Recognition of aerosol transmission of infectious agents: a commentary
Bibliographic record
Abstract
Although short-range large-droplet transmission is possible for most respiratory infectious agents, deciding on whether the same agent is also airborne has a potentially huge impact on the types (and costs) of infection control interventions that are required.The concept and definition of aerosols is also discussed, as is the concept of large droplet transmission, and airborne transmission which is meant by most authors to be synonymous with aerosol transmission, although some use the term to mean either large droplet or aerosol transmission.However, these terms are often used confusingly when discussing specific infection control interventions for individual pathogens that are accepted to be mostly transmitted by the airborne (aerosol) route (e.g. tuberculosis, measles and chickenpox). It is therefore important to clarify such terminology, where a particular intervention, like the type of personal protective equipment (PPE) to be used, is deemed adequate to intervene for this potential mode of transmission, i.e. at an N95 rather than surgical mask level requirement.With this in mind, this review considers the commonly used term of 'aerosol transmission' in the context of some infectious agents that are well-recognized to be transmissible via the airborne route. It also discusses other agents, like influenza virus, where the potential for airborne transmission is much more dependent on various host, viral and environmental factors, and where its potential for aerosol transmission may be underestimated.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".