Skip to main content
View expanded cover

International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction

SBP 2014: Social Computing, Behavioral-Cultural Modeling and Prediction pp 85–93Cite as

Estimating Social Network Structure and Propagation Dynamics for an Infectious Disease

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,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

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-05579-4_11
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   79.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-05579-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.99
Price excludes VAT (USA)
Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Newman, M.E.J.: The spread of epidemic disease on networks. Phys. Rev. E 66, 16128 (2002)

    CrossRef  Google Scholar 

  2. Moore, C., Newman, M.E.J.: Epidemics and percolation in small-world networks. Phys. Rev. E 61, 5678–5682 (2000)

    CrossRef  Google Scholar 

  3. Anderson, R.M., May, R.M.: Infectious diseases of humans. Oxford University Press, Oxford (1991)

    Google Scholar 

  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)

    CrossRef  Google Scholar 

  5. Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86, 3200–3203 (2001)

    CrossRef  Google Scholar 

  6. Kuperman, M., Abramson, G.: Small world effect in an epidemiological model. Phys. Rev. Lett. 86, 2909–2912 (2001)

    CrossRef  Google Scholar 

  7. Keeling, M.J., Eames, K.T.D.: Networks and epidemic models. J. R. Soc. Interface 2, 295–307 (2005)

    CrossRef  Google Scholar 

  8. Eubank, S.: Network based models of infectious disease spread. Jpn. J. Infect. Dis. 58, S9–S13 (2005)

    Google Scholar 

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  12. Newman, M.E.J., Forrest, S., Balthrop, J.: Email networks and the spread of computer viruses. Phys. Rev. E 66, 035101 (2002)

    CrossRef  Google Scholar 

  13. Kermack, W., McKendrick, A.: A contribution to the mathematical theory of epidemics. Proc. R. Soc. A 115, 700–721 (1927)

    CrossRef  MATH  Google Scholar 

  14. Seasonal influenza (flu). Center for Disease Control and Prevention (2010), http://www.cdc.gov/flu/weekly/

  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. 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. 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. Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. Comput. Commun. Rev. 29, 251–262 (1999)

    CrossRef  Google Scholar 

  19. de Solla Price, D.J.: Networks of scientific papers. Science 149, 510–515 (1965)

    CrossRef  Google Scholar 

  20. Newman, M.E.J.: Networks: an introduction. Oxford University Press (2010)

    Google Scholar 

  21. Albert, R., Jeong, H., Barabasi, A.-L.: Error and attach tolerance of complex network. Nature 406, 378–382 (2000)

    CrossRef  Google Scholar 

  22. Pastor-Satorras, R., Vespignani, A.: Immunization of complex networks. Phys. Rev. E 65, 036104 (2002)

    CrossRef  Google Scholar 

  23. Dezsõ, Z., Barabási, A.-L.: Halting viruses in scale-free networks. Phys. Rev. E 65, 055103(R) (2002)

    CrossRef  Google Scholar 

  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. 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 

Download references

Author information

Authors and Affiliations

  1. Draper Laboratory, Cambridge, MA, 02139, USA

    Louis Kim, Mark Abramson & Stephan Kolitz

  2. Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

    Louis Kim, Kimon Drakopoulos & Asu Ozdaglar

Authors
  1. Louis Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Abramson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kimon Drakopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stephan Kolitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Asu Ozdaglar
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for Social Complexity and Department of Computational Social Science, George Mason University, 4400 University Drive, MS 6B2, 22030-4400, Fairfax, VA, USA

    William G. Kennedy

  2. University of Arkansas at Little Rock, 2801 South University Avenue, EIT Building, Room 553, 72204, Little Rock, AR, USA

    Nitin Agarwal

  3. Department of Computer Engineering, Rochester Institute of Technology, 83 Lomb Memorial Drive, Bldg 09, 14623-5603, Rochester, NY, USA

    Shanchieh Jay Yang

Rights and permissions

Reprints and Permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Kim, L., Abramson, M., Drakopoulos, K., Kolitz, S., Ozdaglar, A. (2014). Estimating Social Network Structure and Propagation Dynamics for an Infectious Disease. In: Kennedy, W.G., Agarwal, N., Yang, S.J. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2014. Lecture Notes in Computer Science, vol 8393. Springer, Cham. https://doi.org/10.1007/978-3-319-05579-4_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-05579-4_11

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05578-7

  • Online ISBN: 978-3-319-05579-4

  • eBook Packages: Computer ScienceComputer Science (R0)