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