Travel-related Zika virus cases in Canada: October 2015–June 2017
Bibliographic record
Abstract
BACKGROUND: Zika virus (ZIKV) is an emerging mosquito-borne disease that can cause severe birth defects if contracted congenitally. Since late 2015, there has been a large increase in the number of travel-related cases of Zika virus infection in Canada. OBJECTIVE: The objective of this study was to describe the epidemiology of travel-related Zika cases in Canada from October 2015 to June 2017 and review them in the context of the international outbreak in the Americas. METHODS: Zika virus infections were confirmed by polymerase chain reaction (PCR) detection of viral RNA and/or the serological identification of ZIKV-specific antibodies in serum. Cases of ZIKV infection were identified by provincial and territorial health authorities, and reported on a regular basis to the Public Health Agency of Canada (PHAC). Case information requested included date of illness onset, age category, sex, pregnancy status, and location(s) and dates of travel. Estimates for the monthly number of Canadians travelling outside of Canada to other countries in the Americas were obtained from Statistics Canada and the International Air Transport Association (IATA). Data to produce the epidemic curves of autochthonous cases for each region of the Americas were extracted from country-specific epidemic curves on the Pan American Health Organization website. RESULTS: As of June 7, 2017, 513 laboratory confirmed cases and two Zika-related birth/fetal anomalies were reported across all 10 provinces. Illness in Canadian travellers generally coincided with outbreak intensity in the country of exposure rather than travel volume. There has been no evidence of autochthonous (local) transmission in Canada. Currently, cases are on the decline both in Canada and internationally. CONCLUSION: The surge in Canadian ZIKV infections in 2016 was directly related to the incursion and spread of ZIKV into the Americas. Although cases are now on the decline worldwide, it remains to be seen whether a resurgence of cases in previously affected or new areas will occur. Both outbreak intensity and seasonality of ZIKV transmission should be monitored over time in order to inform the timing of public health education campaigns, as some may turn out to be more effective in the off-peak travel season when the risk of disease transmission may be higher. Ongoing education and awareness among travellers, particularly for pregnant women and those planning pregnancies, is still indicated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".