Secondary attack rates from asymptomatic and symptomatic influenza virus shedders in hospitals: Results from the TransFLUas influenza transmission study
Bibliographic record
Abstract
OBJECTIVE: Nosocomial transmission of influenza is a major concern for infection control. We aimed to dissect transmission dynamics of influenza, including asymptomatic transmission events, in acute care. DESIGN: Prospective surveillance study during 2 influenza seasons. SETTING: Tertiary-care hospital. PARTICIPANTS: Volunteer sample of inpatients on medical wards and healthcare workers (HCWs). METHODS: Participants provided daily illness diaries and nasal swabs for influenza A and B detection and whole-genome sequencing for phylogenetic analyses. Contacts between study participants were tracked. Secondary influenza attack rates were calculated based on spatial and temporal proximity and phylogenetic evidence for transmission. RESULTS: In total, 152 HCWs and 542 inpatients were included; 16 HCWs (10.5%) and 19 inpatients (3.5%) tested positive for influenza on 109 study days. Study participants had symptoms of disease on most of the days they tested positive for influenza (83.1% and 91.9% for HCWs and inpatients, respectively). Also, 11(15.5%) of 71 influenza-positive swabs among HCWs and 3 (7.9%) of 38 influenza-positive swabs among inpatients were collected on days without symptoms; 2 (12.5%) of 16 HCWs and 2 (10.5%) of 19 inpatients remained fully asymptomatic. The secondary attack rate was low: we recorded 1 transmission event over 159 contact days (0.6%) that originated from a symptomatic case. No transmission event occurred in 61 monitored days of contacts with asymptomatic influenza-positive individuals. CONCLUSIONS: Influenza in acute care is common, and individuals regularly shed influenza virus without harboring symptoms. Nevertheless, both symptomatic and asymptomatic transmission events proved rare. We suggest that healthcare-associated influenza prevention strategies that are based on preseason vaccination and barrier precautions for symptomatic individuals seem to be effective.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".