Projections of the impact of transmission-reducing pediatric influenza vaccine in South Africa using high resolution immunologic and infection data

BackgroundTraditional influenza vaccination programs focus on preventing severe illness through the vaccines direct effect. However, vaccinating children may substantially reduce overall burden by lowering transmission, especially in high infection settings and when effective transmission reducing vaccines become available. MethodsTo evaluate the impact of a transmission-reducing vaccination campaign, we developed a Susceptible-Latent-Infectious-Recovered (SLIR) compartmental transmission model calibrated with high-resolution virological and immunological data sourced from a multiyear household cohort study in urban and rural South Africa. The mode explicitly accounted for variations in susceptibility, infectiousness, and viral shedding duration by age group, pre-season hemagglutination inhibition antibody titer and influenza subtype/lineage. Using this framework, we simulated pediatric vaccination scenarios targeting children aged 6 months - 5 years or 6-12 years, varying both vaccine coverage and the vaccines effectiveness in reducing susceptibility and transmission. Scenarios were compared to no-vaccination counterfactuals to project reductions in infections, illness episodes, hospitalizations, and deaths in South Africa. ResultsOur model projects that vaccinating 50% of children aged 6 months - 5 years or 6-12 years with a vaccine that is 50% effective in reducing both susceptibility and viral shedding could lower the overall infection incidence by 58-60% and 48%-51%, respectively. Illness episodes would be reduced by 20-60%, hospitalizations by 26-64%, and deaths by 21-59% across all age groups. The target age group among children that resulted in the highest population-wide vaccination benefits varied with the circulating lineage, season, and urbanization level. ConclusionsOur work demonstrates that pediatric immunization with transmission-reducing vaccines holds significant potential for lowering community influenza burden, particularly in high transmission settings. Our modeling framework offers a practical translational tool for anticipating the impact of new vaccines in low and middle-income settings.