SARS-CoV-2 shedding dynamics across the respiratory tract, sex and disease severity for adult and pediatric COVID-19
Bibliographic record
Abstract
Background SARS-CoV-2 shedding dynamics influence the risk of transmission and clinical manifestations of COVID-19. Yet, the relationships between SARS-CoV-2 shedding dynamics in the upper (URT) and lower respiratory tract (LRT) and age, sex and COVID-19 severity remain unclear. Methods Using systematic review, we developed a dataset of case characteristics (age, sex and COVID-19 severity) and quantitative respiratory viral loads (rVLs). We then conducted stratified analyses to assess SARS-CoV-2 shedding across disease course, COVID-19 severity, the respiratory tract, sex and age groups (aged 0 to 17 years, 18 to 59 years, and 60 years or older). Results The systematic dataset included 1,266 adults and 136 children with COVID-19. In the URT, adults with severe COVID-19 had higher rVLs at 1 day from symptom onset (DFSO) than adults ( P = 0.005) or children ( P = 0.017) with nonsevere illness. Between 1-10 DFSO, severe adults had comparable rates of SARS-CoV-2 clearance from the URT as nonsevere adults ( P = 0.479) and nonsevere children ( P = 0.863). In the LRT, severe adults showed higher rVLs post-symptom onset than nonsevere adults ( P = 0.006). In the analyzed period (4-10 DFSO), severely affected adults had no significant trend in SARS-CoV-2 clearance from LRT ( P = 0.105), whereas nonsevere adults showed a clear trend ( P < 0.001). After stratifying for disease severity, sex and age (including child vs. adult) were not predictive of the duration of respiratory shedding. The estimated accuracy for using URT shedding as a prognostic indicator for COVID-19 severity was up to 65%, whereas it was up to 81% for LRT shedding. Conclusions High, persistent LRT shedding of SARS-CoV-2 characterized severe COVID-19 in adults. After symptom onset, severe cases tended to have slightly higher URT shedding than their nonsevere counterparts. Disease severity, rather than age or sex, predicted SARS-CoV-2 kinetics. LRT specimens more accurately prognosticate COVID-19 severity than do URT specimens. Funding Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant, NSERC Senior Industrial Research Chair and the Toronto COVID-19 Action Fund.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".