Generating Evidence in the Age of COVID-19: Transmission of SARS-CoV-2 by Children
Bibliographic record
Abstract
Schools worldwide were closed in response to the SARS-CoV-2 pandemic, and a key policy question involves how and when these can be re-opened. Observationally, SARS-CoV-2 seems infrequently transmitted by children, which if true argues for the feasibility of swiftly re-establishing schooling. But uncertainty and debate remains over this question. On April 28th a manuscript was posted by the German virologist Christian Drosten (Jones et al., 2020). The manuscript asserted that viral loads in children were the same as in adults, and the authors concluded that infectiousness is therefore not a function of age. They directly connected this to the policy question of opening schools, warning sharply against doing so. This finding and its interpretation were widely disseminated in international news media and were influential in policy debates. We consider the data, analysis and interpretation of this study, especially the sample, variables measured, statistical analysis conducted, and the interpretation of these results in relation to the underlying policy question. We show that the stated conclusion is not supported, and indeed may be contradicted. Laboratory data from a small, non-representative sample were used to steer public discourse instead of adding to the scientific evidence base on the transmission dynamics of SARS-CoV-2 infection.
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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 | 0.056 | 0.282 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".