The influence of case factors and system factors on the timeliness of testing and contact tracing for COVID-19 in The Netherlands
Bibliographic record
Abstract
Source and contact tracing (SCT) is essential to control the transmission of SARS-CoV-2, and the timeliness of SCT is crucial. As little is known about its real-world effectiveness, we investigated the timeliness of SCT in the Netherlands and its determinants. We used routine COVID-19 SCT data from all individuals who tested positive for SARS-CoV-2 at nine Dutch public health services between 1 June 2020 and 28 February 2021 (N = 384 591). We calculated median time intervals between SCT stages. We used multilevel logistic regression to study associations between case factors and system factors, and total SCT delay (symptom onset to SCT initiation >3 days), patient delay (symptom onset to making test appointment >1 day), and response delay (making test appointment to SCT initiation >2 days). The median total SCT interval time was 3 days (interquartile range 2-5). Older age and being a migrant had higher odds of delay; working in health care or education had lower odds of delay. A higher caseload and a scaled-down SCT had higher odds of delay. For age and country of birth, stronger associations with patient delay, and weaker associations with response delay were found. Although SCT during the COVID-19 pandemic might have had merits in prompting people to isolate or quarantine before the availability of a vaccine, the observed interval times indicate that SCT was not fast enough to have a large effect on interrupting transmission chains. Although promising, the added value of digital SCT tools remains uncertain.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".