A two-component model for counts of infectious diseases
A two-component model for counts of infectious diseases
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DOI：
10.1093/biostatistics/kxj016
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发表时间：
2006-07-01
期刊：
影响因子：
2.1
通讯作者：
Schmid, Volker
中科院分区：
文献类型：
--
作者：
Held, Leonhard;Hofmann, Mathias;Schmid, Volker
我们提出了一个随机模型的时间序列的疾病计数的分析，收集在典型的监测系统对法定传染病。该模型是基于泊松或负二项观测模型，有两个组成部分：一个参数驱动的组件与疾病发病率的潜在参数描述流行性季节性模式，这是典型的传染病监测数据。一个观察驱动或流行病的组成部分建模与自回归的病例数在以前的时间点。允许自回归参数根据具有未知数量的变点的贝叶斯变点模型随时间变化。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参数估计值。我们说明了我们的方法，通过分析模拟数据和真实的通知数据从德国传染病监测系统，由罗伯特科赫研究所在柏林。可以从http://www.statistik.lmu.de/similar到mhofmann/twins获得拟合该模型的软件。
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/similar to mhofmann/twins.