| 甘肃省新型冠状病毒肺炎疫情时间风险的空间特征分析 |
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| 引用本文： | 郑芸鹤,刘海霞,苟发香,张宏,杨筱婷,田彦军,魏孔福,成瑶,蒋小娟,刘新凤. 甘肃省新型冠状病毒肺炎疫情时间风险的空间特征分析[J]. 疾病监测, 2020, 35(11): 982-986. DOI: 10.3784/j.issn.1003-9961.2020.11.006 |
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| 作者姓名： | 郑芸鹤 刘海霞 苟发香 张宏 杨筱婷 田彦军 魏孔福 成瑶 蒋小娟 刘新凤 |
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| 作者单位： | 甘肃省疾病预防控制中心，甘肃 兰州 730000 |
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| 摘 要： | ![]()
目的描述并探讨甘肃省新型冠状病毒肺炎(COVID-19)疫情的时间风险特征。方法收集截至2020年2月16日甘肃省各县(区)报告的COVID-19的发病数、相应县(区)人口数等数据,运用空间统计学的方法计算各县(区)的时间风险指数频率指数(α)、持续时间频率(β)和强度指数(γ)。结果全局自相关分析发现,α的Moran’s I系数为0.115,提示未来COVID-19发病频率增加概率较小。γ的Moran’s I系数为0.070,说明COVID-19确诊病例不会集中出现,疫情的发病强度较低。甘肃省COVID-19疫情的α、β存在全局空间自相关性(P<0.05),γ不存在全局空间自相关性(P>0.05)。α局部空间自相关分析发现区域(主要分布于兰州市城关区、七里河区、安宁区、红古区、皋兰县和榆中县)内存在高-高聚集性(P<0.05)。β局部空间自相关分析发现区域(主要分布于兰州市城关区、七里河区、西固区、安宁区、皋兰县和榆中县)内存在高-高聚集性(P<0.05)。结论结果提示兰州市大部分县(区)呈现高风险聚集性,但发病强度较低;全省在疫情期间采取的各项防控措施及时、有效。
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| 关 键 词： | 新型冠状病毒肺炎 时间风险特征 空间自相关分析 |
| 收稿时间： | 2020-08-18 |
Temporal risk of coronavirus disease 2019 in Gansu province |
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| Affiliation: | Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, Gansu, China |
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| Abstract: | ![]()
Objective To describe the temporal risk characteristics of coronavirus disease 2019 (COVID-19) in Gansu province. Methods The information about confirmed COVID-19 cases reported in Gansu as of February 16, 2020 and population data of local counties (districts) were collected to calculate temporal risk of frequency index (α), duration frequency (β) and intensity index (γ) of COVID-19 in Gansu with spatial statistic. Results The Moran's I coefficient of α of COVID-19 in Gansu was 0.115 by global autocorrelation analysis, suggesting that the probability of incidence frequency increase was low. The Moran's I coefficient of γ of COVID-19 in Gansu was 0.070, which showed that the confirmed cases would have no clustering and the epidemic intensity was low. Frequency index (α) and duration frequency (β) had global spatial autocorrelation (P<0.05), while intensity index (γ) had no global spatial autocorrelation (P>0.05). Frequency index (α) showed that there was high-high clustering in local spatial autocorrelation analysis (mainly in Chengguan, Qilihe, Anning, Honggu districts, and Gaolan and Yuzhong counties). There was high-high clustering in Chengguan, Qilihe, Xigu, Anning districts, and Gaolan and Yuzhong countries indicated by duration frequency (β) in local spatial autocorrelation analysis. Conclusion The majority of counties in Lanzhou showed high risk of clustering of COVID-19, but the intensity was low. The prevention and control measures were timely and effective during COVID-19 epidemic period. |
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