Efficient parameter estimation for models of healthcare-associated pathogen transmission in discrete and continuous time.
Efficient parameter estimation for models of healthcare-associated pathogen transmission in discrete and continuous time.
复制标题
离散和连续时间内医疗保健相关病原体传播模型的有效参数估计。
DOI：
10.1093/imammb/dqt021
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Samore,Matthew
中科院分区：
文献类型：
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作者：
Thomas,Alun;Redd,Andrew;Khader,Karim;Leecaster,Molly;Greene,Tom;Samore,Matthew
我们描述了两种新的马尔可夫链蒙特卡罗方法来计算与医疗保健相关感染有关的参数估计。第一种方法将离散时间、患者水平、医院传输模型构建为贝叶斯网络，并利用该框架与现有程序相比，大大提高了估计的计算效率。第二种方法是连续时间的，具有相同的计算优势。这两种方法都已在作者提供的程序中实现。我们使用这些程序来表明，时间离散化可以导致统计偏差在病原体传播率的低估。我们证明了连续实现与离散实现具有相似的运行时间，具有更好的马尔可夫链混合特性，并消除了潜在的统计偏差。因此，我们建议在有连续时间数据的情况下使用它。
We describe two novel Markov chain Monte Carlo approaches to computing estimates of parameters concerned with healthcare-associated infections. The first approach frames the discrete time, patient level, hospital transmission model as a Bayesian network, and exploits this framework to improve greatly on the computational efficiency of estimation compared with existing programs. The second approach is in continuous time and shares the same computational advantages. Both methods have been implemented in programs that are available from the authors. We use these programs to show that time discretization can lead to statistical bias in the underestimation of the rate of transmission of pathogens. We show that the continuous implementation has similar running time to the discrete implementation, has better Markov chain mixing properties, and eliminates the potential statistical bias. We, therefore, recommend its use when continuous-time data are available.