Emergence of rabies in the Gauteng Province, South Africa: 2010–2011
Bibliographic record
Abstract
Canine rabies is enzootic throughout Sub-Saharan Africa, including the Republic of South Africa. Historically, in South Africa the coastal provinces of KwaZulu-Natal and Eastern Cape were most affected. Alarmingly, outbreaks of canine rabies have been increasingly reported in the past decade from sites where it has previously been under control. From January 2010 to December 2011, 53 animal rabies cases were confirmed; these were mostly in domestic dogs from southern Johannesburg, which was previously considered to be rabies free. In addition, one case was confirmed in a 26-month old girl who had been scratched by a pet puppy during this period. The introduction of rabies into Gauteng Province was investigated through genetic analysis of rabies positive samples confirmed during the outbreak period. In addition, the nucleotide sequences of incidental cases reported in the province for the past ten years were also included in the analysis. It was found that the recent canine rabies outbreak in the Gauteng Province came from the introduction of the rabies virus from KwaZulu-Natal, with subsequent local spread in the susceptible domestic dog population of southern Johannesburg. The vulnerability of the province was also highlighted through multiple, dead-end introductions in the past ten years. This is the first report of a rabies outbreak in the greater Johannesburg area with evidence of local transmission in the domestic dog population.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".