Prevalence of SARS-CoV-2 infection among people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVES: People experiencing homelessness are at increased risk of SARS-CoV-2 infection. This study reports the point prevalence of SARS-CoV-2 infection during testing conducted at sites serving people experiencing homelessness in Toronto during the first wave of the COVID-19 pandemic. We also explored the association between site characteristics and prevalence rates. METHODS: The study included individuals who were staying at shelters, encampments, COVID-19 physical distancing sites, and drop-in and respite sites and completed outreach-based testing for SARS-CoV-2 during the period April 17 to July 31, 2020. We examined test positivity rates over time and compared them to rates in the general population of Toronto. Negative binomial regression was used to examine the relationship between each shelter-level characteristic and SARS-CoV-2 positivity rates. We also compared the rates across 3 time periods (T1: April 17-April 25; T2: April 26-May 23; T3: May 24-June 25). RESULTS: The overall prevalence of SARS-CoV-2 infection was 8.5% (394/4657). Site-specific rates showed great heterogeneity with infection rates ranging from 0% to 70.6%. Compared to T1, positivity rates were 0.21 times lower (95% CI: 0.06-0.75) during T2 and 0.14 times lower (95% CI: 0.04-0.44) during T3. Most cases were detected during outbreak testing (384/394 [97.5%]) rather than active case finding. CONCLUSION: During the first wave of the pandemic, rates of SARS-CoV-2 infection at sites for people experiencing homelessness in Toronto varied significantly over time. The observation of lower rates at certain sites may be attributable to overall time trends, expansion of outreach-based testing to include sites without known outbreaks, and/or individual site characteristics.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".