Prevalence Estimates of Antibodies Towards Foot-and-Mouth Disease Virus in Small Ruminants in Uganda
Bibliographic record
Abstract
Foot-and-mouth disease (FMD) is endemic in Uganda with control strategies focusing on vaccination of cattle, while small ruminants are largely ignored. In order for Uganda to establish effective control strategies, it is crucial that the epidemiology of the disease is fully understood. This study summarizes results of serological investigations of sheep and goats for antibodies to FMDV from four districts in 2006 following an FMD outbreak in the region and from an attempted comprehensive random sampling in two districts in 2007. Antibodies were quantified and serotyped using competitive ELISA for antibodies towards non-structural proteins (NSP) and structural proteins towards serotype O, and blocking ELISA for antibodies towards the seven serotypes of FMD virus (FMDV). In 2006, sheep and goats in Bushenyi and Isingiro districts were free from antibodies towards FMDV, while herds in Kasese and Mbarara districts excluding Kahendero village were all positive for antibodies towards NSP and SP-O. In 2007, mean prevalence estimates of antibodies towards FMDV NSP was 14% in goats and 22% in sheep in Kasese district, while Bushenyi was still free. The difference between these two districts probably reflects different levels of FMDV challenge attributed to the variation in exposure rates which again in part may be as a result of the differing husbandry practices. Contrary to 2006, with clear antibodies towards serotype O, the serotype-specificity of the antibodies was less clear in 2007, as antibodies towards both serotype O and SAT serotypes were identified. Our results show that goats and sheep are infected during FMD outbreaks, and that they may be useful for determining the serotype of FMD outbreaks in Uganda, if they are sampled shortly after an outbreak.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".