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