Impact of Changes in Infection Control Measures on the Dynamics of COVID-19 Infections in Schools and Pre-schools
Bibliographic record
Abstract
Introduction: With the increased emergence of SARS-CoV-2 variants, the impact on schools and preschools remains a matter of debate. To ensure that schools and preschools are kept open safely, the identification of factors influencing the extent of outbreaks is of importance. Aim: To monitor dynamics of COVID-19 infections in schools and preschools and identify factors influencing the extent of outbreaks. Methods: In this prospective observational study we analyzed routine surveillance data of Mecklenburg-Western Pomerania, Germany, from calendar week (CW) 32, 2020 to CW19, 2021 regarding SARS-CoV-2 infection events in schools and preschools considering changes in infection control measures over time. A multivariate linear regression model was fitted to evaluate factors influencing the number of students, teachers and staff tested positive following index cases in schools and preschools. Due to an existing multicollinearity in the common multivariate regression model between the variables “face mask obligation for children” and “face mask obligation for adults”, two further separate regression models were set up (Multivariate Model Adults and Multivariate Model Children). Results: We observed a significant increase in secondary cases in preschools in the first quarter of 2021 (CW8 to CW15, 2021), and simultaneously a decrease in secondary cases in schools. In multivariate regression analysis, the strongest predictor of the extent of the outbreaks was the teacher/ caregiver mask obligation (B = −1.9; 95% CI: −2.9 to −1.0; p < 0.001). Furthermore, adult index cases (adult only or child+adult combinations) increased the likelihood of secondary cases (B = 1.3; 95% CI: 0.9 to 1.8; p < 0.001). The face mask obligation for children also showed a significant reduction in the number of secondary cases (B = −0.6; 95% CI: −0.9 to −0.2; p = 0.004. Conclusion: The present study indicates that outbreak events at schools and preschools are effectively contained by an obligation for adults and children to wear face masks.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".