Modelling aerosol transmission of porcine reproductive and respiratory syndrome virus between buildings using computational fluid dynamics
Bibliographic record
Abstract
An integrated computational fluid dynamics (CFD) model was developed to simulate aerosol transmission of Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) from a source to recipient building using a previously published experimental study as a test case. The integrated model consisted of CFD simulations of PRRSV aerosol movement in the atmosphere and within the recipient building, viral infectivity decay, and infection dose-response. Specific hours with the appropriate wind direction during two days (June 6 and 7, 2006) were simulated, based on historical weather data. For a given airborne PRRSV concentration exhausted from the source building, the model predicted the PRRSV distribution, infectivity decay, and probability of infection in the recipient building. Simulations indicated that wind affected the aerosol entry into the recipient building, with more stable and continuous aerosol entry at lower wind speed conditions on June 7. Elevated aerosol and PRRSV concentrations on June 7 resulted in pigs being exposed to higher doses of PRRSV than on June 6, but this only made a difference in probability of infection when there was a moderate level of PRRSV (500 TCID m−3) exhausted from the source building. At this level, there was a difference in exposure dose for pigs at different locations (pens). Overall, the positive PRRSV air sample on the morning of June 7 in the previously reported experimental study confirmed the adequacy of the model simulations, which predicted the aerosol transmission event that infected pigs in the recipient building was likely to have occurred on June 7, 2006.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".