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Discovering network behind infectious disease outbreak

Stochasticity 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 stoc...

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Detalles Bibliográficos
Autor principal: Maeno, Yoshiharu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7125928/
https://www.ncbi.nlm.nih.gov/pubmed/32288081
http://dx.doi.org/10.1016/j.physa.2010.07.014
Descripción
Sumario:Stochasticity 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.