Assessing Influenza Activity Variations in the Asian Region During the Pre- and Post-Pandemic Period (2019-2023)
Bibliographic record
Abstract
Background: The year 2021 witnessed a decline in seasonal influenza cases across Southeast Asia and the broader Asian region. However, a sudden surge in influenza cases during 2022-2023 necessitates a comprehensive exploration and analysis to inform future prediction models. Objective: Our study aims to evaluate the disease burden of influenza in Asian countries post-COVID-19, while comparing seasonal variations to the pre-pandemic influenza patterns. Methods: We conducted an extensive analysis of data spanning from January 2017 to September 2023 across ten Asian countries, categorizing them into three WHO regions. Data was sourced from the WHO Flunet system, falling under the purview of the WHO Global Influenza Program. Findings and Conclusion: The advent of COVID-19 significantly disrupted pre-pandemic influenza patterns. Limited data availability in the Asian WHO region led to some countries having unreported influenza seasons. Nevertheless, COVID-19 and influenza exhibited a mutually suppressive relationship during and after the pandemic. Notably, an observable increase in influenza A subtype cases was identified in both the northern and southern hemispheres, particularly in temperate zones. These variations in the influenza trend provide valuable insights for predicting future influenza outbreaks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".