Transition from saliva droplets to solid aerosols in the context of COVID-19 spreading
Bibliographic record
Abstract
To control the evolution of a pandemic such as COVID-19, knowing the conditions under which the pathogen is being transmitted represents a critical issue, especially when implementing protection strategies such as social distancing and wearing face masks. For viruses and bacteria that spread via airborne and/or droplet pathways, this requires understanding how saliva droplets evolve over time after their expulsion by speaking or coughing. Within this context, the transition from saliva droplets to solid residues, due to water evaporation, is studied here both experimentally, considering the saliva from 5 men and 5 women, and via numerical modeling to accurately predict the dynamics of this process. The model assumes saliva to be a binary water/salt mixture and is validated against experimental results using saliva droplets that are suspended in an ultrasound levitator. We demonstrate that droplets with an initial diameter smaller than 21 μm will produce a solid residue that would be considered an aerosol of <5 μm diameter in less than 2 s (for any relative humidity less than 80% and/or any temperature greater than 20°C). Finally, the model developed here accounts for the influence of the saliva composition, relative humidity and ambient temperature on droplet drying. Thus, the travel distance prior to becoming a solid residue can be deduced. We found that saliva droplets of initial size below 80 μm, which corresponds to the vast majority of speech and cough droplets, will become solid residues prior to touching the ground when expelled from a height of 160 cm.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".