Child transmission of SARS-CoV-2: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Understanding of the role of children in COVID-19 transmission has significant implications for school and childcare policies, as well as appropriate targeting of vaccine campaigns. The objective of this systematic review was to identify the role of children in SARS-CoV-2 transmission to other children and adults. Methods MEDLINE, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, and Web of Science were electronically searched for articles published before March 31, 2021. Studies of child-to-child and child-to-adult transmission and quantified the incidence of index and resulting secondary attack rates of children and adults in schools, households, and other congregate pediatric settings were identified. All articles describing confirmed transmission of SARS-CoV-2 from a child were included. PRISMA guidelines for data abstraction were followed, with each step conducted by two reviewers. Results 40 of 6110 articles identified met inclusion criteria. Overall, there were 0.8 secondary cases per primary index case, with a secondary attack rate of 8.4% among known contacts. The secondary attack rate was 26.4% among adult contacts versus 5.7% amongst child contacts. The pooled estimate of a contact of a pediatric index case being infected as secondary case was 0.10 (95% CI 0.03-0.25). Conclusions Children transmit COVID-19 at a lower rate to children than to adults. Household adults are at highest risk of transmission from an infected child, more so than adults or children in other settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".