Optimal Observation Times in Experimental Epidemic Processes
Optimal Observation Times in Experimental Epidemic Processes
复制标题
实验流行过程中的最佳观察时间
作者：
A. Cook;G. Gibson;C. Gilligan
本文描述了一种选择随机过程的观测时间以最大化其参数的预期信息的方法。考虑了流行病过程的两个常用模型：简单死亡过程和易感-感染（SI）流行病过程，该过程具有在人群内和从人群外传播的双重传染源.最优设计的搜索使用贝叶斯计算方法来探索联合参数-数据-设计空间，结合称为矩闭合的方法来近似可能性，以使验收步骤有效。对于所考虑的过程，少量的最佳选择的观察结果显示，产生几乎一样多的信息，更密集的观察计划，通常用于流行病学实验。对简单死亡过程的分析允许将完整的贝叶斯方法与基于渐进结果的先验点估计周围的局部最优设计进行比较。的鲁棒性的方法错误的先验证明了SI流行病的过程中，计算的棘手性的可能性排除局部最优设计。我们发现，贝叶斯方法得出的最优设计是类似的观察性研究的一个单一的流行病和研究涉及重复的流行病在独立的亚群。然而，当目标是最大化基于信息和非信息先验的信息增益时，不同的最优结果：当实验旨在说服天真或怀疑的观察者，而不是巩固知情观察者的信念时，这会产生影响。一些扩展的方法，包括选择的信息标准和扩展到其他传染病过程的转移概率，简要讨论。
Summary This article describes a method for choosing observation times for stochastic processes to maximise the expected information about their parameters. Two commonly used models for epidemiological processes are considered: a simple death process and a susceptible‐infected (SI) epidemic process with dual sources for infection spreading within and from outwith the population. The search for the optimal design uses Bayesian computational methods to explore the joint parameter‐data‐design space, combined with a method known as moment closure to approximate the likelihood to make the acceptance step efficient. For the processes considered, a small number of optimally chosen observations are shown to yield almost as much information as much more intensively observed schemes that are commonly used in epidemiological experiments. Analysis of the simple death process allows a comparison between the full Bayesian approach and locally optimal designs around a point estimate from the prior based on asymptotic results. The robustness of the approach to misspecified priors is demonstrated for the SI epidemic process, for which the computational intractability of the likelihood precludes locally optimal designs. We show that optimal designs derived by the Bayesian approach are similar for observational studies of a single epidemic and for studies involving replicated epidemics in independent subpopulations. Different optima result, however, when the objective is to maximise the gain in information based on informative and non‐informative priors: this has implications when an experiment is designed to convince a naïve or sceptical observer rather than consolidate the belief of an informed observer. Some extensions to the methods, including the selection of information criteria and extension to other epidemic processes with transition probabilities, are briefly addressed.