Expanding vaccine efficacy estimation with dynamic models fitted to cross-sectional prevalence data post-licensure

Gjini, Erida and Gomes, M. Gabriela M. (2016) Expanding vaccine efficacy estimation with dynamic models fitted to cross-sectional prevalence data post-licensure. Epidemics, 14. pp. 71-82. ISSN 1755-4365

[img]
Preview
Text (Gjini-Gomes-E2016-Expanding-vaccine-efficacy-estimation-dynamic-models-fitted-cross-sectional-prevalence-data-post-licensure)
Gjini_Gomes_E2016_Expanding_vaccine_efficacy_estimation_dynamic_models_fitted_cross_sectional_prevalence_data_post_licensure.pdf
Final Published Version
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo

Download (1MB)| Preview

    Abstract

    The efficacy of vaccines is typically estimated prior to implementation, on the basis of randomized controlled trials. This does not preclude, however, subsequent assessment post-licensure, while mass-immunization and nonlinear transmission feedbacks are in place. In this paper we show how cross-sectional prevalence data post-vaccination can be interpreted in terms of pathogen transmission processes and vaccine parameters, using a dynamic epidemiological model. We advocate the use of such frameworks for model-based vaccine evaluation in the field, fitting trajectories of cross-sectional prevalence of pathogen strains before and after intervention. Using SI and SIS models, we illustrate how prevalence ratios in vaccinated and non-vaccinated hosts depend on true vaccine efficacy, the absolute and relative strength of competition between target and non-target strains, the time post follow-up, and transmission intensity. We argue that a mechanistic approach should be added to vaccine efficacy estimation against multi-type pathogens, because it naturally accounts for inter-strain competition and indirect effects, leading to a robust measure of individual protection per contact. Our study calls for systematic attention to epidemiological feedbacks when interpreting population level impact. At a broader level, our parameter estimation procedure provides a promising proof of principle for a generalizable framework to infer vaccine efficacy post-licensure.