The analysis of hospital infection data using hidden Markov models
The analysis of hospital infection data using hidden Markov models
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DOI：
10.1093/biostatistics/5.2.223
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发表时间：
2004-04-01
期刊：
影响因子：
2.1
通讯作者：
Lipsitch, M
中科院分区：
文献类型：
--
作者：
Cooper, B;Lipsitch, M
传染性医院病原体的监测数据通常由感染患者的低数量计数的短时间序列组成。这些通常表现出过度分散和自相关。到目前为止，几乎所有对这些数据的分析都忽略了生物体的传染性，并使用了仅适用于独立结果的方法。依赖于这种分析的推论不能被认为是可靠的，当病人到病人的传播是重要的。我们提出了一种新的方法来分析这些数据的基础上的流行过程的机械模型。由于重要的医院病原体往往是无症状的，只有一部分患者出现明显的感染，因此常规监测数据通常只能部分观察到流行过程。因此，我们开发了一个“结构化的”隐马尔可夫模型，其中底层的马尔可夫链是由一个简单的transmission model.We应用结构化和标准（非结构化）隐马尔可夫模型的时间序列为三个重要的病原体。我们发现，这两种方法可以提供显着的改善，目前使用的方法时，医院传播是重要的。与标准的隐马尔可夫模型相比，新的方法更简约，更生物合理，并允许关键的流行病学参数进行估计。
Surveillance data for communicable nosocomial pathogens usually consist of short time series of low-numbered counts of infected patients. These often show overdispersion and autocorrelation. To date, almost all analyses of such data have ignored the communicable nature of the organisms and have used methods appropriate only for independent outcomes. Inferences that depend on such analyses cannot be considered reliable when patient-to-patient transmission is important.We propose a new method for analysing these data based on a mechanistic model of the epidemic process. Since important nosocomial pathogens are often carried asymptomatically with overt infection developing in only a proportion of patients, the epidemic process is usually only partially observed by routine surveillance data. We therefore develop a 'structured' hidden Markov model where the underlying Markov chain is generated by a simple transmission model.We apply both structured and standard (unstructured) hidden Markov models to time series for three important pathogens. We find that both methods can offer marked improvements over currently used approaches when nosocomial spread is important. Compared to the standard hidden Markov model, the new approach is more parsimonious, is more biologically plausible, and allows key epidemiological parameters to be estimated.