Suppr超能文献

苏塞克斯郡新冠病毒建模小组:地方当局公共卫生团队、国民保健服务医院信托机构、国民保健服务专员及大学之间合作的方法与成果

The Sussex COVID-19 Modelling Cell: the methods and successes of a collaboration between public health teams in local authorities, NHS hospital trusts, NHS commissioners, and universities.

作者信息

Van Yperen James, Campillo-Funollet Eduard, Memon Anjum, Allman Phil, Clay Jacqueline, Dorey Matt, Evans Graham, Gilchrist Kate, Madzvamuse Anotida

机构信息

Department of Mathematics, University of Sussex, Brighton, UK.

Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.

出版信息

Lancet. 2023 Nov;402 Suppl 1:S94. doi: 10.1016/S0140-6736(23)02086-X.

Abstract

BACKGROUND

The Sussex Modelling Cell (SMC) is a consortium, formed during the COVID-19 pandemic, of representatives from NHS Sussex, and the local authorities and universities in Sussex. The SMC aimed to provide public health teams with local-data-driven modelling, data analysis, and policy and commissioning advice to mitigate the impact of the pandemic on the local population. It also aimed to answer operational questions, since the Government's forecasts were not suitably applicable.

METHODS

From March 23, 2020, the SMC met (virtually) every Thursday to monitor COVID-19 situation reports, answer queries related to data and modelling, and provide interpretations of data or reports from many internal and external sources. SMC also provided quantitative information for public health teams to use within their organisations to advise on the local epidemic picture. Among other tools, the SMC calibrated a mathematical model to local COVID-19 data that could forecast health-care and hospital demand and COVID-19-related deaths.

FINDINGS

Throughout the pandemic, the SMC provided scientific and data-driven evidence on the necessity of body storage contracts, monetary support for urgent care, and operational adjustments surrounding health-care provisions. The scientific evidence was generated and used repeatedly in each organisation to make beneficial decisions in a time of crisis. Although chasing an ever-changing pandemic picture was challenging, our swift reaction to national policy and pandemic changes allowed us to support policymakers, reduce anxiety, and provide clarity on the next steps. Our collaboration is one among few across the country and thus should be not only celebrated but also replicated, with appropriate resources and funding.

INTERPRETATION

Besides mitigating the direct impact of the COVID-19 situation in Sussex, we have established a scientific collaboration relationship, in contrast to a customer-consultant setting, allowing the group to incorporate both the technical and applied perspectives into the work. With a clear structure, ethos and methodology, the SMC is able to step into the gap between academia and public health modelling to consider different impactful questions of operational importance where underlying complicated models exist, such as waiting times or system demand and capacity, and provide data analytic upskilling to public health teams.

FUNDING

Brighton and Hove City Council, East and West Sussex County Council, and Sussex Health and Care Partnership.

摘要

背景

苏塞克斯建模小组(SMC)是在新冠疫情期间成立的一个联盟,由英国国民医疗服务体系苏塞克斯分部、苏塞克斯地方当局和大学的代表组成。SMC旨在为公共卫生团队提供基于本地数据的建模、数据分析以及政策和委托建议,以减轻疫情对当地居民的影响。由于政府的预测并不完全适用,它还旨在回答实际操作问题。

方法

自2020年3月23日起,SMC每周四(通过线上方式)开会,监测新冠疫情情况报告,回答与数据和建模相关的问题,并对来自许多内部和外部来源的数据或报告进行解读。SMC还为公共卫生团队提供定量信息,供其在组织内部使用,以了解当地疫情情况。除其他工具外,SMC根据当地新冠疫情数据校准了一个数学模型,该模型可以预测医疗保健和医院需求以及与新冠疫情相关的死亡情况。

研究结果

在整个疫情期间,SMC提供了科学且基于数据的确凿证据,证明了尸体存储合同的必要性、对紧急护理的资金支持以及围绕医疗保健服务的运营调整。这些科学证据在每个组织中反复生成并被使用,以便在危机时期做出有益的决策。尽管追踪不断变化的疫情形势具有挑战性,但我们对国家政策和疫情变化的迅速反应使我们能够支持政策制定者、减轻焦虑并明确下一步行动。我们的合作在全国范围内为数不多,因此不仅应该受到赞扬,而且应该在有适当资源和资金的情况下进行推广。

解读

除了减轻苏塞克斯新冠疫情的直接影响外,我们建立了一种科学合作关系,与客户-顾问模式不同,这种关系使团队能够将技术和应用视角都融入到工作中。凭借清晰的结构、理念和方法,SMC能够填补学术界与公共卫生建模之间的空白,考虑存在复杂基础模型的具有不同操作重要性的有影响力的问题,如等待时间或系统需求与容量,并为公共卫生团队提供数据分析技能提升。

资金来源

布莱顿霍夫市议会、东苏塞克斯郡议会和西苏塞克斯郡议会以及苏塞克斯健康与护理伙伴关系。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验