Data-Driven age structured model for optimal control and cost-effective HPV vaccine uptake for cervical cancer reduction in Tanzania

Human papillomavirus (HPV) is the leading sexually transmitted infection worldwide and the primary cause of cervical cancer, a major cause of mortality among women in sub-Saharan Africa. In Tanzania, where HPV poses a significant health burden, effective vaccination strategies are critical. We develop an age-structured PDE model capturing HPV transmission in both sexes and cervical cancer progression in females. The model, consisting of ten coupled PDEs, was calibrated using Markov Chain Monte Carlo (MCMC) methods with Tanzanian HPV incidence data (2018–2023), confirming reliable parameter estimates. Applying the Lotka Sharpe McKendrick integral approach, we derived the effective reproduction number R ( ω ) dependent on age-specific vaccination rate. We proved local and global stability of the disease-free equilibrium when R ( ω ) < 1 , supported by numerical simulations showing infection decline under this condition. We formulated an optimization framework balancing epidemiological impact and vaccination costs, identifying age groups that maximize cost-effectiveness. Simulations revealed that extending vaccination beyond ages 9–14, to include 14–29 year old, substantially reduces infections and cervical cancer incidence, with a critical age threshold a ∗ = 26 indicating that a single cohort strategy is optimal. Increased vaccination intensity notably benefits women aged 20–30, where infection peaks. Finally, we strongly recommend sustaining adolescent vaccination programs and implementing targeted catch-up campaigns up to age 29. Expanding and maintaining HPV vaccination programs is an effective, feasible, and cost-efficient strategy to protect girls and young women from cervical cancer, the leading HPV-related disease in Tanzania and to accelerate progress toward the WHO cervical cancer elimination goals.