Two types of errors are associated with any decision rule: a) a false-positive result, that is, declaring an area as having elevated risk when in fact its underlying true rate equals the background level (an error also traditionally referred to as type I error or lack of specificity); and b) a false-negative result, that is, declaring an area to be in the background when in fact its underlying rate is elevated (an error also referred to as type II error or lack of sensitivity). In epidemiology, performances are discussed either by reporting these error rates or their complementary quantities that measure the success rates of the decision rule. The two goals of disease mapping can be summarized as follows: not to overinterpret excesses arising by chance, that is, to minimize the false-positive rate but to detect patterns of true heterogeneity, that is, to maximize the sensitivity. We thus choose to report these two easily interpretable quantities. To be precise, for any decision rule D(c, R0), we compute