张人杰, 李娜, 王笑笑, 刘碧瑶, 李傅冬, 张双凤, 张雪海, 王臻. 2013-2016年浙江省人感染H7N9禽流感疫情的空间分析及回归模型构建[J]. 疾病监测, 2019, 34(1): 15-20. DOI: 10.3784/j.issn.1003-9961.2019.01.006
引用本文: 张人杰, 李娜, 王笑笑, 刘碧瑶, 李傅冬, 张双凤, 张雪海, 王臻. 2013-2016年浙江省人感染H7N9禽流感疫情的空间分析及回归模型构建[J]. 疾病监测, 2019, 34(1): 15-20. DOI: 10.3784/j.issn.1003-9961.2019.01.006
Renjie Zhang, Na Li, Xiaoxiao Wang, Biyao Liu, Fudong Li, Shuangfeng Zhang, Xuehai Zhang, Zhen Wang. Spatial analysis and mathematical modeling of human infection with avian influenza A (H7N9) virus in Zhejiang, 2013−2016[J]. Disease Surveillance, 2019, 34(1): 15-20. DOI: 10.3784/j.issn.1003-9961.2019.01.006
Citation: Renjie Zhang, Na Li, Xiaoxiao Wang, Biyao Liu, Fudong Li, Shuangfeng Zhang, Xuehai Zhang, Zhen Wang. Spatial analysis and mathematical modeling of human infection with avian influenza A (H7N9) virus in Zhejiang, 2013−2016[J]. Disease Surveillance, 2019, 34(1): 15-20. DOI: 10.3784/j.issn.1003-9961.2019.01.006

2013-2016年浙江省人感染H7N9禽流感疫情的空间分析及回归模型构建

Spatial analysis and mathematical modeling of human infection with avian influenza A (H7N9) virus in Zhejiang, 2013−2016

  • 摘要:
    目的 掌握浙江省H7N9禽流感疫情的空间分布特征及分布模式,构建疾病暴发与社会、环境因素之间关系的空间回归模型,为H7N9防控和预警提供方法学参考。
    方法 以浙江省2013 — 2016年人感染H7N9禽流感病例以及病毒外环境监测数据为基础,结合相关的社会、环境数据,运用空间自相关分析(Global Moran’s I)和热点分析(Getis-Ord Gi*)等方法分析疫情的空间分布模式,运用地理加权回归模型(GWR)分析禽流感病例数与人口密度、家禽密度以及外环境病毒分布的关系,并与传统的最小二乘法(OLS)模型进行比较。
    结果 2013 — 2016年浙江省共报告人感染H7N9禽流感252例,外环境监测发现阳性监测点846个,分布于11个地市的77个县(区)。 省级层面疫情呈现以浙北地区为中心逐步向周边地区扩散的趋势,市级层面呈现以人口密集的城区为中心逐步向偏远县(区)扩散的趋势。 空间回归分析显示县(区)的病例数与人口密度、外环境监测病毒阳性点数呈显著正相关( t=4.127、2.697,P=0.000、0.009)。 地理加权回归模型(R2=0.504)预测效果优于OLS回归模型(R2=0.257)。
    结论 浙江省人感染H7N9禽流感疫情起源于湖州地区,并以杭嘉湖一带为中心向周边逐步扩散,目前已广泛分部于全省各地,活禽贸易可能是疫情传播与扩散的主要途径。 病例的分布存在显著的时空聚集性,人口、家禽密度大、病毒外环境监测阳性水平高的地区更容易出现疫情。 GWR对人感染H7N9禽流感病例的分布具有较好的拟合效果,具有实际应用前景。

     

    Abstract:
    Objective To investigate the spatial distribution of the cases of human infection with influenza A (H7N9) virus in Zhejiang province during 2013−2016, establish mathematic models to identify the environmental and social factors associated with disease distribution and provide methodological support for the prevention and control of the disease.
    Methods Based on the incidence data of human infection with H7N9 virus, virus surveillance data of environmental samples as well as population and socioeconomic data in Zhejiang during this period, a spatial database was established. Global Moran’s I and Getis-Ord Gi* were applied to explore the spatial distribution of H7N9 virus infection epidemic. Geographically Weighted Regression (GWR) model was constructed to analyze the spatial correlation between the epidemic and population density, poultry density and virus distribution in environment. The results were compared with those of Ordinary Least Squares (OLS) model.
    Results From 2013 to 2016, a total of 252 human cases of H7N9 virus infection were reported and the viruses were detected in 846 surveillance sites distributed in 77 counties and districts of 11 prefecture-level cities, . At provincial level, the epidemic spread from central-northern part of Zhejiang to surrounding areas; at city level, the epidemic spread from downtown area with high population density to surrounding counties or districts. Spatial regression analysis suggested that the number of human infection cases was correlated with population density and number of H7N9 virus positive surveillance sites (t=4.127 and 2.697 respectively, P=0.000 and 0.009 respectively). GWR model (R2=0.504) showed better fitness compared with OLS model (R2=0.257).
    Conclusion The H7N9 virus infection epidemic in Zhejiang originated in Huzhou and gradually spread to the surrounding areas of Hangjiahu plain. The virus is distributed wildly in the province now. Live poultry marketing might be the main route for the spread of the infection. The spatiotemporal clustering of human infection with H7N9 virus was very obvious, the epidemic was more likely to occur in area with high population and poultry densities and more virus positive surveillance sites. GWR model is suitable for practical application due to its good predicting effect for the distribution of human infections with H7N9 virus.

     

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