Learnings from a rapid spread of COVID-19 in a suburban Canadian hospital
Bibliographic record
Abstract
Background: This study describes a SARS-CoV-2 outbreak which was declared in a community hospital, and discusses the lessons learned that could inform future outbreak prevention and control efforts. Methods: The outbreak took place from September 16, 2020 to October 26, 2020 when COVID-19 incidence in the community was low and the COVID-19 vaccine was not yet available. Epidemiological data, patient clinical information, and whole genome sequencing were utilized for the outbreak investigation and analysis. Results: The index case was a patient whose positive status was unknown to staff and whose symptoms on admission did not fit the screening criteria at the time. A total of 19 patients were linked to the outbreak during the study period, with an attack rate of 29%. All-cause mortality for patient cases was 37%. Whole genome sequencing confirmed genetic relatedness of all patient cases. Conclusion: Vigilance for atypical clinical presentation, strategic patient cohorting, and minimizing movement of positive and exposed patients may help limit transmission. Whole genome sequencing can supplement epidemiological data to inform outbreak investigations.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".