Potential Impact of Changes in the Schedule for Primary Diphtheria-Tetanus Toxoids-Pertussis Immunization as Control Strategy for Pertussis
Bibliographic record
Abstract
BACKGROUND: Pertussis is a vaccine-preventable respiratory disease that may cause death mainly in infants. The schedules for primary pertussis vaccination are set in each country by the local health authorities. Several different schedules meet World Health Organization recommendations, 2-4-6 months, 6-10-14 weeks, 2-3-4 months and 3-4-5 months being the most commonly used worldwide. In this work, we analyze the benefits of changing the vaccination schedule to control the disease. METHODS: We used an age-structured deterministic mathematical model for pertussis transmission to compute the incidences for the 4 above-mentioned schedules. Different vaccination coverages and vaccine effectiveness levels were considered. Immunization data from Argentina and Belgium were used. RESULTS: The highest reduction in incidence was obtained by adopting the 6-10-14 weeks schedule, reaching about a 36% reduction of 0-1-year incidence with respect to the 2-4-6 months schedule. We show the dependence of this reduction on both vaccine effectiveness and coverage. The severe pertussis incidence decreased significantly when the first dose of the 2-4-6 months schedule was accelerated to 6 weeks. Finally, we estimated that the communication campaign adopted in Flanders (Belgium) to improve compliance with the vaccine schedule could lead to a reduction of 16% in severe pertussis incidence and about 7% in total incidence in infants. CONCLUSIONS: Our work highlights the use of mathematical modeling to quantify the benefits of the existing vaccination schedules and the strategies that could be implemented to improve their compliance. Our results indicated that the 6-10-14 weeks is the best schedule option and that the Belgium vaccination campaign significantly reduced the incidence of severe cases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".