Surveillance of foodborne parasitic diseases in Europe in a One Health approach
Bibliographic record
Abstract
In 2012, WHO/FAO ranked 24 foodborne parasites (FBP) using multicriteria decision analysis (MCDA) to provide risk assessors with a basis for prioritising control of highly ranked FBP on the global level. One conclusion was that ranking may differ substantially per region. In Europe, the same methodology was used to rank FBP of relevance for Europe. Of the 24 FBP, the top-five prioritised FBP were identified for Europe as Echinococcus multilocularis, Toxoplasma gondii, Trichinella spiralis, E. granulosus, and Cryptosporidium spp., all of which are zoonotic. The objective of the present study was to provide an overview of surveillance and reporting systems in Europe for these top five prioritised FBP in the human and animal populations, to identify gaps, and give recommendations for improvement. Information on the surveillance systems was collected from 35 European countries and analysed according to the five different regions. For most FBP, human surveillance is passive in most countries and regions in Europe and notification differs between countries and regions. Adequate surveillance programmes for these FBP are lacking, except for T. spiralis, which is notifiable in 34 countries with active surveillance in susceptible animals under EU directive. Although human and animal surveillance data are available for the five prioritised FBP, we identified a lack of consistency in surveillance and reporting requirements between national experts and European bodies. Recommendations for improved surveillance systems are discussed.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".