Inter-epidemic seroprevalence of Rift Valley fever virus and associated risk factors in humans in Eastern Rwanda
Bibliographic record
Abstract
BACKGROUND: Rift Valley fever (RVF) is a mosquito-borne zoonosis that causes periodic and explosive epizootics/epidemics in Africa and the Arabian Peninsula. In Rwanda, RVF virus (RVFV) circulation has resulted into two major outbreaks in 2018 and 2022, both of which involving humans. Information on the magnitude of human exposure to RVFV in the country is scarce. This cross-sectional study aimed to investigate the seroprevalence of RVFV and associated risk factors in humans in the Eastern province of Rwanda, 3 years after the end of the 2018 outbreak. METHODOLOGY: A total of 552 outpatients at health facilities in five districts of the Eastern province were randomly sampled and interviewed between December 2021 and February 2022. Exposure to RVFV was examined by detection of anti-RVFV IgG/IgM antibodies in serum samples using a competitive enzyme linked immunosorbent assay (c-ELISA). Bivariate and multivariate logistic regressions were used to assess the association between risk factors and RVFV seropositivity. RESULTS: The findings revealed an overall seroprevalence of 9.6%. The highest seropositivity, but without significant difference, was observed in Bugesera district (12.9%), followed by Kayonza, (10.8%), Kirehe (8.6%), Rwamagana (7.0%) and Ngoma (6.8%). Odds of seropositivity were significantly higher in people with a history of slaughtering animals (OR 2.26, 95% CI 1.04 - 4.91, p = 0.03), and milking (OR 2.60, 95% CI 1.23 - 5.49, p = 0.012). Sex and age-related differences were not significant. CONCLUSION: This study is a first serological survey of RVFV spillover to humans in the country and shows that rural communities in Rwanda have been exposed to RVFV. These findings suggest the Eastern province of Rwanda as a potential hotspot for RVFV circulation, and emphasize the need for a countrywide One Health-based surveillance, prevention, and control strategy to minimize the effects of RVFV in the country.
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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 | 0.000 |
| 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) | 0.001 | 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".