Vaccination Strategies against HPV Infection and Cervical Cancer in China: A Transmission Modeling Study
Bibliographic record
Abstract
Background Cervical cancer, driven predominantly by persistent high-risk human papillomavirus (HPV) infection, ranks as the fourth most common malignancy in women worldwide. China faces barriers to achieving the World Health Organization (WHO) 2030 elimination targets due to low vaccination rates and complex demographics. Strategic intervention optimization is critical for accelerating elimination. Methods We developed an age-stratified deterministic compartmental model integrating demographic data and HPV transmission dynamics, capturing heterogeneity in age, sex, sexual activity, and intervention efficacy. The model simulated cervical cancer natural history, including HPV infection, progression to precancerous lesions, and invasive cancer and was calibrated using epidemiological data from the Global Burden of Disease. We evaluated multiple vaccination scenarios (varying coverage rates, age groups, and durations) to project incidence trajectories, estimate elimination timelines, and calculate the reproduction number. Sensitivity analyses were conducted to assess parameter effects. Results Without vaccination, HPV infection becomes endemic (R 0 = 1.38), causing 2.92 million cervical cancer cases in China during 2021 to 2070. Maintaining the 2020 vaccination rate would prevent 1.01 million cases in this period. While prioritizing females aged 15 to 26 y maximizes the per-dose impact, expanding vaccination to all females aged ≥15 y is essential for achieving elimination before 2040. Even single-year vaccination would confer >50-y protection. A higher vaccination rate accelerates elimination: annual rates of 0.09, 0.15, and 0.21 among females aged ≥15 y achieve elimination by 2037, 2035, and 2034, respectively, accelerating timelines by 15 to 20 y compared with strategies targeting only 15- to 26-y-olds. Conclusions HPV vaccination is pivotal for reducing cervical cancer burden in China, with prioritizing women aged 15 to 26 y as the optimal strategy. Expanding vaccination to all women aged ≥15 y can accelerate the achievement of WHO elimination targets. Highlights An age-stratified model simulates HPV transmission patterns and assesses cervical cancer interventions. Without intervention, HPV remains endemic (R 0 = 1.38), causing 2.92 million cervical cancer cases in China (2021–2070). Prioritizing 15- to 26-y-olds maximizes the per-dose impact, but expanding to 15+ y cohorts is essential for elimination. Even a single year of vaccination offers >50 y of protection. Females ≥15 y vaccinated annually at rates of 0.09, 0.15, and 0.21 achieve elimination by 2037, 2035, and 2034, respectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".