Analyses of infectious disease data from household outbreaks by Markov chain Monte Carlo methods
Analyses of infectious disease data from household outbreaks by Markov chain Monte Carlo methods
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DOI：
10.1111/1467-9876.00210
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发表时间：
2000-01-01
影响因子：
1.6
通讯作者：
Mollison, D
中科院分区：
文献类型：
--
作者：
O'Neill, PD;Balding, DJ;Mollison, D
传染病数据的分析提出了挑战，因为数据的依赖性和只有部分传播过程是可观察到的。这些困难通常可以通过简化假设来克服。本文探讨了使用马尔可夫链蒙特卡罗（MCMC）方法分析传染病数据，希望它们将允许在更现实的假设下进行分析。考虑了两种重要的数据集，其中包含麻疹和流感爆发的时间和非时间信息。随机流行病模型用于描述产生数据的过程。然后使用MCMC方法在贝叶斯上下文中对模型参数执行推理。使用的MCMC方法包括标准算法，如Metropolis-Hastings算法和Gibbs采样器，以及一种涉及似然近似的新方法。发现标准算法在某些情况下表现良好，但在其他情况下可能表现出严重的收敛困难。我们得到的推论与可用的其他方法得到的估计大体一致。然而，我们也可以为之前的分析中没有报道的参数提供推论。
The analysis of infectious disease data presents challenges arising from the dependence in the data and the fact that only part of the transmission process is observable. These difficulties are usually overcome by making simplifying assumptions. The paper explores the use of Markov chain Monte Carlo (MCMC) methods for the analysis of infectious disease data, with the hope that they will permit analyses to be made under more realistic assumptions. Two important kinds of data sets are considered, containing temporal and non-temporal information, from outbreaks of measles and influenza. Stochastic epidemic models are used to describe the processes that generate the data. MCMC methods are then employed to perform inference in a Bayesian context for the model parameters. The MCMC methods used include standard algorithms, such as the Metropolis-Hastings algorithm and the Gibbs sampler, as well as a new method that involves likelihood approximation. It is found that standard algorithms perform well in some situations but can exhibit serious convergence difficulties in others. The inferences that we obtain are in broad agreement with estimates obtained by other methods where they are available. However, we can also provide inferences for parameters which have not been reported in previous analyses.