Dynamics of Virus Distribution in a Defined Swine Production Network Using Enteric Viruses as Molecular Markers
Bibliographic record
Abstract
Modern swine production systems represent complex and dynamic networks involving numerous stakeholders. For instance, livestock transporters carry live animals between fattening sites, abattoirs, and other premises on a daily basis. This interconnected system may increase the risk of microbial spread within and between networks, although little information is available in that regard. In the present study, a swine network composed of 10 finishing farms, one abattoir, and three types of stakeholders (veterinarians, livestock transporters, and nutritional technicians) in Quebec, Canada, was selected to investigate specific vectors and reservoirs of enteric viruses. Environmental samples were collected from the premises over a 12-month period. Samples were screened using targeted reverse transcription-PCR and sequencing of two selected viral markers, group A rotaviruses (RVA) and porcine astroviruses (PoAstV), both prevalent and genetically heterogeneous swine enteric viruses. The results revealed frequent contamination of farm sites (21.4 to 100%), livestock transporter vehicles (30.6 to 68.8%) and, most importantly, the abattoir yard (46.7 to 94.1%), depending on the sample types. Although high levels of strain diversity for both viruses were found, identical PoAstV and RVA strains were detected in specific samples from farms, the abattoir yard, and the livestock transporter vehicle, suggesting interconnections between these premises and transporters. Overall, the results from this study underscore the potential role of abattoirs and livestock transport as a reservoir and transmission route for enteric viruses within and between animal production networks, respectively. IMPORTANCE: Using rotaviruses and astroviruses as markers of enteric contamination in a swine network has revealed the potential role of abattoirs and livestock transporters as a reservoir and vectors of enteric pathogens. The results from this study highlight the importance of tightening biosecurity measures. For instance, implementing sanitary vacancy between animal batches and emphasizing washing, disinfection, and drying procedures on farms and for transportation vehicles, as well as giving limited access and circulation of vehicles throughout the production premises, are some examples of measures that should be applied properly. The results also emphasize the need to closely monitor the dynamics of enteric contamination in the swine industry in order to better understand and potentially prevent the spread of infectious diseases. This is especially relevant when a virulent and economically damaging agent is involved, as seen with the recent introduction of the porcine epidemic diarrhea virus in the country.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".