Regional Healthcare Ecosystem Analyst (RHEA) Modeling the Environment (MODE): SARS-CoV-2

项目摘要 随着COVID-19冠状病毒大流行的持续，严重肺炎的潜在环境传播 急性呼吸道综合征冠状病毒2型（SARS-CoV-2）是一个重要的问题，特别是在 医院的选择和协调正确的方法（例如，环境清洁和监测， 气流调节）在复杂的医院环境中可能是具有挑战性的， 周转、有限的资源和SARS-CoV-2的潜在快速传播。此外，开发新的 这些办法需要在设计和执行方面得到指导。计算模型与经济， 业务和流行病学组成部分可以评估具有各种特征的方法的价值， 在复杂系统中指导设计和实现的有效性。我们的区域医疗保健生态系统 分析师（RHEA）建模环境（RHEA-MODE）项目已经将开发基于代理的 模型（ABM），以帮助更好地了解和预防耐甲氧西林的环境传播 金黄色葡萄球菌（MRSA）和万古霉素耐药肠球菌（VRE），这两种病原体通常 导致医疗相关感染（HAI）。这提供了一个关键的机会，询问和回答类似的问题， 关于SARS-CoV-2的问题因此，本建议的RHEA-MODE：SARS-CoV-2的目标 一个补充项目是开发医院的ABM，以帮助更好地了解医院的作用 环境和环境清洁和监测方法，预防和控制 SARS-CoV-2的传播。虽然MRSA和VRE可能有一些相似之处，但其特征（例如， 接触，空气传播）和后果（例如，SARS-CoV-2的各种COVID-19结果）不同， 在ABM中需要不同的表示。该病毒还需要不同的干预措施（例如，n95口罩 使用）和可能不同的环境清洁（例如，更积极的标准消毒剂使用，新 程序如紫外线照射，空气过滤）和监测（例如，检查是否符合清洁要求 协议和空气中和表面上病毒的存在）。我们的团队由布鲁斯Y领导。Lee，MD MBA， 他是传染病病原体研究模型（MIDAS）网络的一部分，已有12年以上的历史， 在工业和学术界拥有二十多年的经验，领导大型数学和计算 建模项目，以更好地了解，预防和控制传染病，包括嵌入 在H1N1流感大流行期间，美国卫生与公众服务部协助国家应对。 该项目的具体目标1将开发样本医院的详细计算表示， 他们的环境，并确定医院环境在SARS-CoV-2传播中的作用 在不同的条件和情况下。具体目标2将探索如何进行各种环境清洁 监测产品、方法、途径和策略可以减少SARS-CoV-2的传播、扩散， 以及基于目标1的模拟模型的相关健康和经济结果。

PROJECT SUMMARY ABSTRACT With the ongoing COVID-19 coronavirus pandemic, the potential environmental transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is of significant concern, especially in hospitals. Choosing and coordinating the right approaches (e.g., environmental cleaning and monitoring, airflow regulation) in the complex hospital environment can be challenging, given frequent patient and staff turnover, limited resources, and the potential rapid spread of SARS-CoV-2. Further, developing new approaches requires guidance for design and implementation. Computational modeling with economic, operational, and epidemiologic components can assess the value of approaches with various features and efficacies to guide design and implementation in complex systems. Our Regional Healthcare Ecosystem Analyst (RHEA) Modeling the Environment (RHEA-MODE) project already will be developing agent-based models (ABMs) to help better understand and prevent the environmental transmission of methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE), two pathogens that commonly cause healthcare-associated infections (HAIs). This offers a key opportunity to ask and answer similar questions about SARS-CoV-2. Therefore, the goal of this proposed RHEA-MODE: SARS-CoV-2 supplemental project is to develop ABMs of hospitals to help better understand the role of the hospital environment and environmental cleaning and monitoring methods in preventing and controlling the spread of SARS-CoV-2. While there may be some similarities with MRSA and VRE, the characteristics (e.g., contact, air transmission) and consequences (e.g., various COVID-19 outcomes) of SARS-CoV-2 are different, requiring different representations in the ABMs. The virus also requires different interventions (e.g., N95 mask use) and potentially different environmental cleaning (e.g., more aggressive standard disinfectant use, new procedures like ultraviolet light irradiation, air filtering) and monitoring (e.g., checking compliance with cleaning protocols and for the presence of virus in the air and on surfaces). Our team is led by Bruce Y. Lee, MD MBA, who has been part of the Models of Infectious Disease Agent Study (MIDAS) network for over 12 years and has over two decades of experience in industry and academia leading large mathematical and computational modeling projects to better understand, prevent, and control infectious diseases, including being embedded in the U.S. Department of Health Human Services during the H1N1 flu pandemic to assist the national response. Specific Aim 1 for this project will develop detailed computational representations of sample hospitals and their environments and determine the role of the hospital environment in the transmission of SARS-CoV-2 under various conditions and circumstances. Specific Aim 2 will explore how various environmental cleaning and monitoring products, methods, approaches, and strategies can reduce SARS-CoV-2 transmission, spread, and associated health and economic outcomes based upon the simulation models from Aim 1.