Coyote scat in cities increases risk of human exposure to an emerging zoonotic disease in North America
Bibliographic record
Abstract
Introduction Zoonoses associated with urban wildlife are increasingly concerning for human health and include the recent emergence of alveolar echinococcosis (AE) in North America. AE develops following infection with the tapeworm Echinococcus multilocularis . In Alberta, up to 65% of urban coyotes ( Canis latrans ) are infected with E. multilocularis , and infected scats contain eggs that can be accidentally ingested by people. Our goal was to determine the predictors of infection prevalence and intensity in coyote scats in Edmonton, Canada, and to identify the predictors of coyote scat deposition and content, especially as related to anthropogenic food sources and infrastructure. Methods To study infection prevalence and intensity, volunteers collected 269 scats, which were tested for E. multilocularis using polymerase chain reaction. We compared infection prevalence and shedding intensity by habitat and scat content. To determine predictors of scat presence and content, we used snow tracking to identify 1263 scats. We compared landscape characteristics at scats and available points, and among scats with different contents. We used negative binomial regression to predict scat abundance in city-delineated green spaces. Results 26.0% of tested scats were positive for E. multilocularis ( n = 70), and infection was twice as common as expected near compost and 1.3x more common than expected when scats contained anthropogenic food. Scats were more common than expected near other scats (80% within 1 m of scats, 27% at 11.5 m), buildings (19% at buildings, 16% at 80 m), and the camps of people experiencing homelessness (24% at camps, 20% at 60 m). Scats frequently contained fruit (52.9%), anthropogenic material (36.7%), and birdseed (16.0%), and scats containing anthropogenic material often occurred near human infrastructure, supporting a relationship between anthropogenic attractants and scat accumulation. Discussion These results suggest that abundant food sources and anthropogenic food increase coyote aggregation, increasing both scat abundance and infection rates, which in turn increases risk of exposure to zoonotic parasites for humans. Risk to humans might be reduced by preventing coyote access to anthropogenic and aggregated food sources and educating people who are likely to encounter infected soil or vegetation, including gardeners, park users, and people experiencing homelessness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".