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Estimating Social Network Structure and Propagation Dynamics for an Infectious Disease

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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

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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

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05578-7

  • Online ISBN: 978-3-319-05579-4

  • eBook Packages: Computer ScienceComputer Science (R0)