A protocol for a living mapping review of global research funding for infectious diseases with a pandemic potential - Pandemic PACT.

Q1 Medicine Wellcome Open Research Pub Date : 2024-10-02 eCollection Date: 2024-01-01 DOI:10.12688/wellcomeopenres.21202.2
Olena Seminog, Rodrigo Furst, Thomas Mendy, Omid Rohanian, Shanthi Levanita, Zaharat Kadri-Alabi, Nusrat Jabin, Georgina Humphreys, Emilia Antonio, Adrian Bucher, Alice Norton
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Abstract

The COVID CIRCLE initiative Research Project Tracker by UKCDR and GloPID-R and associated living mapping review (LMR) showed the importance of sharing and analysing data on research at the point of funding to improve coordination during a pandemic. This approach can also help with research preparedness for outbreaks and hence our new programme the Pandemic Preparedness: Analytical Capacity and Funding Tracking Programme (Pandemic PACT) has been established. The LMR described in this protocol builds on the previous UKCDR and GloPID-R COVID-19 Research Project database with addition of the priority diseases from the WHO Blueprint list plus initial additions of pandemic influenza, mpox and plague. We capture data on new funding commitments directly from funders and map these against a core ontology (aligned to existing research roadmaps). We will analyse regularly collated new research funding commitments to provide an open, accessible, near-real-time overview of the funding landscape for a wide range of infectious disease and pandemic preparedness research and assess gaps. The periodicity of updates will be increased in the event of a major outbreak. We anticipate that this LMR and the associated online tool will be a useful resource for funders, policy makers and researchers. In the future, our work will inform a more coordinated approach to research funding by providing evidence and data, including identification of gaps in funding allocation with a particular focus on low- and middle-income countries.

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对具有大流行潜力的传染病的全球研究资金进行活地图审查的协议--大流行病 PACT。
英国疾病与发展研究中心(UKCDR)和 GloPID-R 发起的 COVID CIRCLE 计划 "研究项目追踪"(Research Project Tracker)以及相关的 "活地图审查"(LMR)表明,共享和分析资助点的研究数据对于改善大流行病期间的协调工作非常重要。这种方法还有助于为疫情爆发做好研究准备,因此我们的新计划是 "大流行病准备":因此,我们设立了新计划 "大流行病防备:分析能力和资金跟踪计划"(Pandemic PACT)。本协议中描述的 LMR 建立在之前的 UKCDR 和 GloPID-R COVID-19 研究项目数据库的基础上,增加了世界卫生组织蓝图清单中的优先疾病,并初步增加了大流行性流感、麻疹和鼠疫。我们直接从资助者那里获取新的资助承诺数据,并将这些数据与核心本体(与现有研究路线图相一致)进行映射。我们将对定期整理的新研究资金承诺进行分析,为各种传染病和大流行准备研究提供一个开放、可访问、近乎实时的资金状况概览,并对差距进行评估。在发生重大疫情时,将增加更新周期。我们预计,该 LMR 和相关在线工具将成为资助者、决策者和研究人员的有用资源。今后,我们的工作将通过提供证据和数据,包括确定资金分配缺口,为更加协调的研究资金分配方法提供信息,并特别关注中低收入国家。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Wellcome Open Research
Wellcome Open Research Biochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)
CiteScore
5.50
自引率
0.00%
发文量
426
审稿时长
1 weeks
期刊介绍: Wellcome Open Research publishes scholarly articles reporting any basic scientific, translational and clinical research that has been funded (or co-funded) by Wellcome. Each publication must have at least one author who has been, or still is, a recipient of a Wellcome grant. Articles must be original (not duplications). All research, including clinical trials, systematic reviews, software tools, method articles, and many others, is welcome and will be published irrespective of the perceived level of interest or novelty; confirmatory and negative results, as well as null studies are all suitable. See the full list of article types here. All articles are published using a fully transparent, author-driven model: the authors are solely responsible for the content of their article. Invited peer review takes place openly after publication, and the authors play a crucial role in ensuring that the article is peer-reviewed by independent experts in a timely manner. Articles that pass peer review will be indexed in PubMed and elsewhere. Wellcome Open Research is an Open Research platform: all articles are published open access; the publishing and peer-review processes are fully transparent; and authors are asked to include detailed descriptions of methods and to provide full and easy access to source data underlying the results to improve reproducibility.
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