Porter Aaron T, Oleson Jacob J
Department of Statistics, University of Missouri, Columbia, Missouri 65211, USA.
Biometrics. 2013 Mar;69(1):101-8. doi: 10.1111/j.1541-0420.2012.01809.x. Epub 2013 Jan 16.
Most current Bayesian SEIR (Susceptible, Exposed, Infectious, Removed (or Recovered)) models either use exponentially distributed latent and infectious periods, allow for a single distribution on the latent and infectious period, or make strong assumptions regarding the quantity of information available regarding time distributions, particularly the time spent in the exposed compartment. Many infectious diseases require a more realistic assumption on the latent and infectious periods. In this article, we provide an alternative model allowing general distributions to be utilized for both the exposed and infectious compartments, while avoiding the need for full latent time data. The alternative formulation is a path-specific SEIR (PS SEIR) model that follows individual paths through the exposed and infectious compartments, thereby removing the need for an exponential assumption on the latent and infectious time distributions. We show how the PS SEIR model is a stochastic analog to a general class of deterministic SEIR models. We then demonstrate the improvement of this PS SEIR model over more common population averaged models via simulation results and perform a new analysis of the Iowa mumps epidemic from 2006.
当前大多数贝叶斯易感-暴露-感染-康复（SEIR）模型要么使用指数分布的潜伏期和感染期，允许在潜伏期和感染期采用单一分布，要么对时间分布（特别是在暴露阶段所花费的时间）可用的信息量做出强有力的假设。许多传染病需要对潜伏期和感染期做出更现实的假设。在本文中，我们提供了一种替代模型，该模型允许在暴露和感染阶段都使用一般分布，同时避免了对完整潜伏期数据的需求。这种替代公式是一种路径特定的SEIR（PS SEIR）模型，它沿着通过暴露和感染阶段的个体路径，从而消除了对潜伏期和感染期时间分布的指数假设的需求。我们展示了PS SEIR模型如何是一类一般确定性SEIR模型的随机类似物。然后，我们通过模拟结果证明了这种PS SEIR模型相对于更常见的总体平均模型的改进，并对2006年爱荷华州的腮腺炎疫情进行了新的分析。
