Bayesian inference for partially observed stochastic epidemics

被引：231

作者：

O'Neill, PD

Roberts, GO

机构：

[1] Univ Bradford, Bradford BD7 1DP, W Yorkshire, England

[2] Univ Cambridge, Cambridge CB2 1TN, England

来源：

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY | 1999年 / 162卷

关键词：

Bayesian inference; epidemic; general stochastic epidemic; Gibbs sampler; Hastings algorithm; Markov chain Monte Carlo methods; Reed-Frost epidemic;

D O I：

10.1111/1467-985X.00125

中图分类号：

O1 [数学]; C [社会科学总论];

学科分类号：

03 ; 0303 ; 0701 ; 070101 ;

摘要：

The analysis of infectious disease data is usually complicated by the fact that real life epidemics are only partially observed. In particular, data concerning the process of infection are seldom available. Consequently, standard statistical techniques can become too complicated to implement effectively. In this paper Markov chain Monte Carlo methods are used to make inferences about the missing data as well as the unknown parameters of interest in a Bayesian framework. The methods are applied to real life data from disease outbreaks.

引用

页码：121 / 129

页数：9