Frequency of Bovine Respiratory Disease Complex Bacterial and Viral Agents Using Multiplex Real-Time qPCR in Quebec, Canada, from 2019 to 2023
Bibliographic record
Abstract
The bovine respiratory disease complex (BRD) is a multifactorial disease caused by various bacterial and viral pathogens. Using rapid pathogen detection techniques is helpful for tailoring therapeutic and preventive strategies in affected animals and herds. The objective of this study was to report the frequency of 10 pathogens by multiplex RT-qPCR on samples submitted for BRD diagnosis to a diagnostic laboratory (Biovet Inc., QC, Canada) in the Province of Quebec, Eastern Canada. From the 1st of January 2019 to the 31st of December 2023, a total of 1875 samples were analyzed. Most samples collected were individual samples (1547 of 1860 samples for which information was available (83.17%)), and the rest were from pooled samples of 2 (8.55%, n = 159) or ≥3 specimens (8.28%, n = 154). In 19.3% of the samples (n = 362), no pathogen was found, whereas 54.1% of samples had two or more different pathogens. Among the viruses, bovine coronavirus (BCV) was the most commonly found (27.5% of samples, n = 516), followed by bovine respiratory syncytial virus (BRSV) (17.7%, n = 332), whereas, for bacteria, Pasteurella multocida (50.1%, n = 940) and Mannheimia haemolytica (26.9%, n = 505) were the most common. The frequency of samples positive for Histophilus somni, Mycoplasmopsis bovis, influenza type D virus (IDV), bovine parainfluenza virus type 3 (BPI3V), bovine herpesvirus type 1 (BHV1), and bovine viral diarrhea virus (BVDV) was 22.6%, 22.4%, 4.6%, 4.3%, 2.7%, and 0.9%, respectively. In the multivariable Poisson regression model, the total number of pathogens increased with the number of animals in the pool, with an incidence risk ratio (IRR) of 1.15 (95% CI 1.02–1.29) and 1.32 (1.18–1.47) for 2 individuals in the pool and ≥3 individuals vs. individual samples, respectively. An increased number of pathogens were isolated in the winter season (IRR = 1.28 (95% CI 1.17–1.40)) compared to fall, and a lower number of pathogens were isolated in the summer compared to fall (IRR = 0.82 (95% CI 0.73–0.92)). These seasonal differences were mostly driven by the number of viruses isolated. This study gives interesting insights on the circulation of BRD pathogens in cattle from Eastern Canada.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".