Abstract
Cervical cancer is a common malignancy in women, with persistent human papillomavirus (HPV) infection as its primary cause. Understanding the progression from HPV infection to cervical cancer is crucial. Mathematical models play a key role in converting clinical trial data into long-term health forecasts, helping decision-makers tackle challenges posed by limited data and uncertain outcomes. This paper reviews transmission dynamics models and advancements in simulating HPV transmission leading to cervical cancer. It evaluates preventive and control measures, focusing on the impact of HPV vaccination across different vaccine types, doses, age groups, and both genders. These model-based assessments aim to provide insights for developing effective strategies to prevent and control HPV-related cervical cancer.