A one health approach for integrated vector management monitoring and evaluation
Bibliographic record
Abstract
The French Agency for Food, Environmental and Occupational Health & Safety (Anses) has set up a multidisciplinary working group (WG) to develop an innovative One Health approach for the monitoring and evaluation of an integrated vector management system (IVMS) on a territorial scale. Four existing evaluation guidelines and methods have been combined into a semi-quantitative evaluation approach that takes into account all the dimensions of an integrated process. We propose a set of 34 criteria divided into three sections (objectives and management, implementation, integration) that correspond to the main functional components of an IVMS. Each criterion is assigned a score based on the results of a scoring questionnaire completed by the system's stakeholders, and two graphical outputs are generated using a specific combination of these scores. An overview of the system's performance is provided through a series of pie charts synthesizing the scores for each of the three sections and the corresponding eleven subsections. A radar chart further combines the results according to eight attributes chosen to characterize the qualities of the system. Our approach was tested for the invasive mosquito Aedes albopictus, a main vector of arboviruses, in two French territories with contrasting dengue epidemiology. This approach is intended to be generic and usable in all territories that are at risk of being affected by arboviruses, whether in tropical or temperate regions. Beyond a conventional assessment of the various components of an IVMS, our interdisciplinary and multisectoral approach aims to gain a better understanding of such a system in its environment, its overall functioning and its mechanisms for adapting to contextual change. It also aims to identify avenues for improvement as part of a continuous quality process, and to facilitate comparisons between territories and the cross-fertilization of knowledge between stakeholders.
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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 | 0.043 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".