Modeling the Dynamics of Human Papillomavirus to Inform Vaccination Strategy

点击翻译按钮获取中文摘要

Abstract Despite the public health burden posed by human papillomavirus (HPV) - associated cancers, it is unclear how social and behavioral traits, different kinds of host immunity, and competition between types shape the epidemiology of HPV. The individual-level risk factors that sustain HPV, which may differ between HPV types, define high-risk subpopulations that are key to behavioral public health interventions. Biological interactions between HPV types, which may be synergistic, neutral, or competitive, determine the effect of HPV vaccines. The eradication of vaccine-targeted types could lead to “type replacement” by competing non-targeted types, a phenomenon that has been observed in pneumococcus. The proposed project investigates these aspects of HPV ecology and epidemiology by fitting mechanistic mathematical models to longitudinal data. The data span 37 different HPV types in over 4,000 men sampled across nine years at six-month intervals. The data contain information about patient demographics and sexual practices that affect viral dynamics. By fitting a mechanistic transmission model, we will measure how different behavioral and health traits affect susceptibility to infection and identify core risk groups that may sustain transmission in the population. Furthermore, we will infer the degree and timescale of HPV interactions. We will measure the impact of previous infections on HPV dynamics to infer the extent and duration of immunity in natural infection. We will also measure the impact of coinfections to infer how types interact directly. Our approach, based on Markov Chain Monte Carlo methods for Partially Observed Markov Processes, implicitly incorporates the stochastic, nonlinear dynamics of infection. The proposed research will, for each HPV type, measure its transmissibility in different subpopulations, interactions with other types, and interactions with host immunity. This information will be used in contact network simulations to predict the consequences of current and hypothetical eradication efforts and to optimize intervention strategies.