Shifts in Influenza and Respiratory Syncytial Virus Infection Patterns in Korea After the COVID-19 Pandemic Resulting From Immunity Debt: Retrospective Observational Study
Bibliographic record
Abstract
Background: Nonpharmaceutical interventions (NPIs) such as mask-wearing and social distancing during the COVID-19 pandemic significantly reduced the transmission of common respiratory viruses, including influenza virus and respiratory syncytial virus (RSV). As NPIs were stopped, concerns emerged about "immunity debt," which suggests that limited natural exposure to pathogens may have increased susceptibility and severity, particularly among young children. However, despite growing attention, the postpandemic impact of NPIs on epidemiologic patterns and shifts in age-specific disease burden remains underexplored. Objective: This study aims to investigate, using national surveillance data, the repercussions of the COVID-19 pandemic on the epidemiology and clinical burden of influenza virus and RSV infections in Korea, with an emphasis on the influence of NPIs on the incidence and clinical severity of these infections, particularly among young children. Methods: We analyzed weekly virologic, outpatient, and inpatient surveillance data on influenza virus and RSV infections from the Korea Disease Control and Prevention Agency from 2017 to 2024, covering the prepandemic, pandemic, and postpandemic periods. Time-series analyses were conducted to examine changes in seasonality and to estimate age-specific incidence and clinical severity of influenza virus and RSV infections before and after the COVID-19 pandemic. Results: In the postpandemic seasons, both RSV and influenza virus infections showed disrupted seasonality with delayed and prolonged epidemics. While the overall burden of both viruses was comparable to that for prepandemic periods, there was a notable shift in the age distribution of severe cases. Among influenza-associated hospital admissions, the proportion of school-aged children (7-18 years) doubled, rising from 14% (1,814/12,660) in 2019/20 to 28% (2,176/7,755) in 2022/23. Hospitalization rates in this age group also increased significantly, from 46.8 to 64.4 per 100,000 among children aged 7-12 years, and from 16.4 to 30.0 per 100,000 among those aged 13-18 years. For RSV infections, the burden shifted most prominently to young children aged 1-6 years, whose share of hospital admissions rose from 48% (5,789/11,969) to 61% (7,316/12,011) over the same period. This age group also experienced the largest rise in RSV-associated hospitalization rates, increasing from 230.8 to 357.5 per 100,000 between the 2019/20 and 2022/23 seasons. Conclusions: The patterns of influenza virus and RSV infections in Korea following the COVID-19 pandemic reveal distinct shifts in timing, severity, and the age groups that were most affected. Postpandemic influenza and RSV activity in Korea showed delayed and prolonged epidemics, with shifts in age-specific disease burden rather than an overall increase. Substantial increases in susceptibility and severity among young children for RSV infections and older children for influenza virus infections suggest lingering immunity gaps from reduced exposures during the pandemic. These effects may be further compounded by declining influenza vaccine uptake among children following the pandemic. Our findings underscore the importance of ongoing surveillance and targeted public health measures to manage respiratory viruses in the postpandemic era.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".