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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".