Paul M, Held L, Toschke A M
Biostatistics Unit, Institute of Social and Preventive Medicine, University of Zurich, Zurich, Switzerland.
Stat Med. 2008 Dec 20;27(29):6250-67. doi: 10.1002/sim.3440.
This paper describes a model-based approach to analyse multivariate time series data on counts of infectious diseases. It extends a method previously described in the literature to deal with possible dependence between disease counts from different pathogens. In a spatio-temporal context it is proposed to include additional information on global dispersal of the pathogen in the model. Two examples are given: the first describes an analysis of weekly influenza and meningococcal disease counts from Germany. The second gives an analysis of the spatio-temporal spread of influenza in the U.S.A., 1996-2006, using air traffic information. Maximum likelihood estimates in this non-standard model class are obtained using general optimization routines, which are integrated in the R package surveillance.
本文描述了一种基于模型的方法，用于分析传染病计数的多元时间序列数据。它扩展了文献中先前描述的一种方法，以处理来自不同病原体的疾病计数之间可能存在的相关性。在时空背景下，建议在模型中纳入有关病原体全球传播的额外信息。给出了两个例子：第一个描述了对德国每周流感和脑膜炎球菌病计数的分析。第二个使用空中交通信息对1996 - 2006年美国流感的时空传播进行了分析。在这个非标准模型类中，最大似然估计是使用通用优化程序获得的，这些程序集成在R包surveillance中。
