从家庭分层资料推断流行病学参数
Inference of epidemiological parameters from household stratified data
摘要：
本文考虑了一类具有两个混合水平的SIR疾病动力学的连续时间Markov链模型。对于这个所谓的随机家庭模型，我们提供了两种方法来推断模型参数-管理家庭内的传播，恢复和家庭间的传播-从数据的一天后，每个人成为传染病和家庭中的每一个感染发生，将可从最初的几百个研究。每种方法都是贝叶斯马尔可夫链蒙特卡罗的一种形式，使我们能够计算所有参数的联合后验分布，从而计算家庭繁殖数量和流行病的早期增长率。第一种方法使用标准的数据增强方法执行精确的贝叶斯推断;第二种方法基于从分支过程导出的似然近似执行近似的贝叶斯推断。这些方法的计算效率和后验进行了比较。分支过程被证明是一个很好的近似，并保持计算效率的数据量的增加。
英文摘要：
We consider a continuous-time Markov chain model of SIR disease dynamics with two levels of mixing. For this so-called stochastic households model, we provide two methods for inferring the model parameters---governing within-household transmission, recovery, and between-household transmission---from data of the day upon which each individual became infectious and the household in which each infection occurred, as would be available from first few hundred studies. Each method is a form of Bayesian Markov Chain Monte Carlo that allows us to calculate a joint posterior distribution for all parameters and hence the household reproduction number and the early growth rate of the epidemic. The first method performs exact Bayesian inference using a standard data-augmentation approach; the second performs approximate Bayesian inference based on a likelihood approximation derived from branching processes. These methods are compared for computational efficiency and posteriors from each are compared. The branching process is shown to be an excellent approximation and remains computationally efficient as the amount of data is increased.
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