Assessing the impact of the COVID-19 pandemic on trends of select travel-acquired enteric illnesses in Canada
Bibliographic record
Abstract
Background: Millions of Canadians contract enteric illnesses each year, many of which are acquired during, or are otherwise associated with, international travel. As the number of Canadians travelling fluctuates throughout the year, a corresponding change in the number of travel-acquired enteric illnesses was expected. A change in the number of travel-acquired enteric illnesses was also expected during the COVID-19 pandemic restrictions. Objective: This study aims to explore trends in the number and distribution of select travel-acquired enteric infections in Canada, from May 2017 to April 2023. Methods: To evaluate trends, Student's t-tests and negative binomial regression modelling were conducted. Percent changes and relative risks were calculated to assess the impact of the pandemic on travel-acquired enteric illnesses. Results: Findings demonstrated a seasonal peak in the number of reported travel-acquired enteric illnesses during the winter and spring pre- and post-pandemic travel restrictions (May 2017-February 2020 and September 2021-April 2023). Additionally, there was a decrease in the number of travel-acquired enteric illnesses added to enteric illness travel clusters with cases in more than one province or territory (multi-jurisdictional) during and after the lifting of COVID-19 travel restrictions. However, cases reported post-travel restrictions had a higher risk of being added to a multi-jurisdictional enteric illness travel cluster compared to the pre-travel restriction phase. Conclusion: Nonessential travel restrictions and changes in the healthcare-seeking behaviours due to the pandemic likely account in part for the change in the number of travel-acquired enteric illnesses observed while travel restrictions were implemented and after they were lifted. Further research is required to explain the increased risk of illnesses being added to multi-jurisdictional enteric illness travel clusters after the lifting of travel restrictions compared to pre-COVID-19.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".