COVID-19 attack ratio among children critically depends on the time to removal and activity levels
Bibliographic record
Abstract
Abstract The attack ratio in a subpopulation is defined as the total number of infections over the total number of individuals in this subpopulation. Using a methodology based on modified age-stratified transmission dynamics model, we estimated the attack ratio of COVID-19 among children (individuals 0-11 years) in Ontario, Canada when a large proportion of individuals eligible for vaccination (age 12 and above) are vaccinated to achieve herd immunity among this subpopulation, or the effective herd immunity with additional physical distancing measures (hence effective herd immunity). We describe the relationship between this attack ratio among children, the time to remove infected individuals from the transmission chain and the children-to-children daily contact rate, while considering the increased transmissibility of virus variants (using the Delta variant as an example). We further illustrate the generality and applicability of the methodology established by performing an analysis of the attack ratio of COVID-19 among children in the Canadian population. The clinical attack ratio, the number of symptomatic infections over the total population can be informed from the attack ratio, and both can be reduced substantially via a combination of higher vaccine coverage in the vaccine eligible population, reduced social mixing among children, and rapid testing and isolation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".