Transmission dynamics of SARS-CoV-2 in British Columbia’s largest school district during the second half of the 2020–2021 school year
Bibliographic record
Abstract
OBJECTIVES: To determine the extent and characteristics of in-school transmission of SARS-CoV-2 and determine risk factors for in-school acquisition of COVID-19 in one of Canada's largest school districts. METHODS: We conducted a retrospective chart review of all reportable cases of COVID-19 who attended a kindergarten-Grade 12 (K-12) school within the study area between January and June of the 2020-2021 school year. The acquisition source was inferred based on epidemiological data and, when available, whole genome sequencing results. Mixed effects logistic regression was performed to identify risk factors independently associated with in-school acquisition of COVID-19. RESULTS: Overall, 2877 cases of COVID-19 among staff and students were included in the analysis; of those, 9.1% had evidence of in-school acquisition. The median cluster size was two cases (interquartile range: 1). Risk factors for in-school acquisition included being male (adjusted odds ratio [aOR]: 1.59, 95% confidence interval [CI]: 1.17-2.17), being a staff member (aOR: 2.62, 95% CI: 1.64-4.21) and attending or working in an independent school (aOR: 2.28, 95% CI: 1.13-4.62). CONCLUSION: In-school acquisition of COVID-19 was uncommon during the study period. Risk factors were identified in order to support the implementation of mitigation strategies that can reduce transmission further.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 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".