Spatial and space-time scan statistics [1-4] have become popular methods in disease surveillance for the detection of disease clusters, and they are also used in many other fields. In most applications to date, the interest has been in count data such as disease incidence, mortality or prevalence, for which a Poisson or Bernoulli distribution is used to model the random nature of the counts. For example, in papers published in 2008, Chen et al. [5] studied cervical cancer mortality in the United States; Osei and Duker [6] studied cholera prevalence in Ghana; Oeltmann et al. [7] looked at multidrug-resistant tuberculosis prevalence in Thailand; Mohebbi at al. [8] studied gastrointestinal cancer incidence in Iran; Rubinsky-Elefant et al. [9] looked at human toxocariasis prevalence in Brazil; Frossling et al. [10] evaluated the Neospora caninum distribution in dairy cattle in Sweden; Heres et al. [11] studied mad-cow disease in the Netherlands; and Reinhardt et al. [12] developed a system for prospective meningococcal disease incidence surveillance in Germany.