A Narrative Review of the Measles Outbreak in North America and Globally
Bibliographic record
Abstract
In the early twenty-first century, measles was completely eradicated in the United States of America (USA) and almost eliminated in Canada. This was greatly due to most of the population being vaccinated against the virus. In 2018 and 2019, the USA and Canada experienced a rapidly developing measles virus outbreak due to growing debates about vaccine efficacy and side effects. Therefore, some people refused to vaccinate their children against measles, as well as many other life-threatening preventable diseases. This led to a major measles outbreak and health concern in the USA, Canada, and globally. Some countries including the Democratic Republic of the Congo (DRC) reported a significant number of cases and casualties resulting from measles, mainly due to the lack of funding for vaccines, as well as inadequate vaccination coverage in certain socio-demographic areas. People traveling from these countries can easily transmit the disease, though there has been a steep decline in cases since the travel ban due to coronavirus disease-2019 (COVID-19). The number of unvaccinated children currently in the USA and Canada has quadrupled since 2001. Over the past couple of years, most of the measles cases have been diagnosed in those who either did not receive the measles vaccine or complete the recommended doses of the vaccine. This paper reviews the measles outbreak, in recent years, among unvaccinated individuals in the USA, Canada, and globally.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".