刘牧文, 杨旭辉, 王婧, 宋姝娟, 孙昼, 考庆君. 2019-2020年浙江省杭州市流感病例时空聚集性分析[J]. 疾病监测, 2021, 36(4): 376-380. DOI: 10.3784/jbjc.202012130415
引用本文: 刘牧文, 杨旭辉, 王婧, 宋姝娟, 孙昼, 考庆君. 2019-2020年浙江省杭州市流感病例时空聚集性分析[J]. 疾病监测, 2021, 36(4): 376-380. DOI: 10.3784/jbjc.202012130415
Liu Muwen, Yang Xuhui, Wang Jing, Song Shujuan, Sun Zhou, Kao Qingjun. Spatiotemporal analysis on influenza in Hangzhou, Zhejiang, 2019–2020[J]. Disease Surveillance, 2021, 36(4): 376-380. DOI: 10.3784/jbjc.202012130415
Citation: Liu Muwen, Yang Xuhui, Wang Jing, Song Shujuan, Sun Zhou, Kao Qingjun. Spatiotemporal analysis on influenza in Hangzhou, Zhejiang, 2019–2020[J]. Disease Surveillance, 2021, 36(4): 376-380. DOI: 10.3784/jbjc.202012130415

2019-2020年浙江省杭州市流感病例时空聚集性分析

Spatiotemporal analysis on influenza in Hangzhou, Zhejiang, 2019–2020

  • 摘要:
      目的  探讨浙江省杭州市流行性感冒(流感)疫情的时空分布规律,为优化流感防控策略提供参考。
      方法  2019 — 2020年杭州市的流感发病情况来源于“中国疾病预防控制信息系统”,流感聚集性疫情数据来源于杭州市、区疾病预防控制中心疫情处置报告。 采用ArcGIS 10.2软件进行全局/局部空间自相关分析,用SaTScan软件进行时空聚集性扫描分析。
      结果  2019年1月1日至2020年9月30日杭州市共报告流感病例264 410例,发病率为269.98/万,发病高峰为2019年12月至2020年1月,期间病例数占总发病数的66.54%。 全局空间自相关分析显示,2019年第1季度至2020年第1季度的GSA Moran’s I值为正值(P<0.001)。 局部空间自相关分析共探测到71个街道为高–高聚集区,以杭州市主城区及临近地区为主。 时空扫描分析显示Ⅰ类聚集区主要位于主城区及周边临近地区,聚集时间为2019年12月15日至2020年1月25日,相对危险度(RR)值为20.86,对数似然比(LLR)为142 431.21,P<0.001。 局部空间自相关分析和时空扫描分析所得的热点地区与实际监测到的流感聚集性疫情分布情况相近。
      结论  杭州市流感发病存在时空聚集性,12月至次年1月为防控重点时段,主城区及临近地区为防控重点区域。

     

    Abstract:
      Objective  To analyze the temporal and spatial distribution of influenza epidemics in Hangzhou, and provide evidence for influenza prevention and control.
      Methods  The influenza incidence data in Hangzhou during 2019–2020 were collected from “China Disease Prevention and Control Information System”, and the influenza outbreak data in Hangzhou during this period were derived from the epidemic response reports of Centers for Disease Control and Prevention in Hangzhou. Software ArcGIS 10.2 was used for global/local spatial autocorrelation analysis, and software SaTScan was used for spatiotemporal epidemiological analysis.
      Results  A total of 264 410 influenza cases were reported in Hangzhou from January 1, 2019 to September 30, 2020, with an incidence rate of 269.98 per 10 000. The incidence peak was during December 2019 – January 2020 with cases accounting for 66.54% of the total. GSA Moran’s I value from the first quarter of 2019 to the first quarter of 2020 was positive and statistically significant (P<0.001). A total of 71 areas with high-high clustering were detected by local spatial association analysis, mainly located in the urban area and surrounding areas of Hangzhou. The spatiotemporal analysis showed that the class Ⅰ clustering areas were mainly located in the urban area and the surrounding areas of Hangzhou during December 15 2019 – January 25 2020, and the relative risk (RR) was 20.86, the log likelihood ratio (LLR) was 142 431.21 (P<0.001). The clustering areas detected by local spatial autocorrelation analysis and spatiotemporal analysis were similar to the actual outbreak distribution.
      Conclusion  Temporal and spatial clustering of influenza incidence existed in Hangzhou during 2019–2020. Influenza prevention and control in the main urban areas and surrounding areas should be strengthened from November to January of the following year.

     

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