Höhle Michael
Department of Statistics, Ludwig-Maximilians-Universität München, Ludwigstr. 33, 80539 München, Germany.
Biom J. 2009 Dec;51(6):961-78. doi: 10.1002/bimj.200900050.
An extension of the stochastic susceptible-infectious-recovered (SIR) model is proposed in order to accommodate a regression context for modelling infectious disease data. The proposal is based on a multivariate counting process specified by conditional intensities, which contain an additive epidemic component and a multiplicative endemic component. This allows the analysis of endemic infectious diseases by quantifying risk factors for infection by external sources in addition to infective contacts. Inference can be performed by considering the full likelihood of the stochastic process with additional parameter restrictions to ensure non-negative conditional intensities. Simulation from the model can be performed by Ogata's modified thinning algorithm. As an illustrative example, we analyse data provided by the Federal Research Centre for Virus Diseases of Animals, Wusterhausen, Germany, on the incidence of the classical swine fever virus in Germany during 1993-2004.
为了适应传染病数据建模的回归背景，我们提出了一种随机易感-感染-康复（SIR）模型的扩展形式。该提议基于由条件强度指定的多元计数过程，其中包含一个加性流行成分和一个乘性地方病成分。这使得除了感染性接触外，还能通过量化外部感染源的危险因素来分析地方病传染病。可以通过考虑随机过程的完整似然性并附加参数限制以确保条件强度非负来进行推断。该模型的模拟可以通过绪方修改后的稀疏算法来执行。作为一个说明性示例，我们分析了德国武斯特豪森动物病毒病联邦研究中心提供的关于1993 - 2004年德国经典猪瘟病毒发病率的数据。
