Timing and Predictors of Loss of Infectivity among Healthcare Workers with Primary and Recurrent COVID-19: a Prospective Observational Cohort Study
Bibliographic record
Abstract
Abstract Background There is a need to understand the duration of infectivity of primary and recurrent COVID-19 and identify predictors of loss of infectivity. Methods Prospective observational cohort study with serial viral culture, rapid antigen detection test (RADT) and RT-PCR on nasopharyngeal specimens of healthcare workers with COVID-19. The primary outcome was viral culture positivity as indicative of infectivity. Predictors of loss of infectivity were determined using multivariate regression model. The performance of the US CDC criteria (fever resolution, symptom improvement and negative RADT) to predict loss of infectivity was also investigated. Results 121 participants (91 female [79.3%]; average age, 40 years) were enrolled. Most (n=107, 88.4%) had received ≥3 SARS-CoV-2 vaccine doses, and 20 (16.5%) had COVID-19 previously. Viral culture positivity decreased from 71.9% (87/121) on day 5 of infection to 18.2% (22/121) on day 10. Participants with recurrent COVID-19 had a lower likelihood of infectivity than those with primary COVID-19 at each follow-up (day 5 OR, 0.14; p<0.001]; day 7 OR, 0.04; p=0.003]) and were all non-infective by day 10 (p=0.02). Independent predictors of infectivity included prior COVID-19 (adjusted OR [aOR] on day 5, 0.005; p=0.003), a RT-PCR Ct value <23 (aOR on day 5, 22.75; p<0.001), but not symptom improvement or RADT result. The CDC criteria would identify 36% (24/67) of all non-infectious individuals on Day 7. However, 17% (5/29) of those meeting all the criteria had a positive viral culture. Conclusions Infectivity of recurrent COVID-19 is shorter than primary infections. Loss of infectivity algorithms could be optimized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".