Epidemiological and Spatial Clustering Characteristics of Influenza in Northeast Region of Beijing from 2014 to 2023

Yang Yaming; Zhang Wenzeng

doi:10.3969/j.issn.1001-568X.2025.12.0007

Abstract：OBJECTIVE To analyze the epidemiological and spatiotemporal distribution characteristics of influenza in Northeast region of Beijing from 2014 to 2023,providing insights for targeted prevention and control strategies.METHODS Through descriptive analysis,spatial autocorrelation analysis and spatio-temporal scan analysis,the epidemic characteristics of influenza in Northeast region of Beijing from 2014 to 2023 were explored,and high-risk areas and aggregation patterns were identified.RESULTS From 2014 to 2023,43 995 cases were confirmed in the district,with an average annual incidence rate of 332.277/105.Seasonal peaks typically occurred in winter and spring;the summer peak in 2022 was unusual;and a resurgence in 2023 reached record levels.Spatial analysis showed considerable shifts in high-incidence areas year by year,with cases concentrated in the airport ring belt and northwest central areas in 2019 and extending to Southwest central area in 2023.Incidence was highest among children aged 0～4 years and school-aged youth,with no significant differences between genders(P＞0.05).Spatial autocorrelation analysis indicated clustering from 2015 to 2018,2021,and 2023,with the strongest clustering observed in 2016.Spatiotemporal scanning identified multiple clusters centered around several accumulation area,with a primary cluster in weeks 47-53 of 2023,underscoring these regions as priority areas for winter-spring prevention.CONCLUSION Influenza outbreaks in the district from 2014 to 2023 are most prevalent in winter and spring,with cases concentrated in urban and southwestern regions and highest incidence among infants and children.During and following the COVID-19 pandemic,flu patterns were affected by modified control measures and other reasons.To improve influenza control efforts,future strategies should prioritize vaccination,health education,continuous monitoring,and precision-based interventions to reduce flu transmission risks.