Oliwa Jacquie, Guleid Fatuma Hassan, Owek Collins J, Maluni Justinah, Jepkosgei Juliet, Nzinga Jacinta, Were Vincent O, Sim So Yoon, Walekhwa Abel W, Clapham Hannah, Dabak Saudamini, Kc Sarin, Hadley Liza, Undurraga Eduardo, Hagedorn Brittany L, Hutubessy Raymond Cw
Health Services Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya
Public Health, Institute of Tropical Medicine, Antwerp, Flanders, Belgium.
BMJ Open. 2025 Apr 5;15(4):e093645. doi: 10.1136/bmjopen-2024-093645.
The COVID-19 pandemic highlighted the significance of mathematical modelling in decision-making and the limited capacity in many low-income and middle-income countries (LMICs). Thus, we studied how modelling supported policy decision-making processes in LMICs during the pandemic (details in a separate paper).We found that strong researcher-policymaker relationships and co-creation facilitated knowledge translation, while scepticism, political pressures and demand for quick outputs were barriers. We also noted that routine use of modelled evidence for decision-making requires sustained funding, capacity building for policy-facing modelling, robust data infrastructure and dedicated knowledge translation mechanisms.These lessons helped us co-create a framework and policy roadmap for improving the routine use of modelling evidence in public health decision-making. This communication paper describes the framework components and provides an implementation approach and evidence for the recommendations. The components include (1) funding, (2) capacity building, (3) data infrastructure, (4) knowledge translation platforms and (5) a culture of evidence use.
Our framework integrates the supply (modellers) and demand (policymakers) sides and contextual factors that enable change. It is designed to be generic and disease-agnostic for any policy decision-making that modelling could support. It is not a decision-making tool but a guiding framework to help build capacity for evidence-based policy decision-making. The target audience is modellers and policymakers, but it could include other partners and implementers in public health decision-making.
The framework was created through engagements with policymakers and researchers and reflects their real-life experiences during the COVID-19 pandemic. Its purpose is to guide stakeholders, especially in lower-resourced settings, in building modelling capacity, prioritising efforts and creating an enabling environment for using models as part of the evidence base to inform public health decision-making. To validate its robustness and impact, further work is needed to implement and evaluate this framework in diverse settings.
新冠疫情凸显了数学建模在决策中的重要性，以及许多低收入和中等收入国家（LMICs）建模能力的有限性。因此，我们研究了建模在疫情期间如何支持低收入和中等收入国家的政策决策过程（详情见另一篇论文）。我们发现，强大的研究人员与政策制定者关系以及共同创造促进了知识转化，而怀疑态度、政治压力和对快速产出的需求则是障碍。我们还指出，将建模证据用于决策的常规做法需要持续的资金、面向政策的建模能力建设、强大的数据基础设施以及专门的知识转化机制。这些经验教训帮助我们共同创建了一个框架和政策路线图，以改善建模证据在公共卫生决策中的常规应用。本交流论文描述了框架组成部分，并为各项建议提供了实施方法和证据。这些组成部分包括：（1）资金；（2）能力建设；（3）数据基础设施；（4）知识转化平台；（5）证据使用文化。
我们的框架整合了供应方（建模人员）和需求方（政策制定者）以及促成变革的背景因素。它旨在具有通用性且不针对特定疾病，适用于建模可支持的任何政策决策。它不是一个决策工具，而是一个指导框架，有助于建设基于证据的政策决策能力。目标受众是建模人员和政策制定者，但可能还包括公共卫生决策中的其他合作伙伴和实施者。
该框架是通过与政策制定者和研究人员的合作创建的，反映了他们在新冠疫情期间的实际经验。其目的是指导利益相关者，特别是在资源较少的环境中，建设建模能力、确定工作重点并营造一个有利环境，以便将模型作为证据基础的一部分用于为公共卫生决策提供信息。为验证其稳健性和影响力，需要进一步开展工作，在不同环境中实施和评估该框架。
