OCCUPATION AND HEALTH ›› 2026, Vol. 42 ›› Issue (7): 955-960.

• Treatise • Previous Articles     Next Articles

Analysis of epidemiological trend and spatiotemporal distribution characteristics of influenza in Songjiang District of Shanghai City from 2017 to 2023

WU Jialing, WANG Chao, YE Duqiu, LI Meng, LYU Xihong()   

  1. Shanghai Songjiang District Center for Disease Control and Prevention(Shanghai Songjiang District Health Supervision Institute)Shanghai 201620, China
  • Received:2025-07-07 Revised:2025-07-29 Online:2026-04-01 Published:2026-05-14

Abstract:

Objective To analyze the epidemiological and spatiotemporal distribution characteristics of influenza cases in Songjiang District of Shanghai City from 2017 to 2023,and provide a basis for scientific prevention and control of influenza. Methods The data of influenza cases in Songjiang District of Shanghai City from 2017 to 2023 were collected and analyzed. Results From 2017 to 2023,the average annual incidence of influenza in Songjiang District was 10.38 per 100 000,mainly aged 0-<10 years. From 2021 to 2023,the incidence of influenza in Songjiang District showed an increasing trend(APC=456.65,P<0.05). The distribution of influenza in Songjiang District exhibited spatial clustering in 2017,2018 and 2023. The high-high aggregation areas were detected by local autocorrelation analysis and the hot spot areas were detected by hot spot analysis. Moreover,the class Ⅰ and Ⅱ aggregation areas detected by spatiotemporal scanning analysis were mainly concentrated in the central urban area of Songjiang District and its surrounding towns. Conclusion From 2017 to 2023,the incidence of influenza cases in Songjiang District illustrates a upward trend,with spatiotemporal clustering,predominantly in the central urban area and surrounding towns. Targeted prevention and control measures should be strengthened for key populations in key areas before the peak influenza season.

Key words: Influenza, Epidemic characteristics, Joinpoint regression analysis, Trend analysis, Spatial autocorrelation analysis, Spatiotemporal scanning

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