Practical Indicators for Risk of Airborne Transmission in Shared Indoor Environments and their Application to COVID-19 Outbreaks
Bibliographic record
Abstract
Abstract Some infectious diseases, including COVID-19, can be transmitted via aerosols that are emitted by an infectious person and inhaled by susceptible individuals. Most airborne transmission occurs at close proximity and is effectively reduced by physical distancing, but as time indoors increases, infections occur in those sharing room air despite maintaining distancing. There have been calls for quantified models to estimate the absolute and relative contribution of these different factors to infection risk. We propose two indicators of infection risk for this situation, i.e., relative risk parameter (H r ) and risk parameter (H). They combine the key factors that control airborne disease transmission indoors: virus-containing aerosol generation rate, breathing flow rate, masking and its quality, ventilation and particulate air cleaning rates, number of occupants, and duration of exposure. COVID-19 outbreaks show a clear trend in relation to these factors that is consistent with airborne infection The observed trends of outbreak size (attack rate) vs. H (H r ) allow us to recommend values of these parameters to minimize COVID-19 indoor infection risk. Transmission in typical pre-pandemic indoor spaces is highly sensitive to mitigation efforts. Previous outbreaks of measles, flu, and tuberculosis were assessed along with recently reported COVID-19 outbreaks. Measles outbreaks occur at much lower risk parameter values than COVID-19, while tuberculosis outbreaks are observed at much higher risk parameter values. Since both diseases are accepted as airborne, the fact that COVID-19 is less contagious than measles does not rule out airborne transmission. It is important that future outbreak reports include information on the nature and type of masking, ventilation and particulate-air cleaning rates, number of occupants, and duration of exposure, to allow us to understand the circumstances conducive to airborne transmission of different diseases. Synopsis We propose two infection risk indicators for indoor spaces and apply them to COVID-19 outbreaks analysis and mitigation.
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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 | 0.001 |
| 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".