In this project, we will generate model-driven insights to accelerate and optimise how next-generation malaria vaccines and monoclonal antibodies (mAbs) are developed, evaluated, and deployed, across different age groups, use-cases, and transmission settings. We use OpenMalaria for the modelling, integrating clinical data from a range of candidates: the blood-stage vaccine RH5, the pre-erythrocytic R21, the transmission-blocking Pf230, and leading mAbs.
This project spans seven connected aims:
- Estimate the impact of existing vaccines. Using models calibrated to Phase 2 and 3 trial data, we will estimate how much current vaccine candidates (RH5, R21, Pf230) reduce malaria cases, severe disease, and deaths, alone and in combination, and how cost-effective they are across age groups, use-cases, and transmission settings.
- Define what better vaccines need to do. Building on Aim 1, we will work out the efficacy, durability, and coverage that next-generation vaccines would need to achieve in order to go beyond what existing vaccines can do, including what it would take to support elimination.
- Improve how vaccine trials are designed. We simulate clinical trial designs for multi-stage vaccines to help identify the endpoints, settings, and conditions that let a trial capture effects that are otherwise easy to miss, such as synergy between candidates, durability, and impact on severe disease.
- Weigh the trade-offs between vaccine characteristics. Vaccine developers face competing choices: efficacy versus durability, two doses versus three, one dosing schedule versus another. We will build a framework that simulates these alternatives under real-world constraints, to show which strategies deliver the most impact for the cost.
- Assess mAbs alongside vaccines. We will extend our modelling to leading mAb candidates (such as CIS43LS, L9LS, MAM01, and TB31F), testing how they perform on their own, in combination with RH5/CSP vaccines, and alongside existing chemoprevention campaigns.
- Respond to partners' questions as they arise. A dedicated, flexible strand of work lets us turn around rapid analysis for partners such as the World Health Organization (WHO), Gavi, the Vaccine Alliance, and Coefficient Giving, providing timely evidence as new data and questions emerge.
- Model in collaboration with other teams. We will formalise a collaboration between our OpenMalaria team and the Imperial College modelling group, under the Malaria Vaccine Alliance, so that independent modelling approaches can be compared directly, making the resulting impact estimates more robust and more useful for policy.
Together, these aims are designed to produce practical outputs: data-anchored estimates of the health impact of new vaccines and mAbs; clear targets for the characteristics that a vaccine needs to be worth developing; decision frameworks for scheduling and combining interventions; and concrete recommendations to help developers, funders, WHO, and the Malaria Vaccine Alliance prioritise the most promising strategies.
Grant information: Malaria Vaccine Modelling commencing January 2026.