Meeting report: CEPI workshop on Rift Valley fever epidemiology and modeling to inform human vaccine development, Nairobi, 4–5 June 2024
Bibliographic record
Abstract
Rift Valley fever (RVF) is a zoonotic viral disease that causes epidemics and epizootics among humans and livestock, resulting in substantial health and socioeconomic consequences. Currently, there are no RVF vaccines licensed for humans, but several candidates show promise in early-stage development. Existing gaps in RVF epidemiological data and challenges associated with predicting RVF outbreak risk complicate the planning of efficacy studies, making the pathway to licensure for promising candidates unclear. In June 2024, the Coalition for Epidemic Preparedness Innovations (CEPI) convened a two-day workshop in Nairobi, Kenya, to discuss RVF epidemiology, modeling priorities, and specific gaps relevant to human RVF vaccine development. The workshop included representatives from multiple RVF-endemic countries, key global collaborators, and international health organizations. Workshop participants identified five key priorities: (1) Looking beyond outbreaks: There is a need to better characterize the complex One Health epidemiology of RVF and understand interepidemic persistence of the virus; (2) Better data for better models: Epidemiological modeling is crucial for research, prediction, and planning, but it requires accurate and representative data; (3) New, improved and accessible diagnostics and serological assays: These are needed to inform epidemiology and case definitions, without which RVF research will continue to suffer due to paucity of data and challenges in determining infection and exposure; (4) Defining use cases, regulatory pathways, and implementation strategies for human vaccines: Clarity on these topics will facilitate licensure and effective use of RVF vaccines; and (5) People-centered approaches: Community engagement and involvement of social and behavioral scientists are key to the success of human vaccine research and development and implementation, particularly as the virus impacts livestock and livelihoods. Workshop participants welcomed a renewed focus for RVF epidemiology and modeling, and expressed enthusiasm for continued multidisciplinary collaborations to support enabling sciences for human RVF vaccine research and development.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.054 | 0.015 |
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".