Outbreaks of mumps, measles and rubella in British Columbia, Canada
Bibliographic record
Abstract
Background: There has been a resurgence of mumps, measles and rubella in many parts of the developed world, particularly among older children and young adults. This is due to these age groups experiencing a decline in natural exposure to these viruses as a result of past successful vaccination programs, and the current lower vaccination uptake in some communities. One factor behind reluctance to vaccination is the lack of awareness about the risk of infection and the seriousness of the disease. This paper describes three outbreaks initiated by international travel in a country with high quality of health care, but pockets of insufficient vaccination coverage for the MMR vaccine. Methods: The epidemiology of three distinct outbreaks within the same local health authority between 2010 and 2011 are described in terms of case demographics, immunisation status and setting. Results: In all three outbreaks, the majority of cases were young adults who were either unimmunised or received only one dose of measles, mumps or rubella vaccine. Following the winter Olympics twenty-one cases of locally-acquired measles were identified with two geographically distinct genotypes (H1 and D8). Second, an outbreak of rubella in a workplace was identified (n = 7). The index case had travelled to their country of origin where the disease is still endemic and on their return worked while infectious. Third, an outbreak of mumps occurred among international seasonal workers at a resort community. A total of 108 cases were identified before successful control in the community of origin via targeted immunisation. Conclusion: These outbreaks confirm the existence of susceptible groups that require additional targeted strategies to achieve ≥95% coverage and thereby increase immunity levels in the population beyond that provided by routine childhood immunization. Alongside the need for high vaccination coverage, these outbreaks highlight the need to maintain effective disease surveillance that permits timely implementation of outbreak control measures. In the context of international travel, mass gathering events, immigrants visiting countries of origin and transient foreign workers can all initiate introductionof pathogens that are uncommon to the host country. This compounded by the silent accumulation of susceptible individuals facilitate transmission resulting in community outbreaks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".