Human <i>Salmonella</i> enteritidis illness outbreak associated with exposure to live mice in British Columbia, Canada, 2018–2019
Bibliographic record
Abstract
Abstract In February 2019, five individuals with Salmonella Enteritidis infections matching by whole genome sequencing (WGS) were identified in the province of British Columbia (BC), Canada. Individuals with available exposure information all reported unique exposure to reptiles or rodents, prompting an outbreak investigation. We describe how WGS, case interviews, traceback and environmental testing were used to link this outbreak to one supplier of live mice primarily used as feeder rodents for pet snakes. Our investigation suggested that SE infection in the live mice occurred from contact with surfaces at the facility which were contaminated with the bacteria, either by a new stock of mice or by culled chicks routinely brought in and processed at the facility. Timely control measures for biosecurity and hygiene implemented at the supplier contributed to reducing the ongoing risk of illness in humans. This investigation not only contributes to the weight of evidence on the risk that pet or feeder rodents pose, but also highlights how ongoing infections in animal species can result in the spread of Salmonella to humans.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".