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.
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References
Newman, M.E.J.: The spread of epidemic disease on networks. Phys. Rev. E 66, 16128 (2002)
Moore, C., Newman, M.E.J.: Epidemics and percolation in small-world networks. Phys. Rev. E 61, 5678–5682 (2000)
Anderson, R.M., May, R.M.: Infectious diseases of humans. Oxford University Press, Oxford (1991)
Larson, R.C., Teytelman, A.: Modeling the effects of H1N1 influenza vaccine distribution in the United States. Value in Health 15, 158–166 (2012)
Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86, 3200–3203 (2001)
Kuperman, M., Abramson, G.: Small world effect in an epidemiological model. Phys. Rev. Lett. 86, 2909–2912 (2001)
Keeling, M.J., Eames, K.T.D.: Networks and epidemic models. J. R. Soc. Interface 2, 295–307 (2005)
Eubank, S.: Network based models of infectious disease spread. Jpn. J. Infect. Dis. 58, S9–S13 (2005)
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)
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)
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)
Newman, M.E.J., Forrest, S., Balthrop, J.: Email networks and the spread of computer viruses. Phys. Rev. E 66, 035101 (2002)
Kermack, W., McKendrick, A.: A contribution to the mathematical theory of epidemics. Proc. R. Soc. A 115, 700–721 (1927)
Seasonal influenza (flu). Center for Disease Control and Prevention (2010), http://www.cdc.gov/flu/weekly/
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
Weekly influenza update, May 27, 2010. Massachusetts Department of Public Health (2010), http://ma-publichealth.typepad.com/files/weekly_report_05_27_10.pdf
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
Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. Comput. Commun. Rev. 29, 251–262 (1999)
de Solla Price, D.J.: Networks of scientific papers. Science 149, 510–515 (1965)
Newman, M.E.J.: Networks: an introduction. Oxford University Press (2010)
Albert, R., Jeong, H., Barabasi, A.-L.: Error and attach tolerance of complex network. Nature 406, 378–382 (2000)
Pastor-Satorras, R., Vespignani, A.: Immunization of complex networks. Phys. Rev. E 65, 036104 (2002)
Dezsõ, Z., Barabási, A.-L.: Halting viruses in scale-free networks. Phys. Rev. E 65, 055103(R) (2002)
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)
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)
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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
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DOI: https://doi.org/10.1007/978-3-319-05579-4_11
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