Prevalence of Common Respiratory Viruses in Infants following Coronavirus Disease-19 (COVID-19) Pandemic: A Proportional Meta-Analysis in The European Populations
Bibliographic record
Abstract
Introduction: Viruses remain the predominant etiology of respiratory infections in young children. Due to the COVID-19 pandemic, lifestyle transformations were widely adopted, likely influencing the transmission of respiratory viruses. This meta-analysis therefore aims to investigate the prevalence of respiratory viruses circulating in Europe underlying respiratory infections in infants during the COVID-19 era. Method: Literature searching of publications between 2020 and 2023 was independently performed using PubMed, Embase, CENTRAL, and Scopus databases. The shortlisted studies were that performed virologic testing in infants during COVID-19 era and statistically reported sufficient required data. Paper quality was assessed with the Newcastle Ottawa Scale and the meta-analysis was generated with the R metaprop function. Results: Of a total of 641 studies screened, there were identified 47 potential full-text papers reported virologic testing in infants in Europe. The results highlighted that, out of 7 viruses frequently tested, proportion wise Human Respiratory Syncytial Virus (hRSV), Human Rhinovirus (hRV), and Human Metapneumovirus (hMPV) constituted 0.48 [95%CI: 0.38-0.58], 0.25 [95%CI: 0.16-0.35], and 0.15 [95%CI: 0.06-0.23], respectively, in infants with respiratory infections unrelated to COVID-19. p values were <0.05. Conclusions: The meta-analysis in this study indicated that the three most prevalent respiratory viruses causing respiratory infections in infants tested negative for COVID-19 include hRSV, hRV, and hMPV.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.058 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".