Duration of SARS-CoV-2 shedding: A population-based, Canadian study
Bibliographic record
Abstract
INTRODUCTION: There is an evidence gap regarding the duration of SARS-CoV-2 shedding and of its variability across different care settings and by age, sex, income, and co-morbidities. Such evidence is part of understanding of infectivity and reinfection. We examine direct measures of viral shedding using a linked population-based health administrative dataset. METHODS: Laboratory and sociodemographic databases for Ontario, Canada were linked to identify those testing positive (RT-PCR) between Jan. 15 and April 30, 2020 who underwent subsequent testing by May 31, 2020. To maximise use of available data, we computed two shedding duration estimates defined as the time between initial positive and most recent positive (documented shedding) or second of two negative tests (documented resolution). We also report multivariable results using quantile regression to examine subgroup differences. RESULTS: In Ontario, of the 16,595 who tested positive before April 30, 2020, 6604 had sufficient subsequent testing to allow shedding duration calculation. Documented shedding median duration calculated in 4,889 (29% of 16,595) patients was 19 days (IQR 12-28). Documented resolution median duration calculated in 3,219 (19% of the 16,595) patients was 25 days (IQR 18-34). Long-term care residents had 3-5 day longer shedding durations using both definitions. Shorter documented shedding durations of 2-4 days were observed in those living in higher income neighbourhoods. Shorter documented resolution durations of 2-3 days were observed at the 25th% of the distribution in those aged 20-49. Only 11.5% of those with definitive negative test results reverted to negative status by day 14. CONCLUSIONS: Viral shedding continued well beyond 14 days among this large subset of a population-based group with COVID-19, and longer still for long-term care residents and those living in less affluent neighborhoods. Our findings do not speak to duration of infectivity but are useful for understanding the expected duration of RT-PCR positivity and for identifying reinfection.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".