刘海霞, 杨筱婷, 张宏, 刘新凤. 2016-2020年甘肃省流行性感冒时间风险特征时空分布[J]. 疾病监测, 2022, 37(1): 62-66. DOI: 10.3784/jbjc.202105260306
引用本文: 刘海霞, 杨筱婷, 张宏, 刘新凤. 2016-2020年甘肃省流行性感冒时间风险特征时空分布[J]. 疾病监测, 2022, 37(1): 62-66. DOI: 10.3784/jbjc.202105260306
Liu Haixia, Yang Xiaoting, Zhang Hong, Liu Xinfeng. Spatiotemporal distribution of influenza temporal risk characteristics in Gansu[J]. Disease Surveillance, 2022, 37(1): 62-66. DOI: 10.3784/jbjc.202105260306
Citation: Liu Haixia, Yang Xiaoting, Zhang Hong, Liu Xinfeng. Spatiotemporal distribution of influenza temporal risk characteristics in Gansu[J]. Disease Surveillance, 2022, 37(1): 62-66. DOI: 10.3784/jbjc.202105260306

2016-2020年甘肃省流行性感冒时间风险特征时空分布

Spatiotemporal distribution of influenza temporal risk characteristics in Gansu

  • 摘要:
      目的  了解2016 — 2020年甘肃省流行性感冒(流感)的时空分布特征,评估防控措施实施效果。
      方法  收集甘肃省87个县(区)每周流感数据进行空间扫描分析,采用空间自相关方法对流感的时间风险特征指数(频率指数、持续时间指数和强度指数)进行空间统计分析。
      结果  共报告病例46043例,年均发病率35.09/10万。 2016 — 2020年甘肃省流感高发病率的可能聚集区域集中在定西市和天水市。 流感的频率指数为0.58,持续时间指数为5.73,强度指数为10.49。 全局自相关显示,流感的频率指数和强度指数呈正向空间自相关,局部自相关显示,频率指数和强度指数的热点区域主要集中在定西市。
      结论  甘肃省流感发病存在时空聚集性,定西市是流感的发病高风险区域,不同发病风险区防控措施效果不同。

     

    Abstract:
      Objective  To understand the spatiotemporal distribution of influenza in Gansu province and evaluate the effects of influenza prevention and control measures.
      Methods  The incidence data of influenza in 87 counties of Gansu from 2016 to 2020 were collected for spatial scanning analysis, and the temporal risk characteristic index (frequency index, duration index and intensity index) of influenza was analyzed by spatial autocorrelation method.
      Results  A total of 46 043 cases of influenza were reported in Gansu from 2016 to 2020, with an average annual incidence rate of 35.09/100 000. The areas with high incidence of influenza in Gansu from 2016 to 2020 might be in Dingxi and Tianshui. The frequency index, duration index and intensity index of influenza were 0.58, 5.73 and 10.49 respectively. The global autocorrelation showed that the frequency index and intensity index had positive spatial autocorrelation, and local autocorrelation showed that the hot spots of frequency index and intensity index were mainly in Dingxi.
      Conclusion  The incidence of influenza in Gansu showed spatiotemporal clustering. Dingxi was a high-risk area of influenza, and the prevention and control measures in different risk areas had different effects.

     

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