Bayesian compartmental infectious disease models yield important inference on disease transmission, by appropriately accounting for the dynamics of infection processes. In addition to estimating transition probabilities, these statistical models allow researchers to assess disease risk and quantify the effectiveness of interventions. These models rely on data collected from all individuals classified as positive based on various diagnostic tests. However, such procedures produce both false-positives and false-negatives at varying rates depending on the sensitivity and specificity of the diagnostic tests being used. We propose a novel Bayesian spatio-temporal infectious disease modeling framework that accounts for the additional uncertainty in the diagnostic testing and classification process that provides estimates of the important transmission dynamics. The method is applied to data on the 2006 mumps epidemic in Iowa, in which over 4,000 suspected mumps cases were tested using an oral swab specimen and/or a blood specimen. While both procedures are believed to have high specificities, the sensitivities can be low and vary by the timing of the test as well as vaccination status.