Held Leonhard, Hofmann Mathias, Höhle Michael, Schmid Volker
Department of Statistics, Ludwig-Maximilians-Universität München, Ludwigstrasse 33, 80539 München, Germany.
Biostatistics. 2006 Jul;7(3):422-37. doi: 10.1093/biostatistics/kxj016. Epub 2006 Jan 11.
We propose a stochastic model for the analysis of time series of disease counts as collected in typical surveillance systems on notifiable infectious diseases. The model is based on a Poisson or negative binomial observation model with two components: a parameter-driven component relates the disease incidence to latent parameters describing endemic seasonal patterns, which are typical for infectious disease surveillance data. An observation-driven or epidemic component is modeled with an autoregression on the number of cases at the previous time points. The autoregressive parameter is allowed to change over time according to a Bayesian changepoint model with unknown number of changepoints. Parameter estimates are obtained through the Bayesian model averaging using Markov chain Monte Carlo techniques. We illustrate our approach through analysis of simulated data and real notification data obtained from the German infectious disease surveillance system, administered by the Robert Koch Institute in Berlin. Software to fit the proposed model can be obtained from http://www.statistik.lmu.de/ approximately mhofmann/twins.
我们提出了一种随机模型，用于分析在法定传染病典型监测系统中收集的疾病计数时间序列。该模型基于泊松或负二项式观测模型，包含两个部分：参数驱动部分将疾病发病率与描述地方流行季节模式的潜在参数相关联，这是传染病监测数据的典型特征。观测驱动或流行部分通过对前一时间点的病例数进行自回归建模。自回归参数允许根据具有未知变化点数量的贝叶斯变化点模型随时间变化。参数估计通过使用马尔可夫链蒙特卡罗技术的贝叶斯模型平均获得。我们通过分析从柏林罗伯特·科赫研究所管理的德国传染病监测系统获得的模拟数据和实际通报数据来说明我们的方法。拟合所提出模型的软件可从http://www.statistik.lmu.de/ approximately mhofmann/twins获得。
