  The problem of choosing spatial sampling designs for investigating unobserved
spatial phenomenon S arises in many contexts, for example in identifying
households to select for a prevalence survey to study disease burden and
heterogeneity in a study region D. We studied randomised inhibitory spatial
sampling designs to address the problem of spatial prediction whilst taking
account of the need to estimate covariance structure. Two specific classes of
design are inhibitory designs and inhibitory designs plus close pairs. In an
inhibitory design, any pair of sample locations must be separated by at least
an inhibition distance {$\delta$}. In an inhibitory plus close pairs design, n
- k sample locations in an inhibitory design with inhibition distance
{$\delta$} are augmented by k locations each positioned close to one of the
randomly selected n - k locations in the inhibitory design, uniformly
distributed within a disc of radius {$\zeta$}. We present simulation results
for the Matern class of covariance structures. When the nugget variance is
non-negligible, inhibitory plus close pairs designs demonstrate improved
predictive efficiency over designs without close pairs. We illustrate how these
findings can be applied to the design of a rolling Malaria Indicator Survey
that forms part of an ongoing large-scale, five-year malaria transmission
reduction project in Malawi.
