空间零膨胀泊松模型在云南省布鲁氏菌病空间分析中的应用

doi:10.20199/j.issn.1672-2302.2025.03.006

摘要/Abstract

摘要：

目的探讨空间零膨胀泊松模型在零值过多的云南省布鲁氏菌病病例数据空间分析中的应用价值，为公共卫生领域类似数据分析提供参考。方法通过中国疾病预防控制信息系统获取2022年1—12月云南省布鲁氏菌病发病数据。采用全局莫兰指数、局部莫兰指数分析布鲁氏菌病病例的全局、局部空间自相关性；空间动态窗口扫描统计探测空间聚集簇。分别使用传统泊松模型、零膨胀泊松模型和空间零膨胀泊松模型对数据进行拟合，并基于差信息准则（deviance information criterion, DIC）和渡边-赤池信息准则（Watanabe-Akaike information criterion, WAIC）确定最优模型。结果 2022年1—12月云南省共报告布鲁氏菌病1 015例，县级病例数存在全局空间自相关性（Moran’s I=0.40，Z=8.80，P<0.01）。滇东地区疫情较为严重，并存在一个高风险聚集簇（RR=18.53，LLR=694.21，P<0.01）。空间零膨胀泊松模型的拟合效果最佳（DIC=556.055，WAIC=740.752），非空间零膨胀泊松模型其次（DIC=815.527，WAIC=1 564.548），且均优于传统泊松模型（DIC=975.799，WAIC=1 613.696）。其中后两种模型出现结果偏倚，导致参数估计不准确。对于最优模型，其空间结构化随机效应的后验均数具有空间自相关性（Moran’s I=0.32，Z=5.92，P<0.01），而非结构化随机效应则无空间自相关性（Moran’s I=0.08，Z=1.52，P>0.05）。结论融合了空间效应的零膨胀泊松模型能够更好地处理具有空间自相关特征的零膨胀计数数据，揭示疾病数据中的潜在空间结构，为低流行强度传染病的空间流行病学分析提供了有效的方法。

关键词: 零膨胀, 空间模型, 空间流行病学, 布鲁氏菌病, 云南省

Abstract:

Objective To assess the value of spatial zero-inflated Poisson model applied to the spatial analysis of Brucellosis cases with high zero values in Yunnan Province for methodological references in analysis of the similar data in the field of public health. Methods The data on Brucellosis cases reported in Yunnan Province from January to December of 2022 were collected through the Chinese Disease Prevention and Control Information System. The global Moran’s I, local Moran’s I, and spatial scan statistics were used to analyze global and local spatial autocorrelation and detection of spatial clustering of Brucellosis cases. Traditional Poisson, zero-inflated Poisson, and spatial zero-inflated Poisson models were employed for data fitting, with the optimal model determined based on deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC). Results In total, 1 015 cases of Brucellosis were reported in Yunnan Province from January to December of 2022. The number of cases at county-level showed a significant global spatial autocorrelation (Moran’s I=0.40, Z=8.80, P<0.01), and exhibited a serious outbreak in the eastern region of Yunnan, where a high-risk cluster was identified (RR=18.53, LLR=694.21, P<0.01). The spatial zero-inflated Poisson model demonstrated the best fit (DIC=556.055, WAIC=740.752), followed by the non-spatial zero-inflated Poisson model (DIC=815.527, WAIC=1 564.548), both outperforming the traditional Poisson model (DIC=975.799, WAIC=1 613.696). The latter two models showed biased results, leading to inaccurate parameter estimates. The posterior mean of the spatial structure random effects in the optimal model revealed significant spatial autocorrelation (Moran’s I=0.32, Z=5.92, P<0.01), whereas the non-spatial random effects indicated no significant spatial autocorrelation (Moran’s I=0.08, Z=1.52, P>0.05). Conclusion The spatial zero-inflated Poisson model, which incorporates spatial effects, can better address zero-inflated count data with spatial autocorrelation features, and reveal potential spatial structures in disease data, which may provide effective methodological support for the spatial epidemiological analysis of low-prevalence infectious diseases.

Key words: Zero-inflation, Spatial models, Spatial epidemiology, Brucellosis, Yunnan Province

中图分类号:

R181.3，R516.7

袁睿, 李柯, 张乐乐, 于彬彬, 杨向东, 王鹏, 张志杰. 空间零膨胀泊松模型在云南省布鲁氏菌病空间分析中的应用[J]. 热带病与寄生虫学, 2025, 23(3): 160-164,188.

YUAN Rui, LI Ke, ZHANG Lele, YU Binbin, YANG Xiangdong, WANG peng, ZHANG Zhijie. Application of spatial zero-inflated Poisson model in spatial analysis of Brucellosis in Yunnan Province[J]. Journal of Tropical Diseases and Parasitology, 2025, 23(3): 160-164,188.

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参考文献 30

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变量	SZIP模型		ZIP模型		P模型
变量	系数（95%CI）	RR（95%CI）	系数（95%CI）	RR（95%CI）	系数（95%CI）	RR（95%CI）
α（截距）	-1.353 （-2.495~-0.748）	0.274 （0.083~0.471）	0.187 （0.053~0.321）	1.208 （1.055~1.377）	-0.391 （-0.513~-0.268）	0.678 （0.599~0.764）
β₁ （国内生产总值，百亿元）	-0.001 （-0.002~-0.000）	0.999 （0.998~1.000）	-0.249 （-0.270~-0.229）	0.751 （0.731~0.771）	-0.229 （-0.248~-0.209）	0.772 （0.752~0.791）
β₂ （羊存栏量，万头）	0.039 （0.011~0.072）	1.040 （1.011~1.047）	0.026 （0.022~0.031）	1.027 （1.022~1.031）	0.037 （0.033~0.041）	1.038 （1.033~1.042）
β₃ （2021年发病数）	0.016 （0.006~0.026）	1.016 （1.006~1.026）	0.023 （0.022~0.024）	1.023 （1.022~1.024）	0.025 （0.024~0.026）	1.026 （1.024~1.027）
零膨胀参数p_i（95%CI）	0.114（0.088~0.141）		0.357（0.275~0.443）
DIC	556.055		815.527		975.799
WAIC	740.752		1 564.548		1 613.696