GloPID-R report on chikungunya, o'nyong-nyong and Mayaro virus, part 2: Epidemiological distribution of o'nyong-nyong virus
Bibliographic record
Abstract
The GloPID-R (Global Research Collaboration for Infectious Disease Preparedness) chikungunya (CHIKV), o'nyong-nyong (ONNV) and Mayaro virus (MAYV) Working Group has been established to identify gaps of knowledge about the natural history, epidemiology and medical management of infection by these viruses, and to provide adapted recommendations for future investigations. Here, we present a report dedicated to ONNV epidemiological distribution. Two large-scale ONNV outbreaks have been identified in Africa in the last 60 years, interspersed with sporadic serosurveys and case reports of returning travelers. The assessment of the real scale of ONNV circulation in Africa remains a difficult task and surveillance studies are necessary to fill this gap. The identification of ONNV etiology is made complicated by the absence of multiplex tools in co-circulation areas and that of reference standards, as well as the high cross-reactivity with related pathogens observed in serological tests, in particular with CHIKV. This is a specific obstacle for seroprevalence studies, that necessitate an improvement of serological tools to provide robust results. The scarcity of existent genetic data currently limits molecular epidemiology studies. ONNV epidemiology would also benefit from reinforced entomological and environmental surveillance. Finally, the natural history of the disease deserves to be further investigated, with a specific attention paid to long-term complications. Considering our incomplete knowledge on ONNV distribution, GloPID-R CHIKV, ONNV and MAYV experts recommend that a major effort should be done to fill existing gaps.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".