Computational Approaches of Modelling Human Papillomavirus Transmission and Prevention Strategies: A Systematic Review

TL;DR

This systematic review analyzes computational models for HPV transmission and prevention, highlighting current research, gaps, and future directions to aid in controlling HPV-related diseases globally.

Contribution

It provides a comprehensive summary of state-of-the-art computational epidemiology models for HPV, identifying research gaps and suggesting future directions.

Findings

Computational models effectively simulate HPV transmission dynamics.

Vaccination and screening strategies are evaluated through these models.

Identified gaps in current models for better HPV control.

Abstract

Human papillomavirus (HPV) infection is the most common sexually transmitted infection in the world. Persistent oncogenic Human papillomavirus infection has been a leading threat to global health and can lead to serious complications such as cervical cancer. Prevention interventions including vaccination and screening have been proved effective in reducing the risk of HPV-related diseases. In recent decades, computational epidemiology has been serving as a very useful tool to study HPV transmission dynamics and evaluation of prevention strategies. In this paper, we conduct a comprehensive literature review on state-of-the-art computational epidemic models for HPV disease dynamics, transmission dynamics, as well as prevention efforts. We summarise current research trends, identify gaps in the present literature, and identify future research directions with potential in accelerating the containment and/or elimination of HPV infection.