基于层次贝叶斯时空模型的空间多尺度联合分析模型的构建及应用研究  

Study on Establishment and Application of Multiscale Joint Analysis Model Based on Hierarchical Bayesian Spatio-temporal Model

作　　者：张俊辉[1,2] 冯子健 杨超[1] 朱彩蓉[2] 李晓松[2] 马家奇 

机构地区：[1]泸州医学院公共卫生学院流行病与卫生统计学教研室,646000 [2]四川大学华西公共卫生学院卫生统计学教研室 [3]中国疾病预防控制预防中心

出　　处：《中国卫生统计》2013年第2期199-202,共4页Chinese Journal of Health Statistics

基　　金：国家卫生公益性行业科研专项项目(项目编号200802133);教育部科学技术研究重点项目(项目编号109135);国家科技重大专项(项目编号2009ZX10004-201)

摘　　要：目的在地市和区县两个尺度下,构建基于层次贝叶斯时空模型的空间多尺度联合分析模型,并探讨该模型是否优于单独分析模型。方法在地市和区县两个尺度下,根据我国北方6省(包括内蒙古、山西、黑龙江、河北、吉林、辽宁)的布鲁氏菌病发病数据,构建基于层次贝叶斯时空模型的空间多尺度联合分析模型,并与单独分析的结果进行比较。结果全部模型中,联合分析与单独分析比较,离差信息准则略小,说明联合分析略优于单独分析。结论基于层次贝叶斯时空模型的空间多尺度联合分析模型为正确阐明和解释布鲁氏菌病的时空分布特征提供了新的思路和手段,也可为其他传染病甚至慢性病的同类研究提供方法学参考。Objective To establish multiscale joint analysis model based on hierarchical Bayesian spatio-temporal model at the city scale and at the county scale and to explore whether multiscale joint analy- sis （namely joint model ） was superior to multiscale separate analysis （namely separate model,which carried out at different spatial scales sepa- rately）. Methods Based on the brucellosis incidence data collected from the National Notifiable Infectious Disease Reporting System in the 6 contig- uous provinces （ Inner Mongolia, Shanxi, Heilongjiang, Hebei, Jilin and Lia- oning） of north China from 2004 to 2007, multiscale joint analysis models based on hierarchical Bayesian spatio-temporal model at the city scale and at the county scale was established, which were compared with separate models. Results The comparison results between joint models and sepa- rate models showed that： Either at the city scale or at the county scale, ei- ther joint model or separate model, the model considering the space-time in- teraction was the optimal model. Among all models, the deviance informa-fion criterion（DIC） of joint model was slightly smaller than that of separate model, so we concluded that joint model was slightly superior to separate model. Conclusion Multiscale joint analysis based on hierarchical Bayesian spatio-temporal model provide us innovate idea and a useful tool to fully using the spatio-temporal disease data and to correctly clarify or ex- plain the spatial-temporal distribution of disease.

关 键 词：层次贝叶斯时空模型 多尺度联合分析 空间相关性 空间异质性 布鲁氏菌病 

分 类 号：O212.8[理学—概率论与数理统计] R195[理学—数学]