Human papillomavirus (HPV) is a widespread virus and the leading cause of cervical cancer in women, a serious disease that affects millions globally each year. The HPV vaccination is currently the most effective method for preventing this infection and reducing the incidence of cancer. However, vaccination is often postponed for pregnant women as a medical precaution, creating an extended period during which they remain vulnerable to the virus. This situation raises an important question: What is the impact of delaying vaccination on the spread of HPV within the population and on overall efforts to eliminate the disease?
To address this question, this study developed an innovative dynamic modeling approach that effectively represents the complex interactions among individuals in the population: susceptible or infected individuals, pregnant women in various states, and vaccinated women. This model enables the simulation of the virus’s evolution over time and allows for an analysis of how the delay in vaccination affects the spread of infection.
The project involved creating a comprehensive system that accurately represents the dynamics of HPV transmission. We established specific compartments for different types of individuals and considered delays in vaccination related to pregnancy. To ensure the model’s accuracy and relevance, we selected biological and epidemiological parameters such as the probability of infection, disease progression, and vaccination rates from reliable scientific sources.
A crucial aspect of the study was identifying the conditions under which the virus spreads or is eliminated. We utilized a key metric known as the basic reproduction number, which indicates the virus’s potential for spreading within the population. Our analysis revealed that when this number is less than 1, the disease is likely to diminish over time, even with vaccination delays. Conversely, when the reproduction number exceeds 1, delaying vaccination facilitates the virus’s spread, increasing the number of infected women and a heightened risk of long-term transmission.
To gain a better understanding of these dynamics, we conducted detailed numerical simulations. These simulations demonstrate the effects of vaccination delays across all population categories, allowing us to visualize how the virus spreads and how vaccination despite delays, can mitigate its impact. We created phase portraits to graphically represent the evolution of the virus under various initial conditions, providing a clear and intuitive illustration of the epidemic dynamics.
This thesis significantly contributes to the understanding of HPV and the effects of delayed vaccination. It serves as a valuable predictive tool for public health decision-makers, enabling them to optimize vaccination strategies and protect women, especially pregnant women. Additionally, it highlights critical moments when rapid intervention can prevent substantial virus spread.
This study is more than just a theoretical exercise; it combines scientific data, mathematical modeling, and simulations to provide concrete recommendations. It emphasizes the significance of effective vaccination strategies, contributes to the prevention of cervical cancer, and suggests ways to enhance public health both nationally and internationally.
