The impact of vaccination and coinfection on HPV and cervical cancer
Bibliographic record
Abstract
Understanding the relationship between coinfection with multiplestrains of human papillomavirus and cervical cancer may play a keyrole in vaccination strategies for the virus. In this article weformulate a model with two strains of infection and vaccination forone of the strains (strain 1, oncogenic) in order to investigate howmultiple strains of HPV and vaccination may affect the number ofcervical cancer cases and deaths due to infections with both typesof HPV. We calculate the basic reproductive number $R_i$ for bothstrains independently as well as the basic reproductive number forthe system based on $R_1$ and $R_2$. We also compute theinvasion reproductive number Ř i for straini when strain j is at endemic equilibrium ($i\nej$). We show that the disease-free equilibrium is locally stablewhen $R_0=max\{R_1,R_2\}1$. We determine stability ofthe single strain equilibria using the invasion reproductivenumbers. The $R_1,R_2$ parameter space is partitioned into 4 regionsby the curves $R_1=1, R_2=1,$ Ř 1 = 1, and Ř 2 = 1.In each region a different equilibrium is dominant. The presence ofstrain 2 can increase strain 1 related cancer deaths by more than100 percent, but strain 2 prevalence can be reduced by more than 90percent with 50 percent vaccination coverage. Under certainconditions, we show that vaccination against strain 1 can actuallyeradicate strain 2.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".