Association Between Use of a Voluntary Isolation Center and Reduced Household SARS-CoV-2 Transmission: A Matched Cohort Study From Toronto, Canada
Bibliographic record
Abstract
BACKGROUND: Throughout the coronavirus disease 2019 (COVID-19) pandemic, many jurisdictions established isolation centers to help reduce household transmission; however, few real-world studies support their effectiveness. We compared transmission among households where first cases used the Toronto Voluntary Isolation Centre (TVIC) with households that received routine self-isolation guidance, prior to widespread vaccine availability. METHODS: Households with a first severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) case that had symptom onset between September 2020 and March 2021 and that used TVIC were propensity score matched with up to 10 self-isolation households. Follow-up began for TVIC households on the day after check-in or, for matched self-isolation households, the equivalent delay since first-case symptom onset. The outcome, 28-day secondary attack rate, was analyzed using proportional hazards models. RESULTS: A total of 303 TVIC households were matched with 2943 self-isolation households. Median duration from first-case symptom onset to TVIC check-in was 3 days (interdecile range [IDR] = 1-6); median check-out date was 11 days after onset (IDR = 10-13). The attack rate among TVIC households was 5.2% (53/1015) compared with 8.4% (787/9408) among self-isolation households (hazard ratio [HR] = 0.50; 95% confidence interval [CI], .28-.90). Greater reductions were observed when first cases isolated sooner after symptom onset (HR = 0.37; 95% CI, .13-1.04) and in larger (HR = 0.30; 95% CI, .14-.67) and more crowded (HR = 0.34; 95% CI, .15-.77) households. CONCLUSIONS: Use of a voluntary isolation center was associated with a 50% reduction in household SARS-CoV-2 prior to the availability of vaccines. Beyond SARS-CoV-2, voluntary isolation centers may help control resurgences of other communicable infections or future pandemic pathogens, particularly for individuals who experience difficulties isolating.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".