Transmission of B.1.617.2 Delta variant between vaccinated healthcare workers
Bibliographic record
Abstract
Breakthrough infections with SARS-CoV-2 Delta variant have been reported in doubly-vaccinated recipients and as re-infections. Studies of viral spread within hospital settings have highlighted the potential for transmission between doubly-vaccinated patients and health care workers and have highlighted the benefits of high-grade respiratory protection for health care workers. However the extent to which vaccination is preventative of viral spread in health care settings is less well studied. Here, we analysed data from 118 vaccinated health care workers (HCW) across two hospitals in India, constructing two probable transmission networks involving six HCWs in Hospital A and eight HCWs in Hospital B from epidemiological and virus genome sequence data, using a suite of computational approaches. A maximum likelihood reconstruction of transmission involving known cases of infection suggests a high probability that doubly vaccinated HCWs transmitted SARS-CoV-2 between each other and highlights potential cases of virus transmission between individuals who had received two doses of vaccine. Our findings show firstly that vaccination may reduce rates of transmission, supporting the need for ongoing infection control measures even in highly vaccinated populations, and secondly we have described a novel approach to identifying transmissions that is scalable and rapid, without the need for an infection control infrastructure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".