• 关键词: RAPID; Curtailing; Nosocomial; Amplification; COVID

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.

在新出现的流行病中，受影响最严重的人群之一是一线医护人员。医疗工作者和医疗机构中的感染产生了两个独立但相关的挑战。首先，也是最明显的一点是，生病或垂死的医护人员无法照顾病人，在疫情可能导致医疗系统需求增加之际，造成劳动力短缺。第二个问题是，由于控制医院内的感染非常困难，一旦传染到医疗系统，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，我们称之为“医院放大”，是常见的，包括COVID-19（SARS和MERS）之前的主要冠状病毒流行病以及埃博拉病毒。该项目将试图建模并了解医院内的哪些因素可以防止这种情况发生，以提高医疗保健系统的弹性。这项工作的成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专业发展机会。研究人员将调整现有的医院内感染传播模型以适应COVID-19，并将该模型与北卡罗来纳州医疗设施的空间显式代理模型结合联合收割机。这种方法允许代表医疗保健环境以及初始病例被播种的社区，其中医疗保健工作者可以感染-并被他们的社区感染，它还允许对国家的设施级影响进行建模-水平的决策，使我们能够解决如何最好地保护病人和医护人员的健康的环境中，两者都有很大的感染风险，而且诸如个人防护装备等关键用品不一定是无限的。与此同时，他们将从SHEA研究网络中的医院收集有关COVID-19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，以及确定模型在医院决策中的应用程度，以及它在这方面是否有用。因此，该项目将为医院与周围社区之间的互动提供一个强大而复杂的模型，并从各种医院及时估计COVID-19如何改变他们的做法，以及如何更好地定制模型，以通知COVID-19的控制，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。

项目成果

Estimate of undetected severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection in acute-care hospital settings using an individual-based microsimulation model

使用基于个体的微观模拟模型估计急症护理医院环境中未检测到的严重急性呼吸道冠状病毒 2 (SARS-CoV-2) 感染

• DOI: 10.1017/ice.2022.174
• 发表时间: 2022
• 期刊: Infection Control & Hospital Epidemiology
• 影响因子: 4.5
• 作者: Jones, Kasey;Hadley, Emily;Preiss, Sandy;Lofgren, Eric T.;Rice, Donald P.;Stoner, Marie C.;Rhea, Sarah;Adams, Joëlla W.
• 通讯作者: Adams, Joëlla W.