We consider extensions to previous models for patient level nosocomial infection in several ways, provide a specification of the likelihoods for these new models, specify new update steps required for stochastic integration, and provide programs that implement these methods to obtain parameter estimates and model choice statistics. Previous susceptible-infected models are extended to allow for a latent period between initial exposure to the pathogen and the patient becoming themselves infectious, and the possibility of decolonization. We allow for multiple facilities, such as acute care hospitals or long-term care facilities and nursing homes, and for multiple units or wards within a facility. Patient transfers between units and facilities are tracked and accounted for in the models so that direct importation of a colonized individual from one facility or unit to another might be inferred. We allow for constant transmission rates, rates that depend on the number of colonized individuals in a unit or facility, or rates that depend on the proportion of colonized individuals. Statistical analysis is done in a Bayesian framework using Markov chain Monte Carlo methods to obtain a sample of parameter values from their joint posterior distribution. Cross validation, deviance information criterion and widely applicable information criterion approaches to model choice fit very naturally into this framework and we have implemented all three. We illustrate our methods by considering model selection issues and parameter estimation for data on methicilin-resistant Staphylococcus aureus surveillance tests over 1 year at a Veterans Administration hospital comprising seven wards.

中文翻译：

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医院感染的扩展模型：参数估计和模型选择。

我们考虑以多种方式扩展以前的患者级别医院感染模型，提供这些新模型的可能性规范，指定随机积分所需的新更新步骤，并提供实施这些方法以获得参数估计和模型选择统计数据的程序. 之前的易感感染模型被扩展到允许在最初接触病原体和患者变得具有传染性之间的潜伏期，以及非殖民化的可能性。我们允许多个设施，例如急症护理医院或长期护理设施和疗养院，以及设施内的多个单位或病房。在模型中跟踪和解释单位和设施之间的患者转移，以便可以推断出被殖民的个体从一个设施或单位直接输入到另一个设施或单位。我们允许恒定的传播率、取决于单位或设施中被殖民者数量的传播率，或取决于被殖民者比例的传播率。统计分析是在贝叶斯框架中完成的，使用马尔可夫链蒙特卡罗方法从它们的联合后验分布中获取参数值的样本。交叉验证、偏差信息标准和广泛适用的信息标准方法来模型选择非常自然地适合这个框架，我们已经实现了所有这三个。