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

RAPID：支持评估医院应对 COVID-19 和其他流行病策略的模型的门户 - MASH-Pandemics

基本信息

批准号：
2027624
负责人：
Elise Miller-Hooks
金额：
$ 20万
依托单位：
George Mason University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2022-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027624&HistoricalAwards=false
关键词：
RAPID Portal Support Models Assessing

项目摘要

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.

这项快速反应研究（RAPID）拨款将开发流行病医院（MASH）评估策略模型（MASH-Pandemics）门户网站，并提供医院和地区在应对COVID-19大流行病时迫切需要的必要建模能力。将收集重要的易损坏、时间敏感的数据和资料，以支持这一努力。MASH-Pandemics将建立在以前开发的复杂，详细的基于离散事件模拟的医院容量和典型美国城市医院的能力分析模型的基础上。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，其结果将帮助医院管理者和地区制定最佳的运营变化和合作计划，通过州和国家紧急声明来应对COVID-19疫情。本集团将建立一个网上门户网站，在网站上发布有关应对COVID-19疫情的建模能力、实际发现和建议沿着潜在政策影响的详细信息。此外，来自医院、医院合作和地理区域的运行请求将通过门户网站进行。这项工作将产生关键的综合数据，以便在一个时间至关重要的时期提出快速建议和分析。主要产出将包括，例如：各种修改后的运营战略的潜力，以提高医院绩效和患者生存率，医院合作战略，以帮助区域响应，预测关键供应需求，以动员和优先考虑供应链的支持（或联邦反应能力），以及有效实施能力增强战略的建议（替代护理标准、改良手术、需求管理）。一旦该项目完成和流行病消退，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，尽快向医院和地区提供急需的建议。这个RAPID奖将推进数学建模技术，用于在危机期间捕获关键的医院服务。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、易腐烂的数据，并将生成关键的合成数据，用于在全球COVID-19大流行期间迫切需要的快速分析和预测。该项目采用定量方法，将通过确定有效利用极为有限的关键人员和物质资源的手段，加强医院的准备、能力和能力，这些资源的分配将影响到可能数以千计的生命的生存和保健工作人员沿着支助人员的安全。这项工作的结果将直接支持前线医院或COVID-19“热点”地区，提供机会请求运行并接收对COVID-19应对策略和协作机制有效性的分析。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。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。