Dynamic Random Network Model for Human Papilloma Virus Transmission

Abstract

Human Papilloma Virus (HPV) is a widespread sexually transmitted disease which can lead to cervical cancer. Understanding the factors influencing HPV transmission has been a challenge for scientists and policy makers. We have found that previous modeling studies have not sufficiently accounted for the structural and temporal features of the sexual networks underlying HPV transmission. The aim of this study is to investigate HPV transmission processes and vaccination strategies with a dynamic relationship-based transmission model. We calibrate the epidemic model with real-world network data, and study the transmission processes with different network parameters and transmission rates. We show that a pure vaccination strategy (vaccinating only one gender) is most efficient if female-to-male and male-to-female transmission rates are equal. However, there has been recent evidence in the literature indicating that female-to-male transmission rate might be higher. Incorporating these findings into our model lead to the conclusion that male vaccination is more effective. Finally, based on our simulation results, we provide some suggestions for optimal HPV vaccination strategies.