Systematic literature review of SARS-CoV-2 seroprevalence surveys in Canada through April 2021
Bibliographic record
Abstract
Objectives: To estimate the proportion of the population infected by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in Canada through April 2021, 16 months into the coronavirus disease 2019 (COVID-19) pandemic and 4 months after COVID-19 vaccines became available. Methods: Publication databases, preprint servers, public health databases and the grey literature were searched for seroprevalence surveys conducted in Canada from 1 November 2019 to 10 July 2021. Studies were assessed for bias using the Joanna Briggs Checklist. Numbers of infections derived from seroprevalence estimates were compared with reported cases to estimate under-ascertainment ratios. Results: In total, 12 serosurveys with 210,321 participants were identified. Three (25%) serosurveys were conducted at national level, one (8.3%) was conducted at provincial level, and eight (66.7%) were conducted at local level. All 12 serosurveys had moderate or high risk of bias. The proportion of the population infected by April 2021 was low (2.6%). The proportion of the population infected was higher in surveys of residents of long-term care facilities (43.0-86%), workers at long-term care facilities (22.4-32.4%), and workers in healthcare institutions (1.4-14%). Conclusions: As of April 2021, the proportion of the population infected by SARS-CoV-2 was low in the overall population of Canada, but was high in healthcare facilities, particularly long-term care facilities, supporting the need for vaccines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".