| 郑杨,冯子健,李晓松.贝叶斯时空模型在布鲁氏菌病疫情数据分析中的应用[J].中华流行病学杂志,2011,32(1):68-72 |
| 贝叶斯时空模型在布鲁氏菌病疫情数据分析中的应用 |
| Application of Bayesian spatio-temporal modeling in describing the brucellosis infections |
| 收稿日期:2010-08-06 出版日期:2014-09-10 |
| DOI： |
| 中文关键词: 布鲁氏菌病 贝叶斯统计 时空建模 |
| 英文关键词: Brucellosis Bayesian statistics Spatio-temporal modeling |
| 基金项目:国家卫生行业科研专项经费(200802133) |
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| 摘要点击次数: 4207 |
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| 中文摘要: |
| 以2000-2007年内蒙古地区布鲁氏菌病(布病)疫情数据为例，运用空间统计学和传染病流行病学的相关理论，应用贝叶斯理论框架建立时空模型，分析布病在时间和空间上呈现的格局及其演变，以及与之相关联的协变量及其变化，并与传统分析方法进行比较。结果 显示，拟合协变量的贝叶斯时空模型相对较佳(离差信息准则值最小，为2388.000)。2000-2007年内蒙古自治区101个旗县的布病疫情呈现较强的空间相关性，时空格局存在较明显的共变现象，每年空间相关性不尽相同，空间相关系数后验中位数位于0.968～0.973之间，总体上随时间变化略呈下降趋势。地区类型和牛羊存栏数量与内蒙古布病流行可能有关，且牛羊存栏数量对布病的影响随年份而变化。与传统描述流行病学分析方法比较，贝叶斯方法对布病发病相对危险度的估计更加稳定。 |
| 英文摘要: |
| Based on the number of brucellosis cases reported from the national infectious diseases reporting system in Inner Mongolia from 2000 to 2007, a model was developed. Theories of spatial statistics were used, together with knowledge on infectious disease epidemiology and the frame of Bayesian statistics, before the Bayesian spatio-temporal models were respectively set. The effects of space, time, space-time and the relative covariates were also considered. These models were applied to analyze the brucellosis distribution and time trend in Inner Mongolia during 2000-2007. The results of Bayesian spatio-temporal models was expressed by mapping of the disease and compared to the conventional statistical methods. Results showed that the Bayesian models, under consideration of space-time effect and the relative covariates (deviance information criterion, DIC=2388.000), seemed to be the best way to serve the purpose. The county-level spatial correlation of brucellosis epidemics was positive and quite strong in Inner Mongolia. However, the spatial correlation varied with time and the coefficients ranged from 0.968 to 0.973, having a weakening trend during 2000-2007. Types of region and number of stock (cattle and sheep) might be related to the brucellosis epidemics, and the effect on the number of cattle and sheep changed by year. Compared to conventional statistical methods, Bayesian spatio-temporal modeling could precisely estimate the incidence relative risk and was an important tool to analyze the epidemic distribution patterns of infectious diseases and to estimate the incidence relative risk. |
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