Transition to Whole Genome Sequencing Surveillance: The Impact on National Outbreak Detection and Response for <i>Listeria monocytogenes</i> , <i>Salmonella</i> , Shiga Toxin–Producing <i>Escherichia coli</i> , and <i>Shigella</i> Clusters in Canada, 2015–2021
Bibliographic record
Abstract
Between 2017 and 2019, pulsed-field gel electrophoresis was replaced by whole genome sequencing (WGS) for identifying enteric disease clusters in Canada. The number and characteristics of all clusters of Listeria monocytogenes , Salmonella , Shiga toxin–producing Escherichia coli (STEC), and Shigella spp. between 2015 and 2021 were analyzed. Following the transition to WGS, an increase in the number of Salmonella , STEC, and Shigella clusters was noted, whereas the number of clusters of L. monocytogenes decreased. Unlike previous subtyping methods, WGS provided increased resolution to identify discrete clusters of Salmonella Enteritidis. This led to the identification of a number of outbreaks linked to frozen raw breaded chicken products and ultimately a change in food safety policy to reduce the number of illnesses associated with these products. Other pathogens did not experience a similar increase in the number of outbreaks detected. Although WGS did provide increased confidence in the genetic relatedness of cases and isolates, challenges remained in collecting epidemiological data to link these illnesses to a common source.
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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".