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 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.

• Publication year

  2009

• Venue

  Physica

• Publication date

  2009-05-21

• Fields of study

  Biology, Computer Science, Mathematics, Environmental Science, Medicine

• Identifiers
  DOI 10.1016/j.physa.2010.07.014arXiv 0905.3582PMID 32288081PMCID 7125928
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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