Rift Valley Fever outbreaks in the East African Community: insights from ProMed data (2010–2024)
Bibliographic record
Abstract
Background: Rift Valley Fever (RVF) is a mosquito-borne zoonotic disease that poses a serious threat to both humans and livestock across various regions, particularly in Africa, the Arabian Peninsula, and parts of the Indian Ocean Islands. This study seeks to analyze the spatial and temporal distribution and trends of RVF outbreaks within the East African Community (EAC) countries, offering insights into the patterns and progression of these outbreaks in the region. Methods: We conducted a retrospective analysis of the Program for Monitoring Emerging Diseases (ProMed), a digital, event-based disease surveillance system, to identify reports of outbreak events in Uganda, Kenya, Rwanda, Burundi, Tanzania, and South Sudan from 2010 to 2024. Outbreak events were systematically tabulated by year, and each record was reviewed to assess RVF outbreak characteristics, locations, trends, and spatial-temporal distribution over the past 14 years. Results: Between 2010 and 2024, 67 RVF outbreaks were documented across Uganda, Rwanda, Kenya, Tanzania, Burundi, and South Sudan, impacting both animal and human populations with confirmed cases and fatalities. Key interventions to contain these outbreaks included restricting animal movement, vaccination campaigns, disease awareness initiatives, enhanced surveillance, contact tracing, isolation, and treatment. Reporting of these outbreaks varied across regions, with a notable monthly increase in cases during May and June and the highest annual incidence observed in 2018. Conclusion: The recurrent and widespread outbreaks of Rift Valley Fever across East Africa highlight an urgent need for increased investment in research, surveillance, prevention, and control efforts to combat this disease.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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".