Control of Epidemics on Hospital Networks

Part of the Understanding Complex Systems book series (UCS)


The spread of hospital-related infections such as antibiotic-resistant pathogens forms a major challenge in public healthcare systems world-wide. One of the driving mechanisms of the pathogen spread are referrals or transfers of patients (hosts) between hospitals or readmissions after their stay in the community, constituting a dynamical network of hospitals. We analyze referral patterns of 1 million patients from one Federal State in Germany over the period of three years. We extract the underlying statistics of relocation patterns and build an agent-based computational model of pathogen spread. We simulate an outbreak of an SIS-type infection (susceptible-infected-susceptible) and evaluate characteristic time scales and prevalence levels. For such recurrent diseases, we finally investigate the effect of control measures based on screening and isolation of incoming patients.


Prevalence Level Hospital Size Hospital Network Incoming Patient Pathogen Spread 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



All the authors acknowledge the courtesy of the AOK Niedersachsen for providing the anonymized data on patient referrals. VB thanks André Karch and Johannes Horn for fruitful discussions. VB and PH acknowledge funding by the Deutsche Forschungsgemeinschaft in the framework of Collaborative Research Center 910. At the early stage of this study VB was financially supported by the fellowship “Computational sciences” of the VolkswagenStiftung.


  1. 1.
    World Health Organization, Antimicrobial Resistance: Global Report on Surveillance. WHO, Geneva (2014)Google Scholar
  2. 2.
    T. Donker, J. Wallinga, R. Slack, H. Grundmann, PLoS ONE 7(4), 1 (2012). doi: 10.1371/journal.pone.0035002 CrossRefGoogle Scholar
  3. 3.
    J.F. Gracia, J.P. Onnela, M.L. Barnett, V.M. Eguíluz, N. Christakis, arXiv preprint arXiv:1504.08343 (2015)
  4. 4.
    M. Ciccolini et al., Int. J. Med. Microbiol. 303(6–7), 380 (2013). doi: 10.1016/j.ijmm.2013.02.003 CrossRefGoogle Scholar
  5. 5.
    P. Holme, J. Saramäki, Phys. Rep. 519, 97 (2012). doi: 10.1016/j.physrep.2012.03.001 ADSCrossRefGoogle Scholar
  6. 6.
    A. Casteigts, P. Flocchini, W. Quattrociocchi, N. Santoro, Int. J. Parallel Emergent Distrib. Syst. 27(5), 387 (2012). doi: 10.1080/17445760.2012.668546 Google Scholar
  7. 7.
    V.D. Blondel, J.L. Guillaume, R. Lambiotte, E. Lefebvre, J. Stat. Mech. 10, P10008 (2008)CrossRefGoogle Scholar
  8. 8.
    N. Masuda, New J. Phys. 11(12), 123018 (2009)ADSMathSciNetCrossRefGoogle Scholar
  9. 9.
    T.H. Cormen, Introduction to Algorithms. MIT Press (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institut für Theoretische PhysikTechnische Universität BerlinBerlinGermany
  2. 2.Helmholtz Centre for Infection ResearchBraunschweigGermany
  3. 3.Bernstein Center For Computational Neuroscience BerlinBerlinGermany

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