title: Expanding vaccine efficacy estimation with dynamic models fitted to cross-sectional prevalence data post-licensure.
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.
authors.affiliation: Instituto Gulbenkian de Ciência, Apartado 14, 2781-901 Oeiras, Portugal. Electronic address: egjini@igc.gulbenkian.pt.
authors.forename: Erida
authors.initials: E
authors.lastname: Gjini
authors.affiliation: CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade de Porto, Portugal; Instituto de Matemática e Estatística, Universidade de São Paulo, Brazil; Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
authors.forename: M Gabriela M
authors.initials: MGM
authors.lastname: Gomes
mesh_terms: D003430:Cross-Sectional Studies; D004196:Disease Outbreaks / Q000517:prevention & control* / Q000706:statistics & numerical data*; D006801:Humans; D008962:Models, Theoretical; D015995:Prevalence; D012044:Regression Analysis; D016896:Treatment Outcome; D014611:Vaccination / Q000706:statistics & numerical data*; D014612:Vaccines / Q000276:immunology*
chemical_list: D014612:Vaccines
keywords: Co-infection; Competition; ODE parameter inference; Strain replacement; Vaccination model