Network memory in the movement of hospital patients carrying antimicrobial-resistant bacteria

Abstract

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.

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study

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