Measles – The epidemiology of elimination
Bibliographic record
Abstract
Tremendous progress has been made globally to reduce the contribution of measles to the burden of childhood deaths and measles cases have dramatically decreased with increased two dose measles-containing vaccine coverage. As a result the Global Vaccine Action Plan, endorsed by the World Health Assembly, has targeted measles elimination in at least five of the six World Health Organisation Regions by 2020. This is an ambitious goal, since measles control requires the highest immunisation coverage of any vaccine preventable disease, which means that the health system must be able to reach every community. Further, while measles remains endemic in any country, importations will result in local transmission and outbreaks in countries and Regions that have interrupted local endemic measles circulation. One of the lines of evidence that countries and Regions must address to confirm measles elimination is a detailed description of measles epidemiology over an extended period. This information is incredibly valuable as predictable epidemiological patterns emerge as measles elimination is approached and achieved. These critical features, including the source, size and duration of outbreaks, the seasonality and age-distribution of cases, genotyping pointers and effective reproduction rate estimates, are discussed with illustrative examples from the Region of the Americas, which eliminated measles in 2002, and the Western Pacific Region, which has established a Regional Verification Commission to review progress towards elimination in all member countries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".