Nosocomial infections have important consequences for patients and hospital staff: they worsen patient outcomes and their management stresses already overburdened health systems. Accurate judgements of whether an infection is nosocomial helps staff make appropriate choices to protect other patients within the hospital. Nosocomiality cannot be properly assessed without considering whether the infected patient came into contact with high risk potential infectors within the hospital. We developed a Bayesian model that integrates epidemiological, contact and pathogen genetic data to determine how likely an infection is to be nosocomial and the probability of given infection candidates being the source of the infection.

翻译：院内感染对患者和医院工作人员具有重要影响：它们会恶化患者预后，其管理负担使本已超负荷的医疗系统雪上加霜。准确判断感染是否为院内获得，有助于医护人员做出恰当决策以保护院内其他患者。若不考虑被感染患者是否与院内高危潜在传染源接触，则无法恰当评估院内获得性。我们开发了一个贝叶斯模型，该模型整合了流行病学、接触史及病原体遗传数据，以确定感染为院内获得的可能性，以及特定疑似传染源作为感染源的概率。