Open Epidemiology for pandemic modelling: a transparent, traceable, reusable, open source pipeline for reproducible science

用于大流行病建模的开放流行病学：用于可重复科学的透明、可追踪、可重用、开源管道

• 批准号: ST/V006126/1
• 负责人: Richard Reeve
• 金额: $ 64.67万
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ST%2FV006126%2F1
• 关键词: Open; Epidemiology; pandemic; modelling; transparent

Historically, models used to support advice to government have not been publicly available, at least not readily, prior to publication. Technological advances and the growth of open source and reproducible science mean this is no longer tenable. Although current models feeding into UK policy are publicly available, they still lack the transparent and readily traceable chain of evidence connecting data and assumptions with model outputs that allows them to be readily independently assessed.Our Data Pipeline supports the implementation of COVID-19 epidemiological models that we, the Scottish COVID-19 Response Consortium (SCRC), have developed using volunteer resources within the RAMP initiative, to create new, complementary models. The Data Pipeline fulfils a critical role in our assessment of fitness for purpose for the models in providing policy advice, by managing and documenting a chain of trust that connects the primary data, analyses, and published and unpublished literature on COVID-19 to model outputs, documenting provenance of the conclusions being reached. The software interfaces we develop will be powerful, generic tools that will be useful to any policy-oriented modelling community.

从历史上看，用于支持向政府提供建议的模型在出版之前并不公开，至少不容易公开。技术的进步以及开源和可复制科学的发展意味着这不再站得住脚。尽管目前用于英国政策的模型是公开的，但它们仍然缺乏透明且易于追踪的证据链，这些证据链将数据和假设与模型输出连接起来，使其能够随时进行独立评估。我们的数据管道支持实施我们，苏格兰COVID-19应对联盟（SCRC），利用RAMP倡议内的志愿者资源开发的COVID-19流行病学模型，创造新的互补模式。数据管道通过管理和记录一个信任链，将COVID-19的主要数据、分析以及已发表和未发表的文献与模型输出联系起来，记录得出的结论的出处，在我们评估模型在提供政策建议方面的适用性方面发挥了关键作用。我们开发的软件接口将是功能强大的通用工具，对任何以政策为导向的建模社区都很有用。

项目成果

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.

流行病学建模的可视化：挑战，解决方案，反思和建议。

• DOI: 10.1098/rsta.2021.0299
• 发表时间: 2022-10-03
• 期刊: PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
• 影响因子: 5
• 作者: Dykes, Jason;Abdul-Rahman, Alfie;Archambault, Daniel;Bach, Benjamin;Borgo, Rita;Chen, Min;Enright, Jessica;Fang, Hui;Firat, Elif E.;Freeman, Euan;Gonen, Tuna;Harris, Claire;Jianu, Radu;John, Nigel W.;Khan, Saiful;Lahiff, Andrew;Laramee, Robert S.;Matthews, Louise;Mohr, Sibylle;Nguyen, Phong H.;Rahat, Alma A. M.;Reeve, Richard;Ritsos, Panagiotis D.;Roberts, Jonathan C.;Slingsby, Aidan;Swallow, Ben;Torsney-Weir, Thomas;Turkay, Cagatay;Turner, Robert;Vidal, Franck P.;Wang, Qiru;Wood, Jo;Xu, Kai
• 通讯作者: Xu, Kai

Complex model calibration through emulation, a worked example for a stochastic epidemic model.

• DOI: 10.1016/j.epidem.2022.100574
• 发表时间: 2022-06
• 期刊: EPIDEMICS
• 影响因子: 3.8
• 作者: Dunne, Michael;Mohammadi, Hossein;Challenor, Peter;Borgo, Rita;Porphyre, Thibaud;Vernon, Ian;Firat, Elif E.;Turkay, Cagatay;Torsney-Weir, Thomas;Goldstein, Michael;Reeve, Richard;Fang, Hui;Swallow, Ben
• 通讯作者: Swallow, Ben

Visualization for Epidemiological Modelling: Challenges, Solutions, Reflections & Recommendations

流行病学建模可视化：挑战、解决方案、反思

• DOI: 10.48550/arxiv.2204.06946
• 发表时间: 2022
• 作者: Dykes J

Collection of Observable Notebooks for RAMP VIS Reflection from Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations

RAMP VIS 可观察笔记本集合流行病学建模可视化的反思：挑战、解决方案、反思和建议

• DOI: 10.6084/m9.figshare.20236862
• 发表时间: 2022
• 作者: Dykes J

Trends in the diversity of mortality causes and age-standardised mortality rates among subpopulations within Scotland, 2001-2019.

• DOI: 10.1016/j.ssmph.2022.101192
• 发表时间: 2022-09
• 期刊: SSM-POPULATION HEALTH
• 影响因子: 4.7
• 作者: McMonagle, Ciaran;Brown, Denise;Reeve, Richard;Mancy, Rebecca
• 通讯作者: Mancy, Rebecca