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Discovery of a missing disease spreader

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  • Maeno, Yoshiharu
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    Abstract

    This study presents a method to discover an outbreak of an infectious disease in a region for which data are missing, but which is at work as a disease spreader. Node discovery for the spread of an infectious disease is defined as discriminating between the nodes which are neighboring to a missing disease spreader node, and the rest, given a dataset on the number of cases. The spread is described by stochastic differential equations. A perturbation theory quantifies the impact of the missing spreader on the moments of the number of cases. Statistical discriminators examine the mid-body or tail-ends of the probability density function, and search for the disturbance from the missing spreader. They are tested with computationally synthesized datasets, and applied to the SARS outbreak and flu pandemic.

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    Bibliographic Info

    Article provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.

    Volume (Year): 390 (2011)
    Issue (Month): 20 ()
    Pages: 3412-3426

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    Handle: RePEc:eee:phsmap:v:390:y:2011:i:20:p:3412-3426

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    Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/

    Related research

    Keywords: Flu; Infectious disease outbreak; Node discovery; Probability density function; SARS; Stochastic differential equation;

    References

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    1. Christensen, Claire & Albert, István & Grenfell, Bryan & Albert, Réka, 2010. "Disease dynamics in a dynamic social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(13), pages 2663-2674.
    2. Isham, Valerie & Harden, Simon & Nekovee, Maziar, 2010. "Stochastic epidemics and rumours on finite random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 561-576.
    3. Maeno, Yoshiharu, 2010. "Discovering network behind infectious disease outbreak," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4755-4768.
    4. Fujie, Ryo & Odagaki, Takashi, 2007. "Effects of superspreaders in spread of epidemic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(2), pages 843-852.
    5. Walker, David M. & Allingham, David & Lee, Heung Wing Joseph & Small, Michael, 2010. "Parameter inference in small world network disease models with approximate Bayesian Computational methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 540-548.
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    Cited by:
    1. Yoshiharu Maeno & Kenji Nishiguchi & Satoshi Morinaga & Hirokazu Matsushima, 2012. "Optimal portfolio for a robust financial system," Papers 1211.5235, arXiv.org, revised Feb 2013.
    2. Maeno, Yoshiharu, 2013. "Transient fluctuation of the prosperity of firms in a network economy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3351-3359.

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