Bioaerosol and surface sampling for the surveillance of influenza A virus in swine
Bibliographic record
Abstract
Influenza A virus in swine is of significant importance to human and veterinary public health. Environmental sampling techniques that prove practical would enhance surveillance for influenza viruses in swine. The primary objective of this study was to demonstrate the feasibility of bioaerosol and surface sampling for the detection of influenza virus in swine barns with a secondary objective of piloting a mobile application for data collection. Sampling was conducted at a large swine operation between July 2016 and August 2017. Swine oral fluids and surface swabs were collected from multiple rooms. Room-level air samples were collected using four bioaerosol samplers: a low volume polytetrafluoroethylene (PTFE) filter sampler, the National Institute for Occupational Safety and Health's low volume cyclone sampler, a 2-stage Andersen impactor and/or one high volume cyclonic sampler. Samples were analysed using quantitative RT-PCR. Data and results were reported using a mobile data application. Eighty-nine composite oral fluid samples, 70 surface swabs and 122 bioaerosol samples were analysed. Detection rates for influenza virus RNA in swine barn samples were 71.1% for oral fluids, 70.8% for surface swabs and 71.1% for the PTFE sampler. Analysis revealed a statistically significant relationship between the results of the PTFE sampler and the surface swabs with oral fluid results (p < 0.001 and p < 0.01 respectively). In addition, both the PTFE sampler (p < 0.01) and surface swabs (p = 0.03) significantly correlated with, and predicted oral fluid results. Bioaerosol sampling using PTFE samplers is an effective hands-off approach for detecting influenza virus activity among swine. Further study is required for the implementation of this approach for surveillance and risk assessment of circulating influenza viruses of swine origin. In addition, mobile data collection stands to be an invaluable tool in the field by allowing secure, real-time reporting of sample collection and results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".