Performance of the One Health platform in zoonotic disease surveillance in Guinea
Bibliographic record
Abstract
Introduction: Zoonoses are a major global health threat, especially in low-income countries, due to their prevalence and emergence. Repeated outbreaks emphasize the need for integrated, multisectoral surveillance. While the One Health approach is essential, its implementation faces major barriers. Tools like JEE and OH-EpiCap help assess and improve these systems. This study aims to assess the functioning and effectiveness of regional One Health platforms in Guinea. Methods: A cross-sectional study was conducted across the eight administrative regions of Guinea to evaluate the performance of regional One Health (OH) platforms. Data were collected through structured interviews with 160 stakeholders involved in zoonotic disease surveillance, preparedness, and response. The evaluation focused on several key components: coordination; case recording and disease detection; epidemic preparedness and response; mobilization of material resources; stakeholder training; and financing mechanisms. Regional performance was assessed using the standardized evaluation tool developed by the Africa CDC. A comparative analysis was performed using radar charts to identify performance gaps between regions and to highlight disparities in the implementation of the One Health approach. Results: The overall One Health performance score in Guinea was 41%, indicating a limited level of implementation at the national scale. None of the eight assessed regions reached the 60% performance threshold. Indicator-level analysis revealed significant heterogeneity across regions. Conakry demonstrated strong performance in the domain of legislation (89%), whereas all regions exhibited weak capacities in the mobilization of material resources (9%), highlighting a major cross-cutting challenge. Regional performance scores varied considerably, with particularly low levels observed in Labé, Kindia, and Faranah (33%), underscoring major disparities in the implementation of the One Health framework. Conclusion: This study identified critical gaps in the performance of Guinea's One Health platforms, notably in resource mobilization and regional disparities. Strengthening local capacities, harmonizing practices, and improving multi-sectoral coordination are essential. Using the Africa CDC assessment tool revealed actionable insights to inform policy and investment. These findings emphasize the urgent need to reinforce One Health implementation amid persistent zoonotic threats 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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".