Pediatric household transmission of SARS-CoV-2 infection
Bibliographic record
Abstract
Abstract BACKGROUND As a result of low numbers of pediatric cases early in the COVID-19 pandemic, pediatric household transmission of SARS-CoV-2 remains an understudied topic. This study sought to determine whether there are differences in the odds of household transmission for younger children compared to older children. METHODS We assembled a cohort of all individuals in Ontario, Canada with laboratory-confirmed SARS-CoV-2 infection between June 1 and December 31, 2020. The cohort was restricted to individuals residing in private households (N=132,232 cases in 89,191 households), identified through an address matching algorithm. Analysis focused on households in which the index case was aged <18 years. Logistic regression models were fit to estimate the association between age group of pediatric index cases (0-3, 4-8, 9-13, and 14-17 years) and odds of household transmission. RESULTS A total of 6,280 households had pediatric index cases, and 1,717 (27.3%) experienced secondary transmission. Children aged 0-3 years had the highest odds of household transmission compared to children aged 14-17 years (model adjusted for gender, month of disease onset, testing delay, and average family size: 1.43, 95% CI: 1.17-1.75). This association was similarly observed in sensitivity analyses defining secondary cases as 2-14 days or 4-14 days after the index case, and stratified analyses by presence of symptoms, association with a school/childcare outbreak, or school/childcare reopening. Children aged 4-8 years and 9-13 years also had increased odds of transmission (4-8: 1.40, 95% CI: 1.18-1.67; 9-13: 1.13, 95% CI: 0.97-1.32). CONCLUSIONS This study suggests that younger children are more likely to transmit SARS-CoV-2 infection compared to older children, and the highest odds of transmission was observed for children aged 0-3 years. Differential infectivity of pediatric age groups has implications for infection prevention controls within households, as well as schools/childcare, to minimize risk of household secondary transmission.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".