RAPID: A Portal to Support Models for Assessing Strategies for Hospitals in the COVID-19 and other Pandemics - MASH-Pandemics

这笔快速反应研究（Rapid）赠款将开发流行病医院战略评估模型（MASH）门户网站，提供医院和地区在应对COVID-19大流行时迫切需要的必要建模能力。将收集支持这一努力的重要易逝、时效性强的数据和信息。mash - pandemic将建立在先前开发的基于典型美国城市医院的复杂、详细的离散事件模拟的医院容量和能力分析模型之上。该RAPID项目将支持这些模型的重新规范、数据收集、模型运行和结果分析，其结果将有助于医院管理人员和地区制定最佳的业务变更和协作计划，并通过州和国家紧急声明来应对COVID-19疫情。将建立一个在线门户网站，在该门户网站上发布有关应对COVID-19大流行的建模能力、实际发现和建议以及潜在政策影响的详细信息。此外，将通过门户接收来自医院、医院协作和地理区域的运行请求。这项工作将产生关键的综合数据，以便在时间紧迫的时期内制定快速建议和分析。例如，关键产出将包括：改进各种业务战略以促进医院绩效和患者生存的潜力、帮助区域应对的医院协作战略、预测关键供应需求以调动供应链（或联邦应对能力）的支持并确定其优先次序，以及有效实施能力增强战略（替代护理标准、改进操作、需求管理）的建议。一旦项目完成，流行病消退，该项目将为今后的教育活动提供投入。在执行期间，这项工作的重点将是根据高质量模型运行的结果，尽快向医院和区域提供急需的建议。该RAPID奖将推进数学建模技术，以便在危机期间捕捉关键的医院服务。它采用开放排队网络、离散事件模拟、随机建模、瞬态系统分析和统计方法的概念。这项工作将收集关键的、易变质的数据，并生成关键的综合数据，以便在2019冠状病毒病全球大流行期间进行快速分析和预测。该项目采用定量方法，通过确定有效利用严重有限的关键人员和物质资源的方法，将提高医院的准备情况、能力和能力，这些资源的分配将影响到可能成千上万人的生命的生存以及保健工作者和支助人员的安全。这一努力的成果将直接支持一线医院或COVID-19“热点”地区，提供请求运行和接收COVID-19应对战略和协作机制有效性分析的机会。预计运行请求将以各种形式出现，需要数据收集，建模工作，调查以捕获具有输入分布和参数的随机过程，以及结果分析。这些模型可以迅速得到加强和动员，初步调查结果和建议可以在几周内公布。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。

This Rapid Response Research (RAPID) grant will develop the Models for Assessing Strategies for Hospitals (MASH) in Pandemics (MASH-Pandemics) Portal with requisite modeling capabilities urgently needed by hospitals and regions in responding to the COVID-19 pandemic. Important perishable, time-sensitive data and information to support this effort will be collected. MASH-Pandemics will build on previously developed sophisticated, detailed discrete-event simulation-based hospital capacity and capability analysis models of typical U.S. urban hospitals. This RAPID project will support the re-specification of these models, data collection, model runs, and results analysis, outcomes from which will aid hospital administrators and regions in making optimal operational changes and collaboration plans enabled through state and national emergency declarations in response to the COVID-19 outbreak. An online portal will be constructed on which details of the modeling capabilities, practical findings and recommendations, along with potential policy implications, for responding to the COVID-19 pandemic will be posted. Additionally, run requests from hospitals, hospital collaborations and geographical regions will be taken through the portal. This work will generate crucial synthetic data needed to develop quick recommendations and analyses in a period where time is of the essence. Key outputs will include, for example: potential for various modified operational strategies to benefit hospital performance and patient survival, hospital collaboration strategies to aid regional response, anticipating critical supply needs to mobilize and prioritize support from supply chains (or Federal response capabilities), and recommendations for effective implementation of capacity enhancement strategies (alternative standards of care, modified operations, demand management). The project will provide input to educational activities in the future, once the project is complete and the pandemic subsides. The focus of this work during the period of performance will be on providing, as quickly as possible, crucially needed recommendations to hospitals and regions based on results from runs of high-quality models. This RAPID award will advance mathematical modeling techniques for capturing critical hospital services during crises. It employs concepts of open queuing networks, discrete event simulation, stochastic modeling, transient system analysis, and statistical methods. The work will collect critical, perishable data, and will generate crucial synthetic data for rapid analysis and prediction urgently needed in this period of a global COVID-19 pandemic. With its quantitative approach, the project will enhance hospital readiness, capacity and capability, by identifying means for efficiently using severely limited, critical personnel and physical resources, the allocation of which will affect the survival of potentially thousands of lives and the safety of health care workers along with support staff. Findings from this effort will directly support hospitals at the front line, or regions in COVID-19 “hot spots,” by providing the opportunity to request runs and receive analyses of the effectiveness of COVID-19 response strategies and collaboration mechanisms. It is anticipated that the run requests will come in a variety of forms, requiring data collection, modeling work, investigation to capture stochastic processes with input distributions and parameters, and results analyses. The models can be quickly enhanced and mobilized, and initial findings and recommendations made public in only weeks.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.