Bacterial Zoonoses Transmitted by Household Pets: State-of-the-Art and Future Perspectives for Targeted Research and Policy Actions
Bibliographic record
Abstract
The close contact between household pets and people offers favourable conditions for bacterial transmission. In this article, the aetiology, prevalence, transmission, impact on human health and preventative measures are summarized for selected bacterial zoonoses transmissible by household pets. Six zoonoses representing distinct transmission routes were selected arbitrarily based on the available information on incidence and severity of pet-associated disease caused by zoonotic bacteria: bite infections and cat scratch disease (physical injuries), psittacosis (inhalation), leptospirosis (contact with urine), and campylobacteriosis and salmonellosis (faecal-oral ingestion). Antimicrobial resistance was also included due to the recent emergence of multidrug-resistant bacteria of zoonotic potential in dogs and cats. There is a general lack of data on pathogen prevalence in the relevant pet population and on the incidence of human infections attributable to pets. In order to address these gaps in knowledge, and to minimize the risk of human infection, actions at several levels are recommended, including: (1) coordinated surveillance of zoonotic pathogens and antimicrobial resistance in household pets, (2) studies to estimate the burden of human disease attributable to pets and to identify risk behaviours facilitating transmission, and (3) education of those in charge of pets, animal caretakers, veterinarians and human medical healthcare practitioners on the potential zoonotic risks associated with exposure to pets. Disease-specific recommendations include incentives to undertake research aimed at the development of new diagnostic tests, veterinary-specific antimicrobial products and vaccines, as well as initiatives to promote best practices in veterinary diagnostic laboratories and prudent antimicrobial usage.
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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 | 0.034 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".