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.

这项快速响应研究（快速）将开发用于评估医院策略（MASH）（MASH-PANDEMICS）门户网站的模型，并在响应COVID-19-19大流行时急需急需医院和地区所需的建模能力。将收集重要的易腐烂，时间敏感的数据和信息，以支持这项工作。 Mash-Pandemics将基于先前开发的基于典型的美国城市医院的基于详细的，详细的离散仿真的医院能力分析模型。这个快速项目将支持对这些模型，数据收集，模型运行和结果分析的重新指定，结果将帮助医院管理员和地区通过州和国家紧急声明对COVID-19的最佳运营变更和协作计划进行最佳的操作和协作计划。将构建一个在线门户网站，以在哪些模型功能，实用发现和建议以及潜在的政策含义上进行构建，以响应Covid-19-19的大流行。此外，将通过门户网站提出医院，医院合作和地理区域的运行请求。这项工作将产生至关重要的综合数据，以在时间本质上进行快速建议和分析。关键产出将包括：例如：各种修改后的操作策略的潜力，以使医院绩效和患者的生存受益，医院协作策略，以帮助区域响应，预计需要进行的关键供应需求，以动员和优先考虑供应链（或联邦响应能力）的支持，以及有效实施能力增强策略的建议（替代性能，改良的操作标准），要求管理。该项目将来将为教育活动提供意见，一旦在绩效期间的这项工作的重点将根据高质量模型的运行结果，将尽快向医院和地区提供完全需要的建议。这项快速奖励将推进危机期间捕获关键医院服务的数学建模技术。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，并将生成至关重要的合成数据，以便在整个Covid-19的这一时期急需进行快速分析和预测。通过定量方法，该项目将通过有效地使用严重有限的，关键的人员和身体资源来确定手段来增强医院的准备，能力和能力，其分配将影响可能成千上万的生活以及卫生保健工作者的安全以及支持人员的安全。这项工作的发现将直接支持前线的医院或Covid-19“热点”的地区，通过提供机会要求运行并接受COVID-19响应策略和协作机制的有效性分析。预计运行请求将以各种形式出现，需要数据收集，建模工作，投资以捕获具有输入分布和参数的随机过程以及结果分析。这些模型可以快速增强和动员，并仅在一周内就公开了初步发现和建议。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准通过评估被认为是珍贵的支持。