City Reduced Probability of Infection (CityRPI) for Indoor Airborne Transmission of SARS-CoV-2 and Urban Building Energy Impacts
Bibliographic record
Abstract
ABSTRACT Airborne transmission of aerosols produced by asymptomatic individuals is a large portion of the SARS-CoV-2 spread indoors. Outdoor air ventilation rate, air filtration, room occupancy, exposure time, and mask-wearing are among the key parameters that affect its airborne transmission in indoor spaces. In this work, we developed a new web-based platform, City Reduced Probability of Infection - CityRPI, to calculate the indoor airborne transmission of COVID-19 in various buildings of a city scale. An archetype library of twenty-nine building types is developed based on several standards and references. Among the mitigation strategies recommended to reduce infection risk, some could result in significant energy impacts on buildings. To study the combined effects of energy consumption and reduced infection probability, we integrated CityRPI with City Building Energy Model. We applied the integrated model to Montreal City and studied the impact of six mitigation measures on the infection risk and peak energy demand in winter. It shows that the same strategy could perform quite differently, depending on building types and properties. In the winter season, increasing the outdoor air ventilation rate may cause massive building energy consumption. All strategies are shown to reduce the infection risk but wearing a mask and reducing exposure time are the most effective strategies in many buildings, with around 60% reduction. Doubling the outdoor air ventilation rate is not as effective as other strategies to reduce the risk with less than 35% reduction. It also significantly increases building peak heating demand with 10-60%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".