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We extend Bayesian clustering techniques to explore predictive risk surfaces for covariate profiles of interest.
Figure 2 shows the relative risk surfaces between temperature and cerebrovascular mortality across all temperatures and lags.
BYM smoothing produced more homogeneous geographical areas than Poisson kriging, confirming it yielded smoother risk surfaces [ 17].
The BYM model produces smooth risk surfaces which, when entered into a cluster analysis, identify well-defined geographical clusters of adjacent areas.
The estimation of disease risk surfaces, using statistical point process techniques with case control data (Kelsall and Diggle 1998), offers another approach.
The risk surfaces were created by calculating the ratio of intensity functions for positive and negative herds on a grid of 2 × 2 km cells.
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The aim is to predict a continuous infection risk surface.
The risk surface showed a significant cluster of FIV in northwestern Montana.
We separate the underlying risk surface for each crime into a shared and a crime specific component.
We separated the underlying risk surface for each crime into a shared component, namely mara activity, and a crime specific component.
Instrumental to the quality of the resulting risk surface estimate is the choice of bandwidth for computation of the required numerator and denominator densities.
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