Modelling the impact of respiratory syncytial virus (RSV) vaccine and immunoprophylaxis strategies in New Zealand

Abstract

Background: Mathematical models of respiratory syncytial virus (RSV) transmission can help describe seasonal epidemics and assess the impact of potential vaccines and immunoprophylaxis with monoclonal antibodies (mAb).

Methods: We developed a deterministic, compartmental model for RSV transmission, which was fitted to population-based RSV hospital surveillance data from Auckland, New Zealand. The model simulated the introduction of either a maternal vaccine or a seasonal mAb among infants aged less than 6 months and estimated the reduction in RSV hospitalizations for a range of effectiveness and coverage values.

Results: The model accurately reproduced the annual seasonality of RSV epidemics in Auckland. We found that a maternal vaccine with effectiveness of 30-40% in the first 90 days and 15-20% for the next 90 days could reduce RSV hospitalizations by 18-24% in children younger than 3 months, by 11-14% in children aged 3-5 months, and by 2-3% in children aged 6-23 months. A seasonal infant mAb with 40-60% effectiveness for 150 days could reduce RSV hospitalizations by 30-43%, 34-48% and by 14-21% in children aged 0-2 months, 3-5 months and 6-23 months, respectively.

Conclusions: Our results suggest that either a maternal RSV vaccine or mAb would effectively reduce RSV hospitalization disease burden in New Zealand. Overall, a seasonal mAb resulted in a larger disease prevention impact than a maternal vaccine.

Keywords: Immunoprophylaxis; Maternal vaccine; Mathematical modelling; RSV; Respiratory syncytial virus.