Strengthening local capacity for mathematical modelling in low- and middle-income countries: the process and lessons learnt in implementing the first cohort of Nigeria malaria modelling fellowships - Chijioke Kaduru; Uche Ibe; Shina Aladeshawe; Adaeze Eche-George; Ganiyat Eshikhena; Dumale Aadum; Bassey Okon; Emmanuel D. Iorkase; Kesiye Leghemo; Oladipo Ogunbode; Chukwu Okoronkwo; Onyebuchi Okoro; et al. (Malaria Journal)
Abstract Background 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. Methods 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. Results 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. Conclusion 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.