  Bayesian disease mapping, yet if undeniably useful to describe variation in
risk over time and space, comes with the hurdle of prior elicitation on
hard-to-interpret random effect precision parameters. We introduce a
reparametrized version of the popular spatio-temporal interaction models, based
on Kronecker product intrinsic Gaussian Markov Random Fields, that we name the
variance partitioning (VP) model. The VP model includes a mixing parameter that
balances the contribution of the main and interaction effects to the total
(generalized) variance and enhances interpretability. The use of a penalized
complexity prior on the mixing parameter aids in coding prior information in a
intuitive way. We illustrate the advantages of the VP model using two case
studies.
