陈东真, 张圣洋, 于慧洋, 尹嘉, 李栋, 侯海峰, 邢薇佳, 李晓梅, 丁国永. 山东省2020年1—2月新型冠状病毒肺炎空间聚集性分析[J]. 职业卫生与应急救援, 2020, 38(6): 553-557. DOI: 10.16369/j.oher.issn.1007-1326.2020.06.001
引用本文: 陈东真, 张圣洋, 于慧洋, 尹嘉, 李栋, 侯海峰, 邢薇佳, 李晓梅, 丁国永. 山东省2020年1—2月新型冠状病毒肺炎空间聚集性分析[J]. 职业卫生与应急救援, 2020, 38(6): 553-557. DOI: 10.16369/j.oher.issn.1007-1326.2020.06.001
shu
CHEN Dongzhen, ZHANG Shengyang, YU Huiyang, YIN Jia, LI Dong, HOU Haifeng, XING Weijia, LI Xiaomei, DING Guoyong. Analys is of spatial clustering of coronavirus disease in Shandong Province during January and February of 2020[J]. Occupational Health and Emergency Rescue, 2020, 38(6): 553-557. DOI: 10.16369/j.oher.issn.1007-1326.2020.06.001
Citation: CHEN Dongzhen, ZHANG Shengyang, YU Huiyang, YIN Jia, LI Dong, HOU Haifeng, XING Weijia, LI Xiaomei, DING Guoyong. Analys is of spatial clustering of coronavirus disease in Shandong Province during January and February of 2020[J]. Occupational Health and Emergency Rescue, 2020, 38(6): 553-557. DOI: 10.16369/j.oher.issn.1007-1326.2020.06.001
shu

山东省2020年1—2月新型冠状病毒肺炎空间聚集性分析

Analys is of spatial clustering of coronavirus disease in Shandong Province during January and February of 2020

    摘要
  • 摘要:
    目的 描述山东省新型冠状病毒肺炎（coronavirus disease 2019，COVID-19）的空间热点，探讨其报告病例的聚集性，为制定区域化COVID-19疫情防控策略提供依据。
    方法 采用地理信息系统对山东省各地级市卫生健康委员会COVID-19公告病例数进行管理，采用全局经验贝叶斯平滑、空间经验贝叶斯平滑、全局自相关和局部自相关分析以及空间扫描统计分析方法对数据进行分析。
    结果 山东省2020年1月21日—2020年2月20日共报告COVID-19病例548人，77.19%的病例集中在15~59岁，男性病例占56.02%；报告病例数呈现空间差异性，其中青岛、威海、济南等市的县区报告发病例数较多，超额危险（ER） > 1的县区有45个；全局空间自相关分析未发现COVID-19存在自相关（Moran's I=-0.027，P > 0.05），局部空间自相关分析发现1个热点区域（HH地区），7个冷点区域（LL地区），4个空间异常点（HL或LH地区）；空间扫描分析得到9个有统计学意义的可能聚集区，其中前3个聚集区分别出现在济南市中区（RR=8.65，P < 0.01）、潍坊奎文区（RR=9.02，P < 0.01）和泰安泰山区（RR=6.41，P < 0.01）。
    结论 山东省COVID-19总体分布呈离散性，有1/3县区存在发病超额风险，存在局部聚集性。

     

    Abstract:
    Objective To describe the space hotspots and explore clustering of coronavirus disease (COVID-19) in Shandong Province during the beginning of 2020, which can provide a basis for the development of regional COVID-19 prevention and control strategies.
    Methods Geographical information system(GIS)was used to manage the COVID-19 cases reported by the health commissions of all cities in Shandong Province. The global empirical Bayesian smoothing, spatial empirical Bayes smoothing, spatial autocorrelation and spatial scan statistic were used for further analysis.
    Results Totally 548 cases of COVID-19 were reported in Shandong Province, from January 21 to February 20, 2020;77.19% of cases were between 15 and 59 years old, and 56.02% of them were males. The number of reported cases showed clearly the spatial differences. More cases were reported in the cities in Qingdao, Weihai and Jinan, and there were 45 counties with excess risk (ER) more than 1. The global spatial autocorrelation analyses implied that distribution of COVID-19 was no spatially auto-correlated in Shandong Province (Moran's I=-0.027, P > 0.05). Local autocorrelation analysis found 1 high-risk area(HH area), 7 cold-spot areas(LL area), and 4 spatial abnormal points(HL or LH area). Spatial scanning analysis revealed 9 statistically significant clustering areas, of which the first three clustering areas appeared in Shizhong District of Jinan (RR=8.65, P < 0.01), Kuiwen District of Weifang (RR=9.02, P < 0.01), and Taishan District of Tai'an (RR=6.41, P < 0.01).
    Conclusions The overall distribution of COVID-19 cases in Shandong Province during January 21 to February 20, 2020 was discrete. One third of the counties had the excess risk and local aggregation.

     

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