Using Mathematical Modelling to Evaluate Human Papillomavirus Vaccination Programs in Canada
Bibliographic record
Abstract
Mathematical models provide unique insights to real-world problems. Within the context of infectious diseases, models are used to explore the dynamics of infections and control mechanisms. Human papillomavirus (HPV) globally infects about 630 million people, many of these infections develop into cancers and genital warts. Vaccines are available to protect against the most prevalent and devastating strains of HPV. The introduction of this vaccine as part of a national immunization program in Canada is a complex decision for policy-makers in which mathematical models can play a key role. We use the current recommendations provided by the World Health Organization to explore the integral role mathematical models have in the decision to incorporate the HPV vaccine within a national immunization program. We then provide a review of the literature discussing the role of mathematical models in the decision to include a vaccine in a national immunization program within the context of the HPV vaccine. Next, we evaluate the current standing of mathematical models used within the context of HPV immunization, to highlight the types of models used, underlying assumptions and general recommendations made about these immunization programs. Then, we create and analyze a model to explore the possibility of bettering the current HPV vaccine strategy in Canada. We focus on the effects of the grade of vaccination and the number of doses required to eradicate the targeted strains of HPV.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".