Practical Mitigation Strategies for Countering the Spread of Aerosolized COVID-19 Virus (SARS-CoV-2) Using Ventilation and HEPA Air Purifiers: A Literature Review
Bibliographic record
Abstract
This paper assesses, through an extensive literature review, the use of ventilation and High-Efficiency Particulate Air (HEPA) purifiers as practical mitigation strategies for reducing the spread of aerosolized COVID-191 virus. HEPA is a well-defined standard by the U.S. Department of Energy for filters. The focus of the literature review was on indoor air quality (IAQ) and COVID-19, with a particular emphasis on classroom settings. The start of the review, January 2020, was chosen to coincide with the first cases of COVID-19 in North America. Although children under the age of 12 are currently not yet vaccinated, there is mounting pressure for a return to normal by the start of the new school year, 2021. Also, many classrooms lack pre-installed mechanical ventilation systems (Olsiewski et al., 2021); therefore, mitigation in classrooms often falls solely in the hands of teachers and students. Research shows that ventilation and air purification are essential tools to counter aerosolized transmission (Curtius et al. (2020), the inhaled dose of particles containing virus RNA is six times lower when using air purifiers with an ACH (air changes per hour) of 5.7. However, ventilation and air purifiers are not replacements for masks, which remain vital for countering droplet (>5 μm) transmission. In addition, occupancy (i.e., number and proximity of people present in a given area) and group activity levels (e.g., talking, shouting, singing) play a critical role in viral transmission. Although natural ventilation by opening windows can be an essential strategy to help counter the spread of the virus, the level of ventilation offered by opening windows is largely uncontrollable as it is subject to weather conditions and building design. One must also consider the energy implications (i.e., loss of heat) that this strategy carries. Scientific evidence shows that varying levels of continuous and/or intermittent ventilation, either mechanical or natural, combined with the use of HEPA air purifiers, can provide a higher degree of protection than window access alone (Curtius et al., 2020). Systematic deployment of a hybrid mitigation strategy incorporating both ventilation and HEPA air purification in schools, offices, or other facilities offers a practical way to establish a safe re-opening of society in Canada.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".