Authors :
Felix Eli Wang; Umar M. A.; K. H. Oduwole
Volume/Issue :
Volume 10 - 2025, Issue 9 - September
Google Scholar :
https://tinyurl.com/yj4zpwmr
Scribd :
https://tinyurl.com/54z5j8ux
DOI :
https://doi.org/10.38124/ijisrt/25sep1088
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Abstract :
This study develops an age-structured mathematical model to investigate the dynamics of Human
Papillomavirus (HPV) and cervical cancer progression in the presence of vaccination and treatment. The model stratifies
the population into five epidemiological classes across discrete age groups to capture differences in disease transmission
and intervention outcomes. Analytical results establish conditions for the stability of the disease-free and endemic
equilibria depending on the basic reproduction number (R0
). Numerical simulations show that early vaccination of
adolescents significantly reduces HPV prevalence, while treatment improves outcomes in older populations. The combined
effect of vaccination and treatment proves most effective, leading to projected reductions of over 70% in cervical cancer
incidence within 25 years. The findings highlight the importance of integrated, age-targeted strategies for achieving
cervical cancer elimination goals.
Keywords :
HPV; Cervical Cancer; Age-Structured Model; Vaccination; Treatment; Mathematical Epidemiology.
References :
- A. Brotherton, “Assessing the impact of HPV vaccination programs,” Vaccine, vol. 37, no. 25, pp. 3361–3367, 2019.
- P. Apima and R. Mutwiwa, “Mathematical modeling of HPV dynamics with vaccination,” Journal of Applied Mathematics, pp. 1–12, 2023.
- Y. Kim and H. Kim, “Optimal vaccination strategies in an age-structured HPV model,” Mathematical Biosciences, vol. 351, pp. 108–120, 2024.
- S. Desta, F. Alemu, and H. Bekele, “Evaluating reduced-dose HPV vaccination regimens,” BMC Public Health, vol. 24, no. 1, pp. 45–55, 2024.
- S. Karam, “Optimal control analysis of cervical cancer treatment,” Nonlinear Analysis: Real World Applications, vol. 62, 103367, 2021.
- L. Gao, J. Xu, and W. Zhang, “Modeling socioeconomic disparities in cervical cancer treatment,” Maathematical Biosciences and Engineering, vol. 19, no. 8, pp. 8121–8143, 2022.
- J. Acedo, F. López-García, and A. Villanueva, “Modeling HPV vaccine pressure and viral evolution,” Infectious Disease Modelling, vol. 6, pp. 138–152, 2021.
- N. Sari, B. Putri, and A. Nugroho, “Age-structured SIPC model of HPV with vaccination and immunotherapy,” Journal of Mathematical Biology, vol. 88, pp. 1–23, 2024.
- H. Wang and Y. Chen, “Optimal control of HPV with vaccination and treatment,” Mathematical Biosciences, vol. 357, pp. 108–128, 2023.
- T. Akimenko and F. Adi-Kusumo, “Delayed SIPCV model for HPV incorporating treatment latency,” Applied Mathematics and Computation, vol. 439, 127569, 2023.
- F. Bray et al., “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: A Cancer Journal for Clinicians, vol. 68, no. 6, pp. 394–424, 2018.
- C. Martel, J. Ferlay, and F. Bray, “Global burden of cancers attributable to infections,” The Lancet Oncology, vol. 18, no. 3, pp. 303–313, 2017.
- H. Tanaka, M. Yoshida, and K. Ueda, “Impact of HPV vaccination in Japan,” Journal of Epidemiology, vol. 27, no. 4, pp. 145–152, 2017.
- J. Xi, Z. Yang, and Y. Wu, “Age-specific prevalence of carcinogenic HPV strains in rural China,” Cancer Epidemiology, vol. 64, 101683, 2020.
- T. Fekadu and H. Tilahun, “Deterministic model of HPV with age-specific interventions,” African Journal of Mathematical Sciences, vol. 5, no. 2, pp. 88–101, 2023.
- E. Meites, L. Szilagyi, and M. Markowitz, “Human papillomavirus vaccination: Recommendations of the Advisory Committee on Immunization Practices (ACIP),” MMWR Morbidity and Mortality Weekly Report, vol. 68, no. 32, pp. 698–702, 2019.
- L. Markowitz, H. Drolet, and M. Brisson, “Effectiveness of HPV vaccines in reducing cervical cancer,” Vaccine, vol. 39, no. 42, pp. 6144–6153, 2021.
- N. Schiller, J. Lowy, and D. Markowitz, “HPV vaccination and prospects for eradication,” Nature Reviews Microbiology, vol. 18, no. 4, pp. 240–248, 2020.
- M. Brisson and J. Kim, “Projecting global elimination of cervical cancer,” The Lancet Oncology, vol. 22, no. 1, pp. 64–72, 2021.
- Z. Feng and J. Glasser, “Herd immunity thresholds for HPV under vaccination,” Mathematical Biosciences and Engineering, vol. 19, no. 6, pp. 6210–6230, 2022.
- K. Jelen, P. Kowalski, and A. Nowak, “Genomic variation in HPV type 11,” Virus Research, vol. 250, pp. 34–42, 2018.
- P. van den Driessche and J. Watmough, “Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission,” Mathematical Biosciences, vol. 180, no. 1–2, pp. 29–48, 2002.
This study develops an age-structured mathematical model to investigate the dynamics of Human
Papillomavirus (HPV) and cervical cancer progression in the presence of vaccination and treatment. The model stratifies
the population into five epidemiological classes across discrete age groups to capture differences in disease transmission
and intervention outcomes. Analytical results establish conditions for the stability of the disease-free and endemic
equilibria depending on the basic reproduction number (R0
). Numerical simulations show that early vaccination of
adolescents significantly reduces HPV prevalence, while treatment improves outcomes in older populations. The combined
effect of vaccination and treatment proves most effective, leading to projected reductions of over 70% in cervical cancer
incidence within 25 years. The findings highlight the importance of integrated, age-targeted strategies for achieving
cervical cancer elimination goals.
Keywords :
HPV; Cervical Cancer; Age-Structured Model; Vaccination; Treatment; Mathematical Epidemiology.