Non-exponential tolerance to infection in epidemic systems-modeling, inference, and assessment
Non-exponential tolerance to infection in epidemic systems-modeling, inference, and assessment
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DOI：
10.1093/biostatistics/kxs011
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发表时间：
2012-09-01
期刊：
影响因子：
2.1
通讯作者：
Gibson, Gavin J.
中科院分区：
文献类型：
--
作者：
Streftaris, George;Gibson, Gavin J.
传染病的传播动力学传统上是通过一个时间非均匀的泊松过程来描述的，因此假设在Sellke结构之后疾病耐受水平呈指数分布。在这里，我们重点讨论了在流行病中广泛使用的易感-暴露-感染-去除类模型下使用威布尔个体耐受阈值的泛化。讨论了该方法在口蹄疫实验和天花历史数据中的应用，并给出了仿真结果。推理是使用马尔可夫链蒙特卡罗方法在贝叶斯方法后进行的。进行模型评价，利用基于贝叶斯潜残的性质的方法评估模型的充分性，并利用潜在似然比型检验考虑两个候选模型之间的比较，避免了基于贝叶斯因子的相关方法所遇到的问题。
The transmission dynamics of infectious diseases have been traditionally described through a time-inhomogeneous Poisson process, thus assuming exponentially distributed levels of disease tolerance following the Sellke construction. Here we focus on a generalization using Weibull individual tolerance thresholds under the susceptible-exposed-infectious-removed class of models which is widely employed in epidemics. Applications with experimental foot-and-mouth disease and historical smallpox data are discussed, and simulation results are presented. Inference is carried out using Markov chain Monte Carlo methods following a Bayesian approach. Model evaluation is performed, where the adequacy of the models is assessed using methodology based on the properties of Bayesian latent residuals, and comparison between 2 candidate models is also considered using a latent likelihood ratio-type test that avoids problems encountered with relevant methods based on Bayes factors.