Multizone Modeling of Airborne SARS-CoV-2 Quanta Transmission and Infection Mitigation Strategies in Office, Hotel, Retail, and School Buildings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Airborne transmission of SARS-CoV-2 mostly occurs indoors, and effective mitigation strategies for specific building types are needed. Most guidance provided during the pandemic focused on general strategies that may not be applicable for all buildings. A systematic evaluation of infection risk mitigation strategies for different public and commercial buildings would facilitate their reopening process as well as post-pandemic operation. This study evaluates engineering mitigation strategies for five selected US Department of Energy prototype commercial buildings (i.e., Medium Office, Large Office, Small Hotel, Stand-Alone Retail, and Secondary School). The evaluation applied the multizone airflow and contaminant simulation software, CONTAM, with a newly developed CONTAM-quanta approach for infection risk assessment. The zone-to-zone quanta transmission and quanta fate were analyzed. The effectiveness of mechanical ventilation, and in-duct and in-room air treatment mitigation strategies were evaluated and compared. The efficacy of mitigation strategies was evaluated for full, 75%, 50% and 25% of design occupancy of these buildings under no-mask and mask-wearing conditions. Results suggested that for small spaces, in-duct air treatment would be insufficient for mitigating infection risks and additional in-room treatment devices would be needed. To avoid assessing mitigation strategies by simulating every building configuration, correlations of individual infection risk as a function of building mitigation parameters were developed upon extensive parametric studies.
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.
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 | 0.000 | 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 it