Infectivity and fatality of influenza in pre- and post-COVID-19 pandemic year
Bibliographic record
Abstract
The COVID-19 pandemic and related non-pharmaceutical interventions (NPIs) significantly alter the transmission dynamics of non-SARS-CoV-2 infectious diseases, with respiratory infections such as influenza being disproportionately affected. We aim to compare influenza's epidemiological characteristics between pre-pandemic and post-pandemic periods to inform public health responses. We develop two influenza transmission models incorporating age structure and multi-strain dynamics, featuring time-varying transmission and mortality rates. Using publicly available U.S. data, we calibrate these models to evaluate age- and strain-specific transmission patterns and mortality rates across different pandemic eras. Our analysis reveals that during the final pandemic year, influenza transmission among adults ([Formula: see text] years) initially declined but rebounded to pre-pandemic levels within the first post-pandemic year following NPI relaxation and behavioral normalization, while transmission stability persists in the <18 cohort. All-age influenza mortality rates exhibit a transient elevation during the pandemic's final year before returning to baseline levels pre-pandemic. Furthermore, after the COVID-19 pandemic, the transmission rate of influenza A decreases alongside peaks in new cases, while the transmission of influenza B fluctuates without a decline. Our findings indicate that while the COVID-19 pandemic has induced significant transient modifications in influenza's epidemiological profile, key transmission and mortality characteristics regain pre-pandemic equilibrium within one year following pandemic resolution.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".