Phylogeographic evaluation of the effectiveness of Canadian travel restrictions in reducing SARS-CoV-2 variant importations and burden
Bibliographic record
Abstract
Abstract Evaluating travel restriction effectiveness in mitigating infectious disease burden, exemplified by COVID-19, is critical for informing pandemic response policy, yet methodologies and results evaluating their effectiveness vary considerably. We hypothesized Canadian COVID-19 travel restrictions, including flight bans and enhanced screening, targeting focal source countries where SARS-CoV-2 variants of concern (VOCs) Alpha, Beta, Gamma, Delta, and Omicron were first identified, were variably effective towards averting introductions and case burden. We conducted a retrospective observational study using all the publicly available SARS-CoV-2 sequences and COVID-19 diagnoses up to March 2022, after which polymerase chain reaction (PCR) testing and surveillance sequencing decreased. Average daily variant cases were estimated across global regions and Canadian provinces, which informed subsampling probabilities for sequences for up to 50 000 sequences for VOCs and variants of interest from late 2020 to early 2022. Maximum likelihood phylogeographic methods were used to infer Canadian SARS-CoV-2 sublineages and singletons, representing international viral introductions with and without domestically sampled descendants. Reduction of sublineage and singleton introduction rates and proportional contributions from focal sources were quantified following interventions’ introductions. Sublineages and cases averted via VOC travel restrictions were estimated based on sublineages’ introduction rates and growth characteristics prior to restrictions. Our results suggest that across VOCs subject to targeted travel restrictions, approximately 995 (841–1151) introductions may have been prevented, accounting for an averted burden of 971 371 (321 204–1 004 575) cases, 10 685 (3533–11 050) hospitalizations, and 561 (185–580) deaths, largely accounted for by the Delta-related India flight ban. However, these estimates represent an upper bound of effectiveness if any assumptions were violated, including that introductions can be treated as independent when susceptibility is high, averted introductions mirror characteristics of observed introductions, and that travel restrictions caused sustained changes in travel behaviour. Travel restrictions were most effective when implemented rapidly following variant emergence, during exponential case growth in the focal source country, and concurrent with limited domestic and global circulation. Our analyses suggest that COVID-19 travel restrictions, particularly flight suspensions, mitigated variant case burden when global circulation was limited and case burden was high in the focal source, and highlight their value in future pandemic response, although public health benefits must be weighed against socioeconomic costs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".