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

TL;DR

This study analyzes patient movement in hospitals using network models, revealing significant memory effects that influence infection spread and ward community structures, especially for drug-resistant bacteria.

Contribution

It introduces a memory network approach to capture variable-length patient movement patterns, challenging first-order assumptions and revealing complex ward community hierarchies.

Findings

Memory effects significantly alter node rankings and diffusion dynamics.

A lumped-state memory network effectively captures patient flow patterns.

Ward communities exhibit a quasi-hierarchical structure at multiple levels.

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 drug-resistant bacteria across three large London hospitals. We show that there are substantial memory effects in the movement of hospital patients colonised with drug-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 overlapping communities of wards. We find that these communities of wards display a quasi-hierarchical structure at different levels of granularity which is consistent with different aspects of patient flows related to hospital locations and medical specialties.