Exploring individual HPV coinfections is essential to predict HPV-vaccination impact on genotype distribution: A model-based approach

Abstract

IntroductionAs for other vaccines that only target a subset of circulating pathogen types, human papillomavirus (HPV) immunization raises the concern of a potential risk of genotype replacement. Potential interactions between HPV types may affect infection acquisition and clearance. However, the existence and the nature of these interactions are still largely unknown. Here, we assess how such interactions might affect the impact of HPV vaccination on genotype distribution in the long term. MethodsWe develop two mathematical models of the transmission of oncogenic HPV infections that include interactions between vaccine and nonvaccine genotypes to examine the influence of different coinfection dynamics (simultaneous vs. sequential clearance of coinfections) on the evolution of nonvaccine prevalences postimmunization. ResultsAfter introducing vaccination, the two models give contrasting genotype-replacement outcomes. When hypothesizing that coinfections clear sequentially, genotype replacement depends on whether vaccine and nonvaccine genotypes reduce or favor the acquisition by one or the other. Interestingly, the hypothesis that coinfections clear simultaneously always leads to genotype replacement, even when infections with vaccine types favor the acquisition of infections with nonvaccine types. ConclusionOur results suggest that predictions regarding HPV genotype replacement strongly depend on the assumptions describing the dynamics (acquisition and clearance) of coinfections. In particular, HPV genotype replacement could be compatible with synergistic interactions between types affecting infections acquisition, contrary to previous suggestions. Understanding better how concurrent infections with multiple types change the acquisition and time to clearance of type-specific infections is essential to be able to predict the impact of vaccination on genotype distribution. Longitudinal data collection in populations, particularly examining infection and coinfection acquisition and clearance, is needed to better predict HPV-vaccine impact.