Perceived risk factors and risk pathways of Rift Valley fever in cattle in Ijara district, Kenya
Bibliographic record
Abstract
Ijara district in Kenya was one of the hotspots of Rift Valley fever (RVF) during the 2006/2007 outbreak, which led to human and animal deaths causing major economic losses. The main constraint for the control and prevention of RVF is inadequate knowledge of the risk factors for its occurrence and maintenance. This study was aimed at understanding the perceived risk factors and risk pathways of RVF in cattle in Ijara to enable the development of improved community-based disease surveillance, prediction, control and prevention. A cross-sectional study was carried out from September 2012 to June 2013. Thirty-one key informant interviews were conducted with relevant stakeholders to determine the local pastoralists' understanding of risk factors and risk pathways of RVF in cattle in Ijara district. All the key informants perceived the presence of high numbers of mosquitoes and large numbers of cattle to be the most important risk factors contributing to the occurrence of RVF in cattle in Ijara. Key informants classified high rainfall as the most important (12/31) to an important (19/31) risk factor. The main risk pathways were infected mosquitoes that bite cattle whilst grazing and at watering points as well as close contact between domestic animals and wildlife. The likelihood of contamination of the environment as a result of poor handling of carcasses and aborted foetuses during RVF outbreaks was not considered an important pathway. There is therefore a need to conduct regular participatory community awareness sessions on handling of animal carcasses in terms of preparedness, prevention and control of any possible RVF epizootics. Additionally, monitoring of environmental conditions to detect enhanced rainfall and flooding should be prioritised for preparedness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".