A time-series analysis of testing and COVID-19 outbreaks in Canadian federal prisons to inform prevention and surveillance efforts
Bibliographic record
Abstract
BACKGROUND: Approximately 14,000 adults are currently incarcerated in federal prisons in Canada. These facilities are vulnerable to disease outbreaks and an assessment of coronavirus disease 2019 (COVID-19) testing and outcomes is needed. The objective of this study was to examine outcomes of COVID-19 testing, prevalence, case recovery and death within federal prisons and to contrast these data with those of the general population. METHODS: Public time-series outcome data for prisoners and the general population were obtained on-line from the Correctional Service of Canada and the Public Health Agency of Canada, respectively, from March 30 to May 27, 2020. Prison, province and sex-specific frequency statistics for each outcome were calculated. A total of 50 facilities were included in this study. RESULTS: Of these 50 facilities, 64% reported fewer individuals tested per 1,000 population than observed in the general population and 12% reported zero tests in the study period. Testing tended to be reactive, increasing only once prisons had recorded positive tests. Six prisons reported viral outbreaks, with three recording over 20% cumulative COVID-19 prevalence among prisoners. Cumulatively, in prisons, 29% of individuals tested received a positive result, compared to 6% in the general population. Two of the 360 cases died (0.6% fatality). Four outbreaks appeared to be under control (more than 80% of cases recovered); however, sizeable susceptible populations remain at risk of infection. Female prisoners (5% of the total prisoner population) were over-represented among cases (17% of cases overall). CONCLUSION: Findings suggest that prison environments are vulnerable to widespread severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. Gaps in testing merit public health attention. Symptom-based testing alone may not be optimal in prisons, given observations of widespread transmission. Increased sentinel or universal testing may be appropriate. Increased testing, along with rigorous infection prevention practices and the potential release of prisoners, will be needed to curb future outbreaks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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".