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Estimating Social Network Structure and Propagation Dynamics for an Infectious Disease

  • Louis Kim
  • Mark Abramson
  • Kimon Drakopoulos
  • Stephan Kolitz
  • Asu Ozdaglar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8393)

Abstract

The ability to learn network structure characteristics and disease dynamic parameters improves the predictive power of epidemic models, the understanding of disease propagation processes and the development of efficient curing and vaccination policies. This paper presents a parameter estimation method that learns network characteristics and disease dynamics from our estimated infection curve. We apply the method to data collected during the 2009 H1N1 epidemic and show that the best-fit model, among a family of graphs, admits a scale-free network. This finding implies that random vaccination alone will not efficiently halt the spread of influenza, and instead vaccination and contact-reduction programs should exploit the special network structure.

Keywords

network topology disease dynamics parameter estimation 

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References

  1. 1.
    Newman, M.E.J.: The spread of epidemic disease on networks. Phys. Rev. E 66, 16128 (2002)CrossRefGoogle Scholar
  2. 2.
    Moore, C., Newman, M.E.J.: Epidemics and percolation in small-world networks. Phys. Rev. E 61, 5678–5682 (2000)CrossRefGoogle Scholar
  3. 3.
    Anderson, R.M., May, R.M.: Infectious diseases of humans. Oxford University Press, Oxford (1991)Google Scholar
  4. 4.
    Larson, R.C., Teytelman, A.: Modeling the effects of H1N1 influenza vaccine distribution in the United States. Value in Health 15, 158–166 (2012)CrossRefGoogle Scholar
  5. 5.
    Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86, 3200–3203 (2001)CrossRefGoogle Scholar
  6. 6.
    Kuperman, M., Abramson, G.: Small world effect in an epidemiological model. Phys. Rev. Lett. 86, 2909–2912 (2001)CrossRefGoogle Scholar
  7. 7.
    Keeling, M.J., Eames, K.T.D.: Networks and epidemic models. J. R. Soc. Interface 2, 295–307 (2005)CrossRefGoogle Scholar
  8. 8.
    Eubank, S.: Network based models of infectious disease spread. Jpn. J. Infect. Dis. 58, S9–S13 (2005)Google Scholar
  9. 9.
    Liljeros, F., Edling, C.R., Åmaral, L.A.N., Stanley, H.E., Aberg, Y.: The web of human sexual contacts. Nature 411, 907–908 (2001)CrossRefGoogle Scholar
  10. 10.
    Salathé, M., Kazandjieva, M., Lee, J.W., Levis, P., Feldman, M.W., Jones, J.H.: A high-resolution human contact network for infectious disease transmission. Proc. Natl Acad. Sci. USA 107, 22020–22025 (2010)CrossRefGoogle Scholar
  11. 11.
    Dong, W., Heller, K., Pentland, A.(S.): Modeling Infection with Multi-agent Dynamics. In: Yang, S.J., Greenberg, A.M., Endsley, M. (eds.) SBP 2012. LNCS, vol. 7227, pp. 172–179. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Newman, M.E.J., Forrest, S., Balthrop, J.: Email networks and the spread of computer viruses. Phys. Rev. E 66, 035101 (2002)CrossRefGoogle Scholar
  13. 13.
    Kermack, W., McKendrick, A.: A contribution to the mathematical theory of epidemics. Proc. R. Soc. A 115, 700–721 (1927)CrossRefzbMATHGoogle Scholar
  14. 14.
    Seasonal influenza (flu). Center for Disease Control and Prevention (2010), http://www.cdc.gov/flu/weekly/
  15. 15.
    CDC estimates of 2009 H1N1 influenza cases, hospitalizations and deaths in the United States, April 2009 – March 13, 2010. Center for Disease Control and Prevention (2010), http://www.cdc.gov/h1n1flu/estimates/April_March_13.htm
  16. 16.
    Weekly influenza update, May 27, 2010. Massachusetts Department of Public Health (2010), http://ma-publichealth.typepad.com/files/weekly_report_05_27_10.pdf
  17. 17.
    Table and graph of 2009 H1N1 influenza vaccine doses allocated, ordered, and shipped by project area. Center for Disease Control and Prevention (2010), http://www.cdc.gov/h1n1flu/vaccination/vaccinesupply.htm
  18. 18.
    Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. Comput. Commun. Rev. 29, 251–262 (1999)CrossRefGoogle Scholar
  19. 19.
    de Solla Price, D.J.: Networks of scientific papers. Science 149, 510–515 (1965)CrossRefGoogle Scholar
  20. 20.
    Newman, M.E.J.: Networks: an introduction. Oxford University Press (2010)Google Scholar
  21. 21.
    Albert, R., Jeong, H., Barabasi, A.-L.: Error and attach tolerance of complex network. Nature 406, 378–382 (2000)CrossRefGoogle Scholar
  22. 22.
    Pastor-Satorras, R., Vespignani, A.: Immunization of complex networks. Phys. Rev. E 65, 036104 (2002)CrossRefGoogle Scholar
  23. 23.
    Dezsõ, Z., Barabási, A.-L.: Halting viruses in scale-free networks. Phys. Rev. E 65, 055103(R) (2002)CrossRefGoogle Scholar
  24. 24.
    Barrett, C.L., Beckman, R.J., Khan, M., Kumar, V.A., Marathe, M.V., Stretz, P.E., Dutta, T., Lewis, B.: Generation and analysis of large synthetic social contact networks. In: Rossetti, M.D., Hill, R.R., Johansson, B., Dunkin, A., Ingalls, R.G. (eds.) Proceedings of the 2009 Winter Simulation Conference. IEEE Press, New York (2009)Google Scholar
  25. 25.
    Bisset, K., Chen, J., Feng, X., Kumar, V.A., Marathe, M.: EpiFast: a fast algorithm for large scale realistic epidemic simulations on distributed memory systems. In: Proceedings of the 23rd International Conference on Supercomputing (ICS), pp. 430–439 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Louis Kim
    • 1
    • 2
  • Mark Abramson
    • 1
  • Kimon Drakopoulos
    • 2
  • Stephan Kolitz
    • 1
  • Asu Ozdaglar
    • 2
  1. 1.Draper LaboratoryCambridgeUSA
  2. 2.Massachusetts Institute of TechnologyCambridgeUSA

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