Frontiers in public health · 2026
Suppression and resurgence: the evolving epidemiology of seasonal influenza from 2015 to 2024 in a core urban district of Beijing, China.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Gao Xing, Qin Panpan, Qi Xiao, Wan Yanli, Xu Zhiyuan, Hu Hongpu
Аннотация
BACKGROUND: The COVID-19 pandemic significantly disrupted seasonal influenza dynamics. Understanding post-pandemic rebound patterns is crucial for optimizing future public health strategies.
METHODS: We conducted a comprehensive epidemiological analysis of multi-source surveillance data collected from Chaoyang District, Beijing, China, a representative core urban area, spanning 2015 to 2024. Data sources included: Influenza-like illness (ILI) surveillance records, virological results, and reported influenza cases. A segmented interrupted time-series (ITS) framework, utilizing generalized additive mixed models (GAMMs) with negative binomial distribution and AR (1) structure, characterized the disruption and post-pandemic shifts. This robust modeling approach quantified deviations from historical baselines. We also characterized epidemic seasonality, viral strain dominance, and calculated age-stratified rate ratios (RRs).
RESULTS: Analysis of 2,468,817 ILI cases revealed a distinct "increase-suppression-resurgence" pattern. The annual proportion of influenza-like illness (ILI%) exhibited a peak of 3.85% in 2019, subsequently declined to 2.29% during 2020-2022, and then rebounded to 4.42% in 2024. Segmented GAMM-AR(1) modeling demonstrated that pandemic-era ILI% remained consistently below counterfactual projections, succeeded by a substantial post-pandemic rebound (RR = 1.72, p < 0.001). The models also indicated a structural shift from stable historical seasonality toward highly non-linear temporal dynamics (edf = 9.19, p < 001), where typical winter epidemics were replaced by irregular outbreaks, followed by a prolonged 28-week season in 2023-2024. Virological surveillance consistently showed the absence of B/Yamagata lineage detection after 2020. Notably, while children maintained the highest absolute incidence, adults (15-64 years) exhibited the most profound post-pandemic surge in relative risk (RR = 8.43 vs. 2016-2019 baseline), significantly outpacing people over 65 (RR = 3.53) and children (RR = 3.28).
CONCLUSION: The COVID-19 pandemic was associated with a complex shift in influenza epidemiology, characterized by intensified post-pandemic activity, altered seasonality, and a disproportionate redistribution of reported burden toward working-age adults. This demographic shift likely reflects a combination of post-pandemic changes in healthcare-seeking behavior, surveillance sensitivity, and host-level biological factors, with potential contributions from population-level immune waning. The sustained absence of B/Yamagata lineage detections aligns with global evidence of its probable extinction. These findings underscore the importance of age-stratified influenza monitoring and highlight the need for integrated serological, virological, and behavioral studies to elucidate the determinants of post-pandemic influenza burden.
METHODS: We conducted a comprehensive epidemiological analysis of multi-source surveillance data collected from Chaoyang District, Beijing, China, a representative core urban area, spanning 2015 to 2024. Data sources included: Influenza-like illness (ILI) surveillance records, virological results, and reported influenza cases. A segmented interrupted time-series (ITS) framework, utilizing generalized additive mixed models (GAMMs) with negative binomial distribution and AR (1) structure, characterized the disruption and post-pandemic shifts. This robust modeling approach quantified deviations from historical baselines. We also characterized epidemic seasonality, viral strain dominance, and calculated age-stratified rate ratios (RRs).
RESULTS: Analysis of 2,468,817 ILI cases revealed a distinct "increase-suppression-resurgence" pattern. The annual proportion of influenza-like illness (ILI%) exhibited a peak of 3.85% in 2019, subsequently declined to 2.29% during 2020-2022, and then rebounded to 4.42% in 2024. Segmented GAMM-AR(1) modeling demonstrated that pandemic-era ILI% remained consistently below counterfactual projections, succeeded by a substantial post-pandemic rebound (RR = 1.72, p < 0.001). The models also indicated a structural shift from stable historical seasonality toward highly non-linear temporal dynamics (edf = 9.19, p < 001), where typical winter epidemics were replaced by irregular outbreaks, followed by a prolonged 28-week season in 2023-2024. Virological surveillance consistently showed the absence of B/Yamagata lineage detection after 2020. Notably, while children maintained the highest absolute incidence, adults (15-64 years) exhibited the most profound post-pandemic surge in relative risk (RR = 8.43 vs. 2016-2019 baseline), significantly outpacing people over 65 (RR = 3.53) and children (RR = 3.28).
CONCLUSION: The COVID-19 pandemic was associated with a complex shift in influenza epidemiology, characterized by intensified post-pandemic activity, altered seasonality, and a disproportionate redistribution of reported burden toward working-age adults. This demographic shift likely reflects a combination of post-pandemic changes in healthcare-seeking behavior, surveillance sensitivity, and host-level biological factors, with potential contributions from population-level immune waning. The sustained absence of B/Yamagata lineage detections aligns with global evidence of its probable extinction. These findings underscore the importance of age-stratified influenza monitoring and highlight the need for integrated serological, virological, and behavioral studies to elucidate the determinants of post-pandemic influenza burden.