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Background In the United States, respiratory syncytial virus (RSV) is the leading cause of respiratory-related hospitalization in infants. The well-studied efficacy of the prophylactic monoclonal antibody, palivizumab, at preventing RSV disease in the highest risk infants provides proof of mechanism that serum neutralizing antibody protects against RSV. The expense and burden of monthly antibody injections limit the utility of palivizumab, leaving a large unmet medical need. Maternal immunization, with transplacental transfer of antibodies to the fetus, is an alternative, highly practical approach to protect many more infants. Methods Levels of protection by known palivizumab serum concentrations provide a basis for predicting maternal RSV vaccine efficacy in infants based on serum neutralizing antibody titers elicited in vaccine clinical trials, using statistical modeling to compensate for differences between palivizumab prophylaxis and maternal immunization. The model adjusts for the dependency of maternal vaccine responses on pre-immunization RSV neutralizing titers, exponential decay of maternal antibodies in infants, and exponentially decreasing airway resistance (reducing RSV disease risk) as infants grow. Results The rates of severe RSV disease by age projected from the model match the pattern of US infant hospitalization for RSV, with a peak at 1.5 months of age. The model relates vaccine-elicited increases in maternal RSV neutralizing titers to predicted reductions in severe RSV disease in infants from 0 to 6 months of age. Conclusion Statistical modeling of maternal RSV vaccine efficacy based on elicited RSV neutralizing titers provides a rational basis for decision-making during RSV vaccine development. Disclosures All authors: No reported disclosures.

2781. Statistical Modeling to Predict Maternal RSV Vaccine Efficacy from Neutralizing Titers