Different Challenges in Eliminating HPV16 Compared to Other Types: A Modeling Study
Bibliographic record
Abstract
Background: Human papillomavirus (HPV) vaccination is still not reaching many high-risk populations. HPV16/18 vaccines offer cross-protection against other types, for example, HPV45. Both direct vaccine efficacy and indirect herd protection contribute to vaccination effectiveness. Methods: We used a dynamic transmission model, calibrated to cervical screening data from Italy, to estimate vaccination effectiveness against HPV16 and HPV45 infection, assuming for HPV45 either 95% or lower cross-protection. Results: Basic reproductive number was smaller (2.1 vs 4.0) and hence vaccine effectiveness and herd protection stronger for HPV45 than for HPV16. The largest difference in the reduction of infection prevalence in women <35 years old was found at 70% coverage in girls-only vaccination programs (99% vs 83% for total protection for HPV45 and HPV16, respectively, mainly owing to stronger herd protection, ie, 37% vs 16%). In gender-neutral vaccination, the largest difference was at 40% coverage (herd protection, 54% vs 28% for HPV16 and HPV45, respectively). With ≥80% coverage, even 50% cross-protection would reduce HPV45 by ≥94%. Conclusions: The characteristics of individual high-risk HPV types strongly influence herd protection and determine the level of coverage and cross-protection required to reduce or eliminate the infection through HPV vaccination. HPV16 infection and related cancers are the most difficult to eliminate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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 source (direct Gemma or distilled Codex), 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".