Exhaled aerosols and saliva droplets measured in time and 3D space: Quantification of pathogens flow rate applied to SARS‐CoV‐2
Bibliographic record
Abstract
Abstract SARS‐CoV‐2 and its ever‐emerging variants are spread from host‐to‐host via expelled respiratory aerosols and saliva droplets. Knowing the number of virions which are exhaled by a person requires precise measurements of the size, count, velocity and trajectory of the virus‐laden particles that are ejected directly from the mouth. These measurements are achieved in 3D, at 15,000 images/s, and are applied when speaking, yelling and coughing. In this study, 33 events have been analysed by post‐processing ∼500,000 images. Using these data, the flow rates of SARS‐CoV‐2 virions have been evaluated. At high concentrations, 10 7 virions/mL, it is found that 136–231 virions are ejected during a single cough, where the virion flow rate peak is capable of reaching 32 virions within a millisecond. This peak can reach tens of virions/ms when yelling but reduced to only a few virions/ms when speaking. At medium concentrations, ∼10 5 virions/mL, those results are hundreds of times lower. The total number of virions that are ejected when yelling at 110 dB, instead of speaking at 85 dB, increases by two‐ to threefold. From the measured data analysed in this article, the flow rate of other diseases, such as influenza, tuberculosis or measles, can also be estimated. As these data are openly accessible, they can be used by modellers for the simulation of saliva droplet transport and evaporation, allowing to further advance our understanding of airborne pathogen transmission. Key points Advanced, optimized and combined laser‐based imaging techniques for temporally sizing and tracking respiratory droplets and aerosols. Understanding how pathogens are being ejected from the mouth when speaking, yelling and coughing. Quantifying and analysing the variation of SARS‐CoV‐2 flow rates emission during exhalation.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".