GloPID-R report on chikungunya, o'nyong-nyong and Mayaro virus, part 2: Epidemiological distribution of o'nyong-nyong virus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it