Strengthening the translation of malaria modelling into policy: Design, implementation, and early outcomes of the Regional Malaria Modelling Translational Fellowship

Malaria programmes increasingly rely on modelled evidence to support intervention prioritisation, resource allocation, and elimination planning, yet a persistent gap remains between technical modelling outputs and their use in decision-making. We describe the design, implementation, and early outcomes of the Regional Malaria Modelling Translational Fellowship, a six-month executive education programme delivered in 2025 to 21 fellows nominated by national malaria programmes in seven African countries. The Fellowship was designed to strengthen translational capacity by focusing on question formulation, systems thinking, model design and critique, interpretation of outputs, uncertainty, health economics, communication, and stakeholder engagement. The hybrid structure combined three intensive in-person blocks with regular virtual sessions. Country-teams work on capstone projects throughout the Fellowship, applying learnings to develop policy-relevant modelling proposals aligned with national malaria priorities. The programme was accredited as a University of Cape Town short course, which supported credibility, participant commitment, and institutional endorsement. Early evaluation showed improvements across all competency domains, with the largest gains in fellows' confidence in applying modelling to decision-making, translating model findings into recommendations, and communicating technical results to non-technical audiences. Qualitative feedback suggested that the Fellowship helped shift participants' engagement with modelling from passive acceptance of results toward critical interpretation, collaborative dialogue, and practical application. These findings suggest that translational, executive-style training can strengthen the interface between modelling and malaria policy. The publicly available curriculum offers a replicable framework that may be adapted for other infectious disease and public health settings where modelling evidence is increasingly central to decision-making.