A Five-Year Retrospective Study of Foot-and-Mouth Disease Outbreaks in Southern Africa, 2014 to 2018
Bibliographic record
Abstract
Foot-and-mouth disease (FMD) virus (FMDv), like other ribonucleic acid (RNA) genome viruses, has a tendency to mutate rapidly. As such, available vaccines may not confer enough cross-protection against incursion of new lineages and sublineages. This paper is a retrospective study to determine the topotypes/lineages that caused previous FMD outbreaks in 6 southern African countries and the efficacy of the current vaccines to protect cattle against them. A total of 453 bovine epithelial tissue samples from 33 FMD outbreaks that occurred in these countries from 2014 to 2018 were investigated for the presence of FMDv. The genetic diversity of the identified Southern African Type (SAT)-FMD viruses was determined by comparing sequences from outbreaks and historical prototype sequences. Of the 453 samples investigated, 176 were positive for four FMDv serotypes. Out of the 176 FMD positive cases there were 105 SAT2 samples, 32 SAT1 samples, 21 SAT3 samples, and 18 serotype O samples. Phylogenetic analysis grouped the SATs VP1 gene sequences into previously observed topotypes in southern Africa. SAT1 viruses were from topotypes I and III, SAT2 viruses belonged to topotypes I, II, III, and IV, and SAT3 viruses were of topotypes I and II. Vaccine matching studies on the field FMDv isolates produced r1-values greater than or equal to 0.3 for the three SAT serotypes. This suggests that there is no significant antigenic difference between current SAT FMD vaccine strains and the circulating SAT serotypes. Therefore, the vaccines are still fit-purpose for the control FMD in the region. The study did not identify incursion of any new lineages/topotypes of FMD into the sampled southern African countries.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".