Bayesian inference for partially observed stochastic epidemics
Bayesian inference for partially observed stochastic epidemics
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DOI：
10.1111/1467-985x.00125
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发表时间：
1999-01-01
影响因子：
2
通讯作者：
Roberts, GO
中科院分区：
文献类型：
--
作者：
O'Neill, PD;Roberts, GO
传染病数据的分析通常因为现实生活中的流行病只被部分观察到而变得复杂。特别是，很少有关于感染过程的数据。因此，标准统计技术可能变得过于复杂，无法有效地实现。本文采用马尔可夫链蒙特卡罗方法在贝叶斯框架中对缺失数据和未知感兴趣的参数进行推理。这些方法应用于疾病暴发的真实生活数据。
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.