COVID-19 surveillance in the Flemish school system: development of systematic data collection within the Public Health School System and descriptive analysis of cases reported between October 2020 and June 2021
Bibliographic record
Abstract
Abstract Background The age specific distribution of SARS-CoV-2 cases in schools is not well described. The numbers recorded reflect the intensity of community transmission while being shaped by biases from age-dependent testing regimes and effective age-specific interventions. A case-surveillance system was introduced within the Flemish school and health-prevention network during the 2020-2021 school year. We present epidemiological data of in-school reported cases in pre-, primary and secondary schools based on the surveillance system, in conjunction with test data and community cases from October 2020 to June 2021. Methods We describe the development of the surveillance system and provide the number of reported cases and standardized rates per grade over time. We calculate absolute and relative differences between incidence cases by grade of primary (grades 1-6) and secondary-school (grades 7-12) and compare these to grades 7-8, relating them to non-pharmaceutical infection prevention interventions. Cumulative population incidences (IP) stratified by age, province and social-economic status (SES) of the school population are presented with their 95% confidence intervals (CI). Results A total of 59,996 COVID-19 cases were reported in the school surveillance system, with the highest population adjusted IP in grade 11-12 of 7.39% (95%CI 7.24-7.53) and ranging from 2.23–6.25% from pre-school through grade 10. Age-specific reduction in in-person teaching and introduction of masks, are temporally associated with decreases in incident cases by grades. Lower pupil SES is associated with increased cumulative cases (excess 2,739/100,000 pupils compared to highest SES tertile). Community testing volumes varied more for children compared to adults, with overall higher child test-positivity. Holidays influence capturing of cases by the system, however efficiency increased to above 75% after further automation and integration in existing structures. Conclusion Integration of case surveillance within an electronic school health system is feasible, provides data to follow up the epidemic evolution in schoolchildren and should be part of public health surveillance and pandemic preparedness. The relationship towards community transmission needs careful evaluation because of age-different testing regimens. In the Flemish region, case incidence within schools follows an age gradient that is mitigated through grade specific interventions, while differences by SES remain.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".