Assessment of residential mechanical ventilation for airborne infection control: A computational fluid dynamics approach for isolation strategy development
Bibliographic record
Abstract
Ventilation plays a crucial role in influencing airflow and particle dispersion, highlighting the importance of effective systems to mitigate the spread of viruses in indoor spaces. While most research has concentrated on non-residential buildings, less attention has been given to optimizing ventilation in residential settings. This study examines a typical ventilation system in a detached house in British Columbia, Canada, using Computational Fluid Dynamics simulations to evaluate its effectiveness in reducing airborne infection risks. The study investigates eight different scenarios, varying diffuser locations (near the ceiling and floor), two types of ventilation rates based on standards, and the positioning of the infected individual while considering flow, heat, and particle dynamics. The concentration of injected and distributed particles is used to assess infection probabilities as an additional risk indicator. In addition, the Wells-Riley model is applied to quantify the infection probability. The results indicate that the location of the infected person and diffuser placement significantly impact particle dispersion and infection risk, with near-ceiling outlets generally reducing concentrations more effectively than near-floor outlets. Higher ventilation rates decrease particle concentration when diffusers are near the ceiling but can increase concentrations with near-floor diffusers due to turbulence. Optimizing diffuser placement and ventilation rates is crucial for minimizing airborne transmission in residential settings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".