Investigation and management of a large community mumps outbreak among young adults in Toronto, Canada, January 2017–February 2018
Bibliographic record
Abstract
BACKGROUND: In 2017, a mumps outbreak was identified in a cohort of 18-34 year olds in Toronto, Canada. OBJECTIVE: To describe a large community mumps outbreak in an urban centre from January 2017 to February 2018 among young adults. METHODS: A broad range of interventions were implemented in an attempt to reach the target audience; including case and contact management, vaccination clinics at schools and clinicians' offices, school exclusions, bar inspections, traditional communication strategies (including health care provider updates and posters) and newer communication strategies (including three sequential social media campaigns). RESULTS: A total of 143 cases of mumps were identified. Although cases' ages ranged from three to 72 years, most (76%) were 18-34 year olds, many of whom had frequented bars and local food establishments in downtown Toronto. 84% (n=120) of the cases were community-acquired. Only 16% (n=23) of the cases reported exposures in schools and post-secondary school institutions. Of those, 39% (n=56) of cases had an unknown vaccination history; 34% (n=49) were either not vaccinated or partially vaccinated with one dose of measles-mumps-rubella vaccine; and 27% (n=38) had received the recommended two doses of mumps vaccine. Determining vaccination status was a challenge, in part due to the lack of a registry. Vaccination was recommended when subjects were known to have had fewer than two doses of vaccine or had an unknown vaccination status. A social media campaign, emphasizing the risk of social activities if not protected from the mumps, yielded over 500,000 impressions from Facebook and Twitter messages and ads and an impressive engagement rate of between 1% and 10x%. CONCLUSION: This was the largest mumps outbreak in Toronto in over 20 years. Among young adults, ongoing social media and traditional communication campaigns can contribute to the control of community mumps outbreaks. Encouraging vaccine uptake is desirable, but without a vaccine registry it is difficult to assess vaccination coverage among adults. Susceptible cohorts of young adults who were not adequately vaccinated pose a risk for future outbreaks. Given that almost 30% of the mumps cases were fully vaccinated with two doses of mumps-containing vaccine, even two doses may not provide complete protection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".