SARS-CoV-2 in Nursing Homes: Analysis of Routine Surveillance Data in Four European Countries
Bibliographic record
Abstract
Transmission of SARS-CoV-2 in nursing homes is poorly documented. Using surveillance data of 228 European private nursing homes, we estimated weekly SARS-CoV-2 incidences among 21,467 residents and 14,371 staff members, compared to that in the general population, between August 3, 2020, and February 20, 2021. We studied the outcomes of “episodes of introduction” where one case was first detected and computed attack rates, reproduction ratio (R), and dispersion parameter (k). Out of 502 episodes of SARS-CoV-2 introduction, 77.1% (95%CI, 73.2%-80.6%) led to additional cases. Attack rates were highly variable, ranging from 0.4% to 86.5%. The R was 1.16 (95%CI, 1.11-1.22) with k at 2.5 (95%CI, 0.5-4.5). The timing of viral circulation in nursing homes did not mirror that in the general population (p-values<0.001). We estimated the impact of vaccination in preventing SARS-CoV-2 transmission. Before vaccination’s roll-out, a cumulated 5,579 SARS-CoV-2 infections were documented among residents and 2,321 among staff. Higher staffing ratio and previous natural immunization reduced the probability of an outbreak following introduction. Despite strong preventive measures, transmission likely occurred, regardless of building characteristics. Vaccination started on January 15, 2021, and coverage reached 65.0% among residents, and 42.0% among staff by February 20, 2021. Vaccination yielded a 92% reduction (95%CI, 71%-98%) of outbreak probability, and lowered R to 0.87 (95%CI, 0.69-1.10). In the post-pandemic era, much attention will have to be paid to multi-lateral collaboration, policy making, and prevention plans.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".