The role of case importation in explaining differences in early SARS-CoV-2 transmission dynamics in Canada—A mathematical modeling study of surveillance data
Bibliographic record
Abstract
OBJECTIVE: The North American coronavirus disease-2019 (COVID-19) epidemic exhibited distinct early trajectories. In Canada, Quebec had the highest COVID-19 burden and its earlier March school break, taking place two weeks before those in other provinces, could have shaped early transmission dynamics. METHODS: We combined a semi-mechanistic model of SARS-CoV-2 transmission with detailed surveillance data from Quebec and Ontario (initially accounting for 85% of Canadian cases) to explore the impact of case importation and timing of control measures on cumulative hospitalizations. RESULTS: A total of 1544 and 1150 cases among returning travelers were laboratory-confirmed in Quebec and Ontario, respectively (symptoms onset ≤03-25-2020). Hospitalizations could have been reduced by 55% (95% CrI: 51%-59%) if no cases had been imported after Quebec's March break. However, if Quebec had experienced Ontario's number of introductions, hospitalizations would have only been reduced by 12% (95% CrI: 8%-16%). Early public health measures mitigated the epidemic spread as a one-week delay could have resulted in twice as many hospitalizations (95% CrI: 1.7-2.1). CONCLUSION: Beyond introductions, factors such as public health preparedness, responses and capacity could play a role in explaining interprovincial differences. In a context where regions are considering lifting travel restrictions, coordinated strategies and proactive measures are to 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".