  An expectation-based scan statistic is proposed for the prospective
monitoring of spatio-temporal count data with an excess of zeros. The method,
which is based on an outbreak model for the zero-inflated Poisson distribution,
is shown to be superior to traditional scan statistics based on the Poisson
distribution in the presence of structural zeros. The spatial accuracy and
detection timeliness of the proposed scan statistic is investigated by means of
simulation, and an application on weekly cases of Campylobacteriosis in Germany
illustrates how the scan statistic could be used to detect emerging disease
outbreaks. An implementation of the method is provided in the open source R
package scanstatistics available on CRAN.
