Temporal and Serotypic Dynamics of Actinobacillus pleuropneumoniae in South African Porcine Populations: A Retrospective Study from 1985 to 2023
Bibliographic record
Abstract
Actinobacillus pleuropneumoniae is a major bacterial pathogen causing porcine pleuropneumoniae, which is a disease of notable economic impact and high fatality rates among pigs worldwide. It has been reported that 19 distinct serotypes of this bacterium exist. Despite its global prominence, there exists a scarcity of information regarding its prevalence and distribution in South Africa. Thus, this study used laboratory records to investigate the serotype diversity, temporal distribution, and seasonal patterns of A. pleuropneumoniae isolated from porcine samples spanning from 1985 to 2023 within South Africa. Data from laboratory registries of 354 cases, obtained from three veterinary laboratories in South Africa, were analyzed. The data were categorized into two-time frames: term 1, covering 1985 to 2001, and term 2, spanning from 2002 to 2023. The dataset identified 11 different serotypes, with serotype 7 being the most prevalent at 22.7% (n = 62), which was followed by serotype 5 at 13.8% (n = 42). The study highlighted variations in the prevalence of serotypes among diseased animals over a 38-year period. Serotypes 3, 5, 7 and 8 were commonly observed during this time, while serotype 4 was absent from 1985 to 2001, and serotypes 1, 6, and 10 were absent from 2002 to 2023. The distribution of serotypes showed a diverse variation in the age of affected animals, clinical manifestation, and seasonal occurrence. Key findings revealed that serotype 7 was the most prevalent across all seasons with the highest occurrence in winter. Additionally, Gauteng province showed the highest prevalence of various serotypes. The information collected during this study will serve as a baseline for future epidemiological studies as well as inform control strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".