Urban Rats ( <i>Rattus norvegicus</i> ) through a One Health Lens: Social and Ecological Factors Promote Opportunities for Urban Leptospirosis in Rats, Dogs, and People
Bibliographic record
Abstract
Abstract Living in cities creates One Health challenges because urban environments can promote pathogen transmission in wildlife and human-wildlife interactions with commensal species such as rats. In this study, we examined social and ecological processes that lead to an elevated risk of leptospirosis, a fatal rat-associated disease transmitted through Leptospira interrogans bacteria in urine. We examined rat and human factors associated with (1) human exposure to rat urine in the home environment, (2) the presence of rats carrying L. interrogans on the block, and (3) environmental conditions associated with rat infection. We surveyed residents and trapped rats on the same 16 blocks in four neighbourhoods in Chicago. Survey respondents were more likely to observe rat urine in their homes if they had lower incomes. Blocks where rats were carrying L. interrogans had higher rat abundance and respondents with higher incomes, who reported dogs dying from leptospirosis, children playing in yards with rat waste, flooded yards, and gardens with rat burrows. Rats were more likely to be infected with L. interrogans if they were trapped on a block with more accessible garbage and if they were older. Our results highlight that rat presence alone does not determine the risk of close contact with rat-associated pathogens; socio-economics can affect an individual’s ability to exclude animals from living spaces. In addition, improved waste management may help mitigate disease risks for humans, wildlife, and domestic animals. We also discuss opportunities for public education about rat-associated zoonoses and lessons learned about meaningful community engagement in One Health work. Information © The Authors 2024
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".