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

项目总结摘要 随着新冠肺炎冠状病毒大流行的持续，潜在的环境传播严重 急性呼吸综合征冠状病毒2(SARS-CoV-2)令人严重关切，特别是在 医院。选择和协调正确的方法(例如，环境清洁和监测， 在复杂的医院环境中，考虑到频繁的患者和工作人员，气流调节)可能具有挑战性 营业额、有限的资源以及SARS-CoV-2的潜在快速传播。进一步，开发新的 方法需要为设计和实施提供指导。计算建模具有经济、 操作和流行病学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施的效率。我们的区域医疗生态系统 分析师(RHEA)环境建模(RHEA-MODE)项目将开发基于代理的 帮助更好地了解和预防耐甲氧西林病毒在环境中传播的模型(ABM) 金黄色葡萄球菌(MRSA)和万古霉素耐药肠球菌(VRE)是两种常见的致病菌 引起医疗保健相关感染(HAI)。这提供了一个询问和回答类似问题的关键机会 关于SARS-CoV-2的问题。因此，这种提出的RHEA模式的目标是：SARS-CoV-2 补充项目是开发医院的ABM，以帮助更好地了解医院的角色 预防和控制环境污染的环境和环境清洁与监测方法 SARS-CoV-2的传播。虽然可能与MRSA和VRE有一些相似之处，但其特征(例如， SARS-CoV-2的接触(接触、空气传播)和后果(例如各种新冠肺炎后果)是不同的， 在ABM中需要不同的表示。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，新的 紫外线照射、空气过滤等程序)和监测(例如，检查清洁情况 协议以及空气中和表面上的病毒存在)。我们的团队由李小龙领导，他是工商管理硕士， 12年来，世卫组织一直是传染病代理人研究(MIDAS)网络模型的一部分， 在工业界和学术界有二十多年的经验，领导着大型数学和计算 建模项目以更好地了解、预防和控制传染病，包括嵌入到 美国卫生部公共服务部在H1N1流感大流行期间协助国家应对。 该项目的具体目标1将开发样本医院的详细计算表示法 并确定医院环境在SARS-CoV-2传播中的作用 在各种条件和情况下。具体目标2将探索各种环境清洁如何 而监测产品、方法、途径和策略可以减少SARS-CoV-2的传播，传播， 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.