Seroprevalence of antibodies for bovine viral diarrhoea virus, Brucella abortus and Neospora caninum, and their roles in the incidence of abortion/foetal loss in dairy cattle herds in Nakuru District, Kenya
Bibliographic record
Abstract
BACKGROUND: No comprehensive studies have been carried out on the infectious causes of abortion in Kenyan dairy cattle herds. A survey was carried out to determine the seroprevalence of antibodies against Bovine Viral Diarrhoea Virus (BVDV), Brucella abortus (BA) and Neospora caninum (NC) among dairy cattle herds in Nakuru County, a major dairying area in Kenya. A prospective sero-epidemiological study was also undertaken to investigate the effects of BVDV, BA and NC on the occurrence of bovine abortion in dairy cattle herds, where monthly rectal palpations for pregnancy were performed, and monthly serum samples were tested for antibodies to the 3 pathogens. RESULTS: In the 398 randomly selected cattle on 64 dairy herds, the seroprevalences of antibodies to BVDV, NC and BA were 79.1, 25.6 and 16.8%, respectively. Of the cattle seropositive to NC, 83.3% were also seropositive to BVDV and 13.7% to BA. Of the cattle seropositive to BVDV, 17.1% were also seropositive to BA. Among 260 monitored pregnant dairy cattle on the same 64 dairy farms, an incidence risk for abortion of 10.8% (28/260) was identified, while the incidence of other foetal losses was 1.1% (3/260). The incidence rates of sero-conversion for NC, BVD and BA were 1.1, 0.06 and 0.5 new infections/100 cow-months at risk, respectively. The foetal losses were mainly observed in animals less than 96 months old and occurred in mid-gestation. Neospora caninum was associated with most cases (29.0%) of foetal losses, followed by mixed infections of NC and BVDV (12.9%), BVDV (9.9%) and co-infections of BA and NC (6.5%). CONCLUSIONS: This is the first study to document the substantial incidence risk of BVDV and NC abortions in dairy cattle in Kenya, and demonstrates the relative importance of BA, BVDV and NC infections in dairy cattle in Kenya. Kenya laboratories should offer diagnostic tests for BVDV and NC to help farmers determine their roles in abortions on their farms. A comprehensive policy on the control of these important diseases should also be put in place by government with the involvement of all stakeholders in the dairy cattle industry.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".