Bayesian Calibration of Using CO <sub>2</sub> Sensors to Assess Ventilation Conditions and Associated COVID-19 Airborne Aerosol Transmission Risk in Schools
Bibliographic record
Abstract
Abstract Ventilation rate plays a significant role in preventing the airborne transmission of diseases in indoor spaces. Classrooms are a considerable challenge during the COVID-19 pandemic because of large occupancy density and mainly poor ventilation conditions. The indoor CO 2 level may be used as an index for estimating the ventilation rate and airborne infection risk. In this work, we analyzed a one-day measurement of CO 2 levels in three schools to estimate the ventilation rate and airborne infection risk. Sensitivity analysis and Bayesian calibration methods were applied to identify uncertainties and calibrate key parameters. The outdoor ventilation rate with a 95% confidence was 1.96 ± 0.31ACH for Room 1 with mechanical ventilation and fully open window, 0.40 ± 0.08 ACH for Rooms 2, and 0.79 ± 0.06 ACH for Room 3 with only windows open. A time-averaged CO 2 level < 450 ppm is equivalent to a ventilation rate > 10 ACH in all three rooms. We also defined the probability of the COVID-19 airborne infection risk associated with ventilation uncertainties. The outdoor ventilation threshold to prevent classroom COVID-19 aerosol spreading is between 3 – 8 ACH, and the CO 2 threshold is around 500 ppm of a school day (< 8 hr) for the three schools. Practical Implications The actual outdoor ventilation rate in a room cannot be easily measured, but it can be calculated by measuring the transient indoor CO 2 level. Uncertainty in input parameters can result in uncertainty in the calculated ventilation rate. Our three classrooms study shows that the estimated ventilation rate considering various input parameters’ uncertainties is between ± 8-20 %. As a result, the uncertainty of the ventilation rate contributes to the estimated COVID-19 airborne aerosol infection risk’s uncertainty up to ± 10 %. Other studies can apply the proposed Bayesian and MCMC method to estimating building ventilation rates and airborne aerosol infection risks based on actual measurement data such as CO 2 levels with uncertainties and sensitivity of input parameters identified. The outdoor ventilation rate and CO 2 threshold values as functions of exposure times could be used as the baseline models to develop correlations to be implemented by cheap/portable sensors to be applied in similar situations to monitor ventilation conditions and airborne risk levels.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".