The impact of COVID-19 pandemic on influenza surveillance: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Influenza activity was reported to be below the seasonal levels during the Coronavirus disease-2019 (COVID-19) pandemic globally. However, during the severe acute respiratory syndrome corona virus-2 outbreak, the routine real-time surveillance of influenza-like illness and acute respiratory infection was adversely affected due to the changes in priorities, economic constraints, repurposing of hospitals for COVID care, and closure of outpatient services. Methods: A systematic review and meta-analysis were carried out to assess the pooled proportion of symptomatic cases tested for influenza virus before the current pandemic in 2019 and during the pandemic in 2020/21. An electronic search of PubMed/MEDLINE, Scopus, and Google Scholar was carried out for the articles reporting the impact of the COVID-19 pandemic on influenza surveillance among humans using search terms. The study was designed based on Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines and the meta-analysis was performed to synthesize the pooled proportion of patients sampled for influenza with 95% confidence interval (CI). Results: The nine qualified studies from the WHO European region, Canada, Japan, Germany, Italy, Spain, South Africa, and the United States were pooled by random-effects meta-analysis. The overall pooled proportion of symptomatic cases sampled for influenza surveillance before and during the pandemic was 2.38% (95% CI 2.08%-2.67%) and 4.18% (95% CI 3.8%-4.52%), respectively. However, the pooled proportion of samples tested for influenza before the pandemic was 0.69% (95% CI 0.45%-0.92%) and during the pandemic was 0.48% (95% CI 0.28%-0.68%) when studies from Canada were excluded. Conclusion: The meta-analysis concludes that globally there was a decline in influenza surveillance during the COVID-19 pandemic except in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".