Measles in Canada: modelling outbreaks with variable vaccine coverage and interventions
Bibliographic record
Abstract
BACKGROUND: The global incidence of measles has increased markedly since 2023. In Canada, where measles has had elimination status for more than two decades, most cases can typically be traced to travel. While the majority of Canadians are vaccinated against the measles virus, or considered immune due to previous infection, there are communities with low vaccination coverage. METHODS: In this study, we develop a stochastic Susceptible-Exposed-Infectious-Recovered model to explore what measles outbreaks could look like upon importation into Canada under a number of scenarios, vaccination coverage levels, and public health interventions. We collect reports of real-world measles outbreaks and compare them to our model outbreaks' size and duration. RESULTS: Our model suggests that community level outbreaks can be controlled at or above 85% vaccination coverage with public health interventions and that above 95% coverage, 99% of measles introductions do not result in an outbreak. Below 85% coverage, outbreaks in small communities (size 1000) with relatively strong public health measures range from median size of under 4 (80% coverage) to 186 (55%), comparable to reported outbreaks in Canada and elsewhere. Outbreaks very often last under 60 days. We characterize how outbreak sizes and durations depend on the strength of interventions, community size and vaccination coverage. We make the model available as a web-based 'shiny' application. CONCLUSIONS: Since the vast majority of measles cases in Canada can be traced to imported cases, our model serves as a last step in the chain of actions needed to bridge from global measles outbreaks to local scenarios within Canada. Given cases entering Canada, we are able to project the duration and size of an outbreak, helping to inform the public of the measles-related risk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".