Abstract
The present work mainly focuses on the issue of risk model in spatial data analysis. Through the analysis of morbidity data of influenza A (H1N1) across China's administrative regions from 2009 to 2012, a comparative study was carried out among Poisson model, Poisson-Gamma model, log-normal model, EB estimator of moment and Bayesian hierarchical model. By using R programming language, the feasibility of the above analysis methods was verified and the variability of the estimated values generated by each model was calculated, the Bayesian model for spatial disease analysis was improved, and estimator considering uncorrelated spatial model, correlated spatial model and covariate factors was proved to be the best by comparing DIC values of the models. By using the Markov chain for simulative iteration, iterative convergence was illustrated by graphs of iteration track, autocorrelation function, kernel density and quantile estimation. The research on spatial variability of disease morbidity is helpful in detecting epidemic areas and forewarning the pathophoresis of prospective epidemic disease.
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