The impact of heating, ventilation, and air conditioning design features on the transmission of viruses, including the 2019 novel coronavirus: a systematic review of ventilation and coronavirus
Bibliographic record
Abstract
Abstract Aerosol transmission has been a pathway for virus spread for many viruses. Similarly, emerging evidence regarding SARS-CoV-2, and the resulting pandemic as declared by WHO in March 2020, determined aerosol transmission for SARS-CoV-2 to be significant. As such, public health officials and professionals have sought data regarding the effect of Heating, Ventilation, and Air Conditioning (HVAC) features to control and mitigate viruses, particularly coronaviruses. A systematic review was conducted using international standards to identify and comprehensively synthesize research examining the effectiveness of ventilation for mitigating transmission of coronaviruses. The results from 32 relevant studies showed that: increased ventilation rate was associated with decreased transmission, transmission probability/risk, infection probability/risk, droplet persistence, virus concentration, and increased virus removal and virus particle removal efficiency; increased ventilation rate decreased risk at longer exposure times; some ventilation was better than no ventilation; airflow patterns affected transmission; ventilation feature (e.g., supply/exhaust, fans) placement influenced particle distribution. Some studies provided qualitative recommendations; however, few provided specific quantitative ventilation parameters suggesting a significant gap in current research. Adapting HVAC ventilation systems to mitigate virus transmission is not a one-solution-fits-all approach but instead requires consideration of factors such as ventilation rate, airflow patterns, air balancing, occupancy, and feature placement. Practical Implications Increasing ventilation, whether through ventilation rates (ACH, m 3 /h, m 3 /min, L/min) or as determined by CO 2 levels (ppm), is associated with decreased transmission, transmission probability/risk, infection probability/risk, droplet persistence, and virus concentration, and increased virus removal and efficiency of virus particle removal. As well, professionals should consider the fact that changing ventilation rate or using mixing ventilation is not always the only way to mitigate and control viruses as varying airflow patterns and the use of ventilation resulted in better outcomes than situations without ventilation. Practitioners also need to consider occupancy, ventilation feature (supply/exhaust and fans) placement, and exposure time in conjunction with both ventilation rates and airflow patterns. Some recommendations with quantified data were made, including using an air change rate of 9 h -1 for a hospital ward; waiting six air changes or 2.5 hours before allowing different individuals into an unfiltered office with ∼2 fresh air changes (FCH) and one air change for a high-efficiency MERV or HEPA filtered laboratory; and using a pressure difference between -2 and -25 Pa in negative pressure isolation spaces. Other recommendations for practice included using or increasing ventilation, introducing fresh air, using maximum supply rates, avoiding poorly ventilated spaces, assessing fan placement and potentially increasing ventilation locations, and employing ventilation testing and air balancing checks.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".