A Statistical Model to Predict Protection Against Infant Respiratory Syncytial Virus Disease Through Maternal Immunization.

Background/objectives: Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants worldwide. Maternal immunization to protect younger infants is supported by evidence that virus-neutralizing antibodies, which are efficiently transferred across the placenta from mother to fetus, are a primary immune mediator of protection. In maternal RSV vaccine studies, estimates of correlates of protection are elusive because many factors of maternal-fetal immunobiology and disease characteristics must be considered for the estimates.

Methods: We developed statistical models that aims to predict vaccine efficacy (VE) in infants following maternal immunization by including quantifiable covariates of the antibody titer distribution of the mother (pre- and post-immunization), the transplacental transfer ratio of IgG antibodies, the rate of antibody decay, and RSV disease incidence rate, all of which are season- and time-dependent and vary by infant age.

Result: Our model shows that integrating the lower respiratory tract disease risk based on infant airway diameter and associated airway resistance is critical to appropriately model predicted infant VE. The VE predictions by our models, which preceded maternal RSV prefusion F vaccine efficacy trial primary readouts, closely align with the VE outcomes of these field studies.

Conclusion: Our models successfully predicted VE of the RSV maternal vaccines and have potential use in modeling the clinical trial out-comes of other respiratory disease vaccines where maternal antibodies play a role in the protection of newborns.