Seroprevalence of leptospirosis and Japanese encephalitis in swine in ten provinces of Vietnam
Bibliographic record
Abstract
BACKGROUND: Leptospirosis is an important zoonotic disease with a global distribution, affecting a wide range of mammalian animals and humans. Japanese encephalitis (JE) virus is the major vector-borne zoonotic disease in the Asia-Pacific region. The main objective of this study was to evaluate the seroprevalence of serovar-specific Leptospira and JE in swine from 10 provinces in Vietnam. METHODS: Samples were initially collected for swine influenza surveillance from March to April 2017 at large-scale farms (with at least 50 sows and/or 250 fattening pigs) with pigs that tested positive for influenza in the previous surveillance period (2015-16). FINDINGS: A total of 2,000 sera samples were analyzed from 10 provinces. Overall, the seroprevalence of leptospirosis was 21.05% (95% CI: 19.28-22.90) using a cut-off titer of ≥ 1:100. The apparent prevalence of JE was 73.45% (95% CI: 71.46-75.37) while the true prevalence was slightly higher (74.46%, 95% credible interval: 73.73-86.41). We found a relatively high presence of leptospirosis and JE in pigs kept on large farms. Prevalence was comparable with other studies suggesting opportunistic testing of samples collected for other surveillance purposes can be a valuable tool to better understand and prevent the potential transmission of these zoonotic diseases from pigs to people in Vietnam. CONCLUSION: Our study provides evidence to veterinarians and animal health professionals for evidence-based practice such as diagnosis, vaccination and zoonotic control. Further investigation into the possible role of different domestic animals, wildlife species or environmental factors is needed to identify the potential risk factors and transmission routes in Vietnam.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".