MOdeling Nursing homes to Affect Response to COVID-19 (MONARC)

项目总结摘要 疗养院受到严重急性呼吸综合征冠状病毒2的打击尤为严重 (SARS-CoV-2)大流行。NHS可能是传播的震中，可能会继续帮助推动 总体流行，因为它们是复杂的、相互关联的卫生设施网络的关键部分 区域。然而，确定如何最好地预防/控制SARS-CoV-2在疗养院的传播 可能会很有挑战性。NH本身是一个复杂的系统，由NH居民/工作人员/访客组成，两者混合在一起 另一些则在一天中以不同的方式出现。因为NHS和他们所处的生态系统是复杂的 系统，整合了SARS的经济、运营和流行病学方面的计算模型- CoV-2可以为决策者提供关于如何最好地防止SARS-CoV-2传播的重要见解 在NHS内部和整个周边地区。这个拟议项目的总体目标是建模 养老院影响新冠肺炎反应(MONARC)，是开发基于代理的模型(ABM) 并使用这些模型来帮助设计和评估各种SARS-CoV2政策 和干预措施(例如，针对NH居民、NH工作人员和 访客)。此外，该项目将开发一种新的计算工具，供NH管理员和 然后，其他地区的公共卫生官员和政策制定者可以用来建立他们的NHS模型，以 用于做出有关新冠肺炎预防和应对的决策。这个项目将由两位经验丰富的人领导 研究人员和他们的团队在开发ABM方面合作了十多年 预防/控制传染病在医疗机构中的传播。自2007年以来，这包括帮助 决策者应对美国面临的几乎每一种主要传染病威胁，包括嵌入 卫生与公众服务部(HHS)在2009年甲型H1N1流感疫情期间。这个项目将是 我们过去的项目和我们现在的新冠肺炎冠状病毒建模工作。特定目标1将开发以下ABM 70名国民健康保险机构在OC中评估不同SARS-CoV-2症状筛查和新冠肺炎检测的影响 计时、频率、考试类型等策略。《特定目标2》将探讨各种 将新冠肺炎阳性的NH居民和关心他们的工作人员内部和跨部门进行队列划分的策略 不同的国民健康保险制度。特定目标3将开发一种计算工具，可以同时评估症状 筛查、测试和队列战略，以解决国民保健系统中的新冠肺炎问题，考虑到当地的流行率， 设施大小，并遵守感染预防标准。Monarcc项目将带来多个 创新包括：1)解决关于NHS可以做些什么的紧迫但目前尚未解决的问题 预防/控制SARS-CoV-2的传播，2)确定SARS-CoV-2的防控策略 应根据不同的NHS和NH居民和工作人员特点进行量身定做；3)制定 NHS可以用来帮助确定应对SARS-CoV-2的最佳策略的计算工具。

PROJECT SUMMARY ABSTRACT Nursing homes have been hit particularly hard by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. NHs may serve as epicenters of transmission that could continue to help fuel the overall pandemic because they are critical parts of complex, interconnected networks of health facilities in a region. However, determining how best to prevent/control the transmission of SARS-CoV-2 in nursing homes can be challenging. A NH itself is a complex system, consisting of NH residents/staff/visitors that mix with each other in different ways throughout a given day. Since NHs and the ecosystems that they sit within are complex systems, computational modeling that integrates economic, operational, and epidemiologic aspects of SARS- CoV-2 can provide decision makers with important insights on how best to prevent the spread of SARS-CoV-2 within NHs and throughout the surrounding region. The overall goal of this proposed project, MOdeling Nursing homes to Affect Response to COVID-19 (MONARC), is to develop agent-based models (ABMs) of the 70 NHs in OC and use these models to help design and evaluate various SARS-CoV2 policies and interventions (e.g., screening, testing, and cohorting strategies for NH residents, NH staff and visitors). Furthermore, the project will develop a new computational tool that NH administrators and public health officials and policymakers in other regions can then use to build models of their NHs to use to make decisions about COVID-19 prevention and response. This project will be led by two seasoned investigators and their teams who have worked together for over a decade on developing ABMs to prevent/control the spread of infectious diseases in healthcare facilities. Since 2007, this has included helping decision makers address nearly every major infectious disease threat to the U.S., including being embedded in Health and Human Services (HHS) during the 2009 H1N1 epidemic. This project will be a natural extension of our past projects and our current COVID-19 coronavirus modeling work. Specific Aim 1 will develop ABMs of the 70 NHs in OC to evaluate the impact of different SARS-CoV-2 symptom screening and COVID-19 testing strategies such as the timing, frequency, and test types. Specific Aim 2 will explore the value of various strategies to cohort COVID-19-positive NH residents and the staff who care for them, within and across different NHs. Specific Aim 3 will develop a computational tool that can simultaneously evaluate symptom screening, testing, and cohorting strategies to address COVID-19 in NHs, accounting for local prevalence, facility size, and adherence to infection prevention standards. The MONARC project will bring multiple innovations including: 1) addressing urgent but currently unaddressed questions about what NHs can do to prevent/control the spread of SARS-CoV-2, 2) determining how SARS-CoV-2 prevention and control strategies should be tailored by different NHs and NH resident and staff characteristics and 3) developing a computational tool that NHs can use to help determine the best strategies in response to SARS-CoV-2.