Approximate Bayesian computation for spatial SEIR(S) epidemic models.
Approximate Bayesian computation for spatial SEIR(S) epidemic models.
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DOI：
10.1016/j.sste.2017.11.001
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发表时间：
2018-03
影响因子：
3.4
通讯作者：
Hinman JA
中科院分区：
文献类型：
--
作者：
Brown GD;Porter AT;Oleson JJ;Hinman JA
近似贝叶斯计算（ABC）为复杂贝叶斯推理问题的估计提供了一种有吸引力的方法，对于这些问题，估计后验分布的核是不可能的或计算成本很高。这些高度并行化的技术已经成功地应用于许多领域，特别是在传统方法如马尔可夫链蒙特卡罗（MCMC）不切实际的情况下。在这项工作中，我们展示了近似贝叶斯推理在空间异质易感-暴露-感染-去除（SEIR）随机流行病模型中的应用。这些模型具有可处理的后验分布，但是MCMC技术对于中等规模的问题在计算上是不可行的。我们通过开源ABSEIR软件包讨论了这些技术的实际实施。在模拟和2014年基孔肯雅热在美洲流行的空间异质性背景下，探讨了ABC相对于传统MCMC方法在小问题中的性能。
Approximate Bayesian Computation (ABC) provides an attractive approach to estimation in complex Bayesian inferential problems for which evaluation of the kernel of the posterior distribution is impossible or computationally expensive. These highly parallelizable techniques have been successfully applied to many fields, particularly in cases where more traditional approaches such as Markov chain Monte Carlo (MCMC) are impractical. In this work, we demonstrate the application of approximate Bayesian inference to spatially heterogeneous Susceptible-Exposed-Infectious-Removed (SEIR) stochastic epidemic models. These models have a tractable posterior distribution, however MCMC techniques nevertheless become computationally infeasible for moderately sized problems. We discuss the practical implementation of these techniques via the open source ABSEIR package for R. The performance of ABC relative to traditional MCMC methods in a small problem is explored under simulation, as well as in the spatially heterogeneous context of the 2014 epidemic of Chikungunya in the Americas.