Discovering network behind infectious disease outbreak
AbstractStochasticity and spatial heterogeneity are of great interest recently in studying the spread of an infectious disease. The presented method solves an inverse problem to discover the effectively decisive topology of a heterogeneous network and reveal the transmission parameters which govern the stochastic spreads over the network from a dataset on an infectious disease outbreak in the early growth phase. Populations in a combination of epidemiological compartment models and a meta-population network model are described by stochastic differential equations. Probability density functions are derived from the equations and used for the maximal likelihood estimation of the topology and parameters. The method is tested with computationally synthesized datasets and the WHO dataset on the SARS outbreak.
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Bibliographic InfoArticle provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.
Volume (Year): 389 (2010)
Issue (Month): 21 ()
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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/
Epidemiological compartment model; Meta-population network model; Maximal likelihood estimation; Severe Acute Respiratory Syndrome; Stochastic differential equation;
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- 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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- Yoshiharu Maeno & Kenji Nishiguchi & Satoshi Morinaga & Hirokazu Matsushima, 2012. "Optimal portfolio for a robust financial system," Papers 1211.5235, arXiv.org, revised Feb 2013.
- Maeno, Yoshiharu, 2011. "Discovery of a missing disease spreader," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3412-3426.
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