Impact of the COVID-19 Pandemic on the Reported Incidence of Select Bacterial Enteric Diseases in Canada, 2020
Bibliographic record
Abstract
The aim of this study was to describe the impact of the COVID-19 pandemic on reported cases and clusters of select enteric diseases in Canada, for the period of March 2020 to December 2020. Weekly counts of laboratory confirmed cases of Salmonella , Shigella , Shiga toxin–producing Escherichia coli (STEC), and Listeria monocytogenes were obtained from laboratory surveillance data. These data were supplemented with epidemiological information on the suspected source of illness, collected for cases identified within whole genome sequencing clusters. Incidence rate ratios were calculated for each pathogen. All data were compared with a prepandemic reference period. Decreases in the number of reported cases in 2020 compared with the previous 5-year period were noted for Salmonella , Shigella , Escherichia coli O157, and non-O157 STEC. Reported number of cases for L. monocytogenes in 2020 remained similar to those of the previous 5-year period. There was a considerable decline (59.9%) in the number of cases associated with international travel compared with a 10% decline in the number of domestic cases. Comparison of reported incidence rates of clustered versus sporadic cases for each pathogen showed little variation. This study represents the first formal assessment of the impact of COVID-19 on reported enteric diseases in Canada. Reported case counts across several pathogens saw notable declines in 2020 compared with prepandemic levels, with restrictions on international travel playing a key role. Additional research is needed to understand how limitations on social gatherings, lock downs, and other public health measures have impacted enteric diseases.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".