Stochasticity and spatial heterogeneity are of great interest recently in studying the spread of an infectious disease. Populations in a combination of epidemiological compartment models and a meta-population network model are described by stochastic differential equations. The presented method solves a node discovery problem to identify the nodes within a given dataset which are directly influenced by an unknown neighboring node during the spread. The dataset is either the time sequence data on the number of infectious persons or new cases in the early growth phase of an infectious disease outbreak. The network topology and transmission parameters are revealed by the maximal likelihood estimation. The degree of influence on individual nodes from an unknown origin is calculated with the technique of the extreme sequence detection given the revealed topology and parameters. The method is tested with computationally synthesized datasets and the WHO dataset on SARS outbreak.
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