Evaluation of a vaccination strategy by serosurveillance data: The case of varicella
Bibliographic record
Abstract
Serological studies have many important epidemiologic applications. They can be used to investigate acquisition of various infections in different populations, measure the induction of an immune response in the host, evaluate the persistence of antibody, identify appropriate target groups and the age for vaccination. Serological studies can also be used to determine the vaccine efficacy. Since 1995 a varicella vaccine is available and it has been recommended in several countries (e.g. USA, Australia, Canada, Costa Rica, Ecuador, etc.). Nevertheless few varicella seroprevalence studies in countries that adopted an URV are available. It is related to the relatively recent introduction of the vaccination and to the lack of structured and collaborative surveillance systems based on serosurvey at national or regional level. Varicella seroprevalence data collected before the introduction of vaccination strategies allowed to establish the age of vaccination (e.g., indicated the opportunity to offer the vaccine to Italian susceptible adolescents). In the post-vaccination era, seroprevalence data demonstrated vaccine as immunogenic and excluded an increase of the age of infection linked to the vaccination strategy. New seroprevalence studies should be performed to answer to open questions, such as the long-term immunity and the change of the herpes zoster epidemiological pattern related to the vaccine.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".