COVID-19 Disease in Under-5 Children: Current Status and Strategies for Prevention including Vaccination
Bibliographic record
Abstract
Since the coronavirus disease (COVID-19) pandemic hit the globe in early 2020, we have steadily gained insight into its pathogenesis; thereby improving surveillance and preventive measures. In contrast to other respiratory viruses, neonates and young children infected with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have a milder clinical presentation, with only a small proportion needing hospitalization and intensive care support. With the emergence of novel variants and improved testing services, there has been a higher incidence of COVID-19 disease reported among children and neonates. Despite this, the proportion of young children with severe disease has not increased. Key mechanisms that protect young children from severe COVID-19 disease include the placental barrier, differential expression of angiotensin-converting enzyme 2 (ACE-2) receptors, immature immune response, and passive transfer of antibodies via placenta and human milk. Implementing mass vaccination programs has been a major milestone in reducing the global disease burden. However, considering the lower risk of severe COVID-19 illness in young children and the limited evidence about long-term vaccine safety, the risk-benefit balance in children under five years of age is more complex. In this review, we do not support or undermine vaccination of young children but outline current evidence and guidelines, and highlight controversies, knowledge gaps, and ethical issues related to COVID-19 vaccination in young children. Regulatory bodies should consider the individual and community benefits of vaccinating younger children in their local epidemiological setting while planning regional immunization policies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".