CO2 levels and SARS-CoV-2 transmission in public schools: A retrospective cohort study in Montreal (Canada)
Bibliographic record
Abstract
Scientific evidence suggests that SARS-CoV-2 is transmitted primarily through close contact with susceptible individuals and that the risk of transmission increases during prolonged exposure in confined, inadequately ventilated and densely occupied spaces. In response to concerns related to inadequate ventilation for students and staff, CO2 concentrations were measured in all of Montreal’s public elementary and high schools during winter of 2020–21. This study aims to evaluate the associations between ventilation system types, mid-class CO2 concentrations and SARS-CoV-2 transmission amongst Montreal's public school’s attendees during the 2020–2021 school year. Data on building ventilation types, CO2 measurements and school characteristics such as total enrollment and socioeconomic status (SES), were sourced from Ministry of Education’s administrative data for the 2020–2021 school year. During this pandemic period, the Montreal Public Health Department investigated COVID-19 cases among students and staff in public elementary and high schools, gradually developing a regional database of school cases and outbreaks from intervention files. Negative binomial regression models were employed to examine associations between mid-class CO2 concentrations and COVID-19 incidence rates for both total cases and those acquired within schools. Regression models were adjusted for school types, neighborhood COVID-19 incidence rates and school SES. The mid-class CO2 concentrations median was 1050 ppm in the 384 school buildings. CO2 concentrations were associated with ventilation systems, with schools using natural ventilation exhibiting higher concentrations compared to three other types of mechanical ventilation. No positive association was observed between the incidence rates of school-acquired cases and higher mid-class CO2 concentrations in both unadjusted and adjusted models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".