Modeling the Spread of Porcine Reproductive and Respiratory Syndrome Among Pig Farms in Lira District of Northern Uganda
Bibliographic record
Abstract
Porcine Reproductive and Respiratory Syndrome (PRRS) is a viral swine disease that causes reproductive failure in breeding sows and respiratory distress in growing pigs. The main objectives were to simulate the transmission patterns of PRRS in Uganda using North American Animal Disease Spread Model (NAADSM) and to evaluate the potential effect of prevention and control options such as vaccination and movement control. The median number of infectious farms at the end of 52 weeks for the baseline scenario was 735 (36.75% of the 2,000 farms). The best effects of vaccination were observed in scenarios 60% farm coverage and 80% farm coverage, which resulted in 82 and 98.2% reduction in the median number of infectious farms at the end of the simulation, respectively. Vaccination of all medium and large farms only (33% of the farms) resulted in a 71.2% decrease in the median number of infectious farms at the end of 52 weeks. Movement control (MC) results showed that the median number of infectious farms at the end of 52 weeks decreased by 21.6, 52.3, 79.4, and 92.4% for scenarios MC 20, MC 40, MC 60, and MC 80%, respectively. This study provides new insights to the government of Uganda on how PRRS can be controlled. The large and medium farms need to be prioritized for vaccination, which would be a feasible and effective way to limit the spread of PRRS in Uganda. Scavenging pigs should be confined at all times, whether in the presence or absence of any disease outbreaks.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".