Large community mumps outbreak in Manitoba, Canada, September 2016–December 2018
Bibliographic record
Abstract
BACKGROUND: After routine mumps immunization programs were implemented in Manitoba in the 1980s, incidence was low, with 0-9 cases of disease annually. In September 2016, a mumps outbreak began in fully vaccinated university students in Winnipeg, Manitoba. OBJECTIVE: We describe the investigation of this province-wide mumps outbreak, which lasted between September 2016 and December 2018. We present the details of public health measures implemented and challenges encountered. Possible contributing factors to the sustained transmission are also provided. METHODS: Probable and confirmed cases of mumps were investigated by public health departments using the investigation form developed for this outbreak. Confirmed mumps cases were linked to the provincial immunization registry. An outbreak response team planned and implemented control measures across the province. RESULTS: The outbreak began in vaccinated university students in September 2016 and spread across the province. Activity was high and prolonged in the northern remote areas. By the end of 2018, 2,223 cases had been confirmed. All age groups were affected, and incidence was highest among people aged 18-29 years. Two-dose coverage of mumps-containing vaccine in confirmed cases was close to 70%. CONCLUSION: This prolonged outbreak revealed a large vulnerable population likely resulting from under-vaccination and waning vaccine-induced immunity in the absence of natural boosting from exposure to mumps virus. It is important to maintain high two-dose coverage with mumps-containing vaccines. A third dose of mumps-containing vaccine in future outbreaks may be considered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".