An age-structured mathematical model to assess the combined effects of vaccine and non-vaccine interventions on malaria transmission and burden

Abstract

Malaria remains a significant public health problem, particularly in sub-Saharan Africa, where its burden remains a concern despite existing control measures. Recent advances in vaccination, particularly with RTS, S and R21, offer new opportunities to mitigate malaria transmission and disease severity. In this study, we develop an age-structured mathematical model to assess the combined effects of vaccination and non-vaccine interventions on malaria transmission dynamics. The model integrates demographic parameters, long-lasting insecticide-treated nets (LLIN), access to treatment for clinical cases, vaccine efficacy profiles, and waning immunity to simulate disease burden in age cohorts over a long-term period. Our approach enables a comparative assessment of projected malaria transmission rates, disease burden, and disability-adjusted life years (DALYs) under different vaccination scenarios. For application purposes, the model is calibrated using national-level demographic data for a low transmission setting (Ethiopia) and a high transmission setting (Nigeria). Preliminary findings suggest that the integration of vaccination with existing malaria control strategies could significantly reduce malaria incidence and mortality over time. This study provides valuable insights into optimizing vaccination strategies and guiding public health policies aimed at long-term malaria control in endemic regions.