A Comparative Economic Analysis of Immunization Programs for Pertussis and Measles: The Use of ARIMA Model to Study the Epidemiological Situation in England and Wales
Bibliographic record
Abstract
Objective: We evaluate pertussis and measles immunization strategies and compare the consequences in terms of health effects and economic costs. Methods: Based on epidemiological data for pertussis and measles in England and Wales from 1970 to 2012, we use ARIMA approach to model the relation between notification cases and vaccination coverage for each disease. We then perform an economic evaluation of vaccination programs at 95% and discuss the benefits for the society to achieve this level when compared with lower vaccination rates currently observed. The advantages for the society of increasing vaccination coverage up to 98% are considered respectively for pertussis and measles. Results: The programs at a 95% vaccination rate, which is able to significantly reduce the mortality and the morbidity due to pertussis and measles, are confirmed to be the best cost saving immunization strategy. The total social net benefits are systematically maximized when the programs are compared to strategies with lower vaccination coverage. The decision to exceed the herd immunity level and reach the rate at 98% is economically justified for measles, while for pertussis the programs at 98% are less cost effective than the programs at 95%. Conclusion: Additional efforts must be carried out to promote measles vaccination since immunization strategies at 95% and at higher rates are recommended on epidemiological and economic grounds.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".