  The scan statistic sets the benchmark for spatio-temporal surveillance
methods with its popularity. In its simplest form it scans the target area and
time to find regions with disease count higher than expected. If the shape and
size of the disease outbreaks are known, then to detect it sufficiently early
the scan statistic can design its search area to be efficient for this shape
and size. A plan that is efficient at detecting a range of disease outbreak
shapes and sizes is important because these vary from one outbreak to the next
and are generally never known in advance. This paper offers a forward selection
scan statistic that reduces the computational effort on the usual single window
scan plan, while still offering greater flexibility in signalling outbreaks of
varying shapes. The approach starts by dividing the target geographical regions
into a lattice. Secondly it smooths the time series of lattice cell counts
using multivariate exponential weighted moving averages. Thirdly, these EWMA
cell counts are spatially smoothed to reduce spatial noise and leave the
spatial signal. The fourth step uses forward selection approach to scanning
mutually exclusive and exhaustive rectangular regions of dynamic dimensions. In
the fifth step, it prunes away all insignificant scanned regions where counts
are not significantly higher than expected. An outbreak is signaled if at least
one region remains after pruning. If all regions are pruned away - including
the scan of the target region, then no outbreak is signaled.
