Considerations for vaccinating children against COVID-19
Bibliographic record
Abstract
COVID-19 vaccines have been introduced in children and adolescents in many countries. However, high levels of community transmission and infection-derived immunity make the decision to introduce COVID-19 vaccination of children in countries yet to do so particularly challenging. For example, other vaccine preventable diseases, including measles and polio, generally have far higher childhood morbidity and mortality in low-income and middle-income countries (LMICs) than COVID-19, and coverage with these vaccines has declined during the pandemic. Many countries are yet to introduce pneumococcal conjugate and rotavirus vaccines for children, which prevent common causes of childhood death, or human papillomavirus vaccine for adolescents. The Pfizer and Moderna COVID-19 vaccines that have been widely tested in children and adolescents have a positive risk-benefit profile. However, the benefit is less compared with other life-saving vaccines in this age group, particularly in LMICs and settings with widespread infection-derived immunity. The resources required for rollout may also pose a considerable challenge in LMICs. In this paper, we describe COVID-19 in children, with a focus on LMICs, and summarise the published literature on safety, efficacy and effectiveness of COVID-19 vaccination in children and adolescents. We highlight the complexity of decision-making regarding COVID-19 vaccination of children now that most of this low-risk population benefit from infection-derived immunity. We emphasise that at-risk groups should be prioritised for COVID-19 vaccination; and that if COVID-19 vaccines are introduced for children, the opportunity should be taken to improve coverage of routine childhood vaccines and preventative healthcare. Additionally, we highlight the paucity of epidemiological data in LMICs, and that for future epidemics, measures need to be taken to ensure equitable access to safe and efficacious vaccines before exposure to 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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".