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Additional file 1: Fig. S1. State networks of $${\mathcal {M}}_1$$ M 1 and $${\mathcal {M}}_2$$ M 2 . Fig. S2. PageRank difference between $${\mathcal {M}}_1$$ M 1 and $${\mathcal {M}}_2$$ M 2 over specialities and buildings. Fig. S3. Optimisation of clustering rate for state lumping. Fig. S4. Markov Stability run statistics. Fig. S5. Markov stability community partitions. Fig. S6. Variation of Information between hospital structures in community partitions. Fig. S7. 2-Way community partition to hospital site. Fig. S8. Hospital wards overlapping communities across Markov stability partitions. Figs. S9, S10. Multiscale centrality model comparison. Fig. S11. Distribution of node statistics. Table S1. Cross validation ranking significance. Tables S2–S4. Examining the overlap between Markov stability community partitions and known hospital structures.

Additional file 1: Table S1. Details of positive microbiology cultures by site of sampling and patient outcome. Numbers (n) denote patients who had a positive microbiology result for that particular organism during hospital admission

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., Comment: 16 pages. 8 main figures. Submitted to the Applied Network Science Special Issue on Epidemic Dynamics and Control on Networks