Hospitals constitute highly interconnected systems that bring into contact an abundance of infectious pathogens and susceptible individuals, thus making infection outbreaks both common and challenging. In recent years, there has been a sharp incidence of antimicrobial-resistance amongst healthcare-associated infections, a situation now considered endemic in many countries. Here we present network-based analyses of a data set capturing the movement of patients harbouring antibiotic-resistant bacteria across three large London hospitals. We show that there are substantial memory effects in the movement of hospital patients colonised with antibiotic-resistant bacteria. Such memory effects break first-order Markovian transitive assumptions and substantially alter the conclusions from the analysis, specifically on node rankings and the evolution of diffusive processes. We capture variable length memory effects by constructing a lumped-state memory network, which we then use to identify individually import wards and overlapping communities of wards. We find these wards align closely to known hotspots of transmission and commonly followed pathways patients. Our framework provides a means to focus infection control efforts and cohort outbreaks of healthcare-associated infections.

医院构成了高度相互联系的系统，这些系统使大量的传染性病原体和易感人群相互接触，从而使感染爆发既普遍又具有挑战性。近年来，在医疗保健相关的感染中，抗菌素耐药性的发病率急剧上升，在许多国家，这种情况现在被认为是地方性的。在这里，我们对数据集进行基于网络的分析，该数据集捕获了伦敦三所大型医院中携带抗药性细菌的患者的活动。我们表明，在有抗生素抗性细菌定植的医院患者的运动中，存在实质性的记忆效应。这样的记忆效应打破了一阶马尔可夫传递假设，并大大改变了分析得出的结论，特别是关于节点排名和扩散过程的演变。我们通过构建集总状态存储网络来捕获可变长度的存储效果，然后将其用于识别单独导入的病房和病房的重叠社区。我们发现这些病房与已知的传播热点和患者通常遵循的途径密切相关。我们的框架提供了一种重点关注感染控制工作和与医疗保健相关的感染人群爆发的方法。