Leveraging Topological and Temporal Structure of Hospital Referral Networks for Epidemic Control

  • Vitaly Belik
  • André Karch
  • Philipp Hövel
  • Rafael Mikolajczyk
Part of the Theoretical Biology book series (THBIO)


Antimicrobial-resistant pathogens constitute a major threat for health care systems worldwide. The hospital-related pathway is a key mechanism of their spread. Contrary to intra-hospital transmission data that requires sophisticated contact tracing technologies, data on inter-hospital transmission is collected on a regular basis. We investigate the dataset of patient referrals between hospitals in a large region of Germany. This dataset contains approximately one million patients over a 3-year period. The dataset is used to build a dynamic network of hospitals where nodes are hospitals and edges represent movements of patients between them. We consider the worst-case scenario of a highly contagious disease corresponding to deterministic infection dynamics. Furthermore, we investigate the impact on epidemic processes of the correction to the temporal network due to home (or community) visits of possibly contagious patients returning to hospitals. Moreover, we implement an extensive stochastic agent-based computational model of epidemics on this network. By leveraging the topological and temporal network structure for epidemic control, we propose intervention schemes able to hinder spread. Our approach can be used to design optimal control strategies for containment of nosocomial diseases in health-care networks.



All the authors acknowledge the courtesy of the AOK Niedersachsen for providing the anonymized data on patient referrals. 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.
    Cassini, A., Plachouras, D., Eckmanns, T., Sin, M.A., Blank, H.P., Ducomble, T., Haller, S., Harder, T., Klingeberg, A., Sixtensson, M., et al.: PLoS Med. 13(10), e1002150 (2016)CrossRefGoogle Scholar
  2. 2.
    O’Neill, J.: The review on antimicrobial resistance. Tackling drug-resistant infections globally: final report and recommendations. [WebCite Cache ID 6jI5znBnd] (2016). Accessed 26 July 2016
  3. 3.
    Keeling, M.J., Danon, L., Vernon, M.C., House, T.A.: Proc. Natl. Acad. Sci. 107(19), 8866 (2010)CrossRefGoogle Scholar
  4. 4.
    Belik, V., Geisel, T., Brockmann, D.: Phys. Rev. X 1(1), 011001 (2011)Google Scholar
  5. 5.
    Rosvall, M., Esquivel, A.V., Lancichinetti, A., West, J.D., Lambiotte, R.: Nat. Commun. 5, 4630 (2014)CrossRefGoogle Scholar
  6. 6.
    Scholtes, I., Wider, N., Pfitzner, R., Garas, A., Tessone, C.J., Schweitzer, F.: Nat. Commun. 5, 5024 (2014)CrossRefGoogle Scholar
  7. 7.
    Holme, P., Saramäki, J.: Phys. Rep. 519(3), 97 (2012)CrossRefGoogle Scholar
  8. 8.
    Casteigts, A., Flocchini, P., Quattrociocchi, W., Santoro, N.: Int. J. Parallel Emergent Distrib. Syst. 27(5), 387 (2012)CrossRefGoogle Scholar
  9. 9.
    Fernandez-Gracia, J., Onnela, J.P., Barnett, M., Eguiluz, V.M., Christakis, N.A.: Spread of pathogens in the patient transfer network of US hospitals. arXiv preprint arXiv:1504.08343 (2015)Google Scholar
  10. 10.
    Donker, T., Wallinga, J., Slack, R., Grundmann, H.: PLoS One 7(4), 1 (2012)CrossRefGoogle Scholar
  11. 11.
    Ohst, J., Liljeros, F., Stenhem, M., Holme, P.: EPJ Data Sci. 3(1), 1 (2014)CrossRefGoogle Scholar
  12. 12.
    Rocha, L.E., Singh, V., Esch, M., Lenaerts, T., Stenhem, M., Liljeros, F., Thorson, A.: arXiv preprint arXiv:1611.06784 (2016)Google Scholar
  13. 13.
    Karkada, U.H., Adamic, L., Kahn, J.M., Iwashyna, T.J.: Intensive Care Med. 37(10), 1633 (2011)CrossRefGoogle Scholar
  14. 14.
    Schneider, C.M., Belik, V., Couronné, T., Smoreda, Z., González, M.C.: J. R. Soc. Interface 10(84), 20130246 (2013)CrossRefGoogle Scholar
  15. 15.
    Kovanen, L., Karsai, M., Kaski, K., Kertész, J., Saramäki, J.: Temporal motifs. In: Temporal Networks, pp. 119–133. Springer, Berlin/Heidelberg (2013)Google Scholar
  16. 16.
    Lentz, H.H.K., Koher, A., Hövel, P., Gethmann, J., Sauter-Louis, C., Selhorst, T., Conraths, F.: PLoS One 11(5), e0155196 (2016)CrossRefGoogle Scholar
  17. 17.
    Wieler, L.H., Ewers, C., Guenther, S., Walther, B., Lübke-Becker, A.: Int. J. Med. Microbiol. 303(6–7), 380 (2013)Google Scholar
  18. 18.
    Belik, V., Hövel, P., Mikolajczyk, R.: Control of epidemics on hospital networks. In: Control of Self-Organizing Nonlinear Systems, pp. 431–440. Springer International Publishing, Cham (2016)Google Scholar
  19. 19.
    Lentz, H., Selhorst, T., Sokolov, I.M.: Phys. Rev. Lett. 110(11), 118701 (2013)CrossRefGoogle Scholar
  20. 20.
    Koher, A., Lentz, H.H.K., Hövel, P., Sokolov, I.: PLoS One 11(4), e0151209 (2016)CrossRefGoogle Scholar
  21. 21.
    Konschake, M., Lentz, H.H.K., Conraths, F.J., Hövel, P., Selhorst, T.: PLoS One 8(2), e55223 (2013)CrossRefGoogle Scholar
  22. 22.
    Marschall, J., Mühlemann, K.: Infect. Control 27(11), 1206 (2006)Google Scholar
  23. 23.
    Génois, M., Vestergaard, C.L., Cattuto, C., Barrat, A.: Nat. Commun. 6, 8860 (2015)CrossRefGoogle Scholar
  24. 24.
    Gillespie, D.T.: J. Phys. Chem. 81(25), 2340 (1977)CrossRefGoogle Scholar
  25. 25.
    Cormen, T.H.: Introduction to Algorithms. MIT Press, Cambridge (2009)MATHGoogle Scholar
  26. 26.
    Liu, S., Perra, N., Karsai, M., Vespignani, A.: Phys. Rev. Lett. 112(11), 118702 (2014)CrossRefGoogle Scholar
  27. 27.
    Belik, V., Fengler, A., Fiebig, F., Lentz, H.H.K., Hövel, P.: arXiv preprint arXiv:1509.04054 (2016)Google Scholar
  28. 28.
    Brockmann, D., Helbing, D.: Science 342(6164), 1337 (2013)CrossRefGoogle Scholar
  29. 29.
    Iannelli, F., Koher, A., Brockmann, D., Hövel, P., Sokolov, I.M.: Phys. Rev. E 97, 012313 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Vitaly Belik
    • 1
  • André Karch
    • 2
  • Philipp Hövel
    • 3
  • Rafael Mikolajczyk
    • 2
  1. 1.System Modeling Group, Institute for Veterinary Epidemiology and BiostatisticsFreie Unversität BerlinBerlinGermany
  2. 2.Helmholtz Center for Infection ResearchBraunschweigGermany
  3. 3.Institut für Theoretische PhysikTechnische Universität Berlin BerlinGermany

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