title: [Application of Bayesian spatio-temporal modeling in describing the brucellosis infections].
abstract: 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.
authors.affiliation: Department of Health Statistics, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Zheng
authors.forename: Zi-jian
authors.initials: ZJ
authors.lastname: Feng
authors.forename: Xiao-song
authors.initials: XS
authors.lastname: Li
mesh_terms: D001499:Bayes Theorem; D002006:Brucellosis / Q000453:epidemiology*; D002681:China / Q000453:epidemiology; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D012306:Risk