Community-based surveillance of zoonotic parasites in a ‘One Health’ world: A systematic review
Bibliographic record
Abstract
The One Health (OH) concept provides an integrated framework for observing and improving health issues involving human, animal, and environmental factors, and has been applied in particular to zoonotic disease problems. We conducted a systematic review of English and Chinese language peer-reviewed and grey literature databases to identify zoonotic endoparasite research utilizing an OH approach in community-based settings. Our review identified 32 articles where specimens collected simultaneously from all three OH domains (people, animals, and the environment) were assessed for endoparasite infection or exposure. Study sites spanned 23 countries, and research teams brought together an average of seven authors from two countries. Surveillance of blood-borne and gastrointestinal protozoa were most frequently reported (19 of 32; 59%), followed by trematodes, nematodes, and cestodes. Laboratory techniques varied greatly between studies, and only 16 identified parasites using Polymerase Chain Reaction (PCR) in all three OH domains. Our review identified important gaps in parasitology research operating under an OH framework. We recommend that investigators working in the realm of zoonotic disease strive to evaluate all three OH domains by integrating modern molecular tools as well as techniques provided by economists and social scientists.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".