A Cross-Sectional, Population-Based, Seroepidemiological Study of Rift Valley Fever in Cameroonian Cattle Populations
Bibliographic record
Abstract
Rift Valley fever (RVF) is an important emerging zoonoses causing abortion and neonatal deaths in livestock and hemorrhagic fever in humans. It is typically characterized by acute epidemics with abortion storms often preceding human disease and these events have been associated with the El Niño weather cycles. Outside of areas that experience epidemics, little is known about its epidemiology. Here, we present results from a serological study using biobank samples from a study of cattle conducted in 2013 at two sites in Cameroon. A total of 1,458 cattle from 100 herds were bled and sera screened using a commercially available RVF ELISA. The overall design-adjusted animal-level apparent seroprevalence of RVF exposure for the Northwest Region (NWR) of Cameroon was 6.5% (95% CI: 3.9-11.0) and for the Vina Division (VIN) of the Adamawa Region was 8.2% (95% CI: 6.2-11.0). The age-stratified serological results were also used to estimate the force of infection, and the age-independent estimates were 0.029 for the VIN and 0.024 for the NWR. The effective reproductive number was ~1.08. Increasing age and contact with wild antelope species were associated with an increased risk of seropositivity, while high altitudes and contact with buffalo were associated with a reduced risk of seropositivity. The serological patterns are more consistent with an endemical stability rather than the more typical epidemic patterns seen in East Africa. However, there is little surveillance in livestock for abortion storms or in humans with fevers in Cameroon, and it is, therefore, difficult to interpret these observations. There is an urgent need for an integrated One Health approach to understand the levels of human- and livestock-related clinical and asymptomatic disease and whether there is a need to implement interventions such as vaccination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".