Kaduru Chijioke, Ibe Uche, Aladeshawe Shina, Eche-George Adaeze, Eshikhena Ganiyat, Aadum Dumale, Okon Bassey, Iorkase Emmanuel D, Leghemo Kesiye, Ogunbode Oladipo, Okoronkwo Chukwu, Okoro Onyebuchi, Igumbor Ehimario U, Oyeyemi Abisoye, Uhomoibhi Perpetua, Babatunde Seye
Corona Management Systems, Plot 2014, CAD Zone B09, Celina Ayom Crescent, Kado-Abuja, Nigeria.
Bill and Melinda Gates Foundation, Abuja, Nigeria.
Malar J. 2025 Apr 10;24(1):116. doi: 10.1186/s12936-025-05345-2.
Mathematical modelling plays a crucial role in understanding malaria epidemiology and evaluating anti-malarial interventions. In sub-Saharan Africa, National Malaria Control Programs are increasingly collaborating with modellers to optimize impact within constrained fiscal environments and evaluate the effectiveness of ongoing malaria control efforts. Despite Nigeria's National Malaria Elimination Program soliciting modelling expertise, there remains a significant capacity gap in low- and middle-income countries (LMICs), including Nigeria. To address this, the Nigerian Malaria Modelling Fellowship (MMF) adopts a one-health approach within the Nigerian Field Epidemiology and Laboratory Training Program.
The MMF aims to enhance mathematical modelling capacity among Nigerian public health professionals by increasing the number of doctoral and postdoctoral graduates proficient in using modelling for planning, program evaluation, and outcome assessment. This paper highlights the initiative's innovative aspects and shares initial implementation insights.
Implemented using a human-centred design, MMF is a collaborative effort involving multiple public health stakeholders. The curriculum spans four courses-Malaria, Mathematical Modelling, Evidence Translation, and Project Management-each with targeted modules. The first cohort recruitment attracted 2173 applications, rigorously screened through a five-step process, selecting 33 Fellows from all geopolitical zones of Nigeria. The cohort applies a one-health lens and includes 48% female representation. Key findings highlight the importance of government leadership, gender mainstreaming, stakeholder co-creation, leveraging existing investments, adopting best practices, and expanding engagement to meet national needs.
MMF demonstrates a collaborative effort to build modelling capacity among epidemiologists and healthcare professionals in LMICs, particularly for malaria. The rigorous recruitment process underscores a strong interest in mathematical modelling. The human-centred approach has fostered government leadership, multi-stakeholder engagement, and national ownership. This paper recommends increased commitments to local capacity strengthening in LMICs and advocates for evaluating the project, including assessing Fellows' competencies post-training to ensure effective capacity development.
数学建模在理解疟疾流行病学和评估抗疟干预措施方面发挥着关键作用。在撒哈拉以南非洲，国家疟疾控制项目越来越多地与建模人员合作，以在财政受限的环境中优化影响，并评估正在进行的疟疾控制工作的有效性。尽管尼日利亚的国家疟疾消除项目寻求建模专业知识，但包括尼日利亚在内的低收入和中等收入国家（LMICs）仍存在显著的能力差距。为解决这一问题，尼日利亚疟疾建模奖学金（MMF）在尼日利亚现场流行病学和实验室培训项目中采用了“同一健康”方法。
MMF旨在通过增加精通使用建模进行规划、项目评估和结果评估的博士和博士后毕业生数量，提高尼日利亚公共卫生专业人员的数学建模能力。本文重点介绍了该倡议的创新方面，并分享了初步实施见解。
MMF采用以人为本的设计实施，是一项涉及多个公共卫生利益相关者的合作努力。课程涵盖四门课程——疟疾、数学建模、证据转化和项目管理——每门课程都有针对性的模块。第一批学员招募吸引了2173份申请，通过五步流程进行严格筛选，从尼日利亚所有地缘政治区挑选出33名学员。该学员群体采用“同一健康”视角，女性占比48%。主要发现突出了政府领导、性别主流化、利益相关者共同创造、利用现有投资、采用最佳实践以及扩大参与以满足国家需求的重要性。
MMF展示了在低收入和中等收入国家的流行病学家和医疗保健专业人员中建立建模能力的合作努力，特别是针对疟疾。严格的招募过程凸显了对数学建模的浓厚兴趣。以人为本的方法促进了政府领导、多利益相关者参与和国家自主权。本文建议增加对低收入和中等收入国家当地能力建设的投入，并主张对该项目进行评估，包括评估学员培训后的能力，以确保有效的能力发展。
