Performance of the prediction model was promising as it was significantly better than random prediction (i.e., the AUCs were higher than 0.5). Small proportions of source and secondary cases in the available data may have limited performance. However, the model can be an effective decision support tool if its ability to identify a case likely to be involved in transmission is superior to the intuition of public health officials. Thus, further evaluation of the model in the context of TB control program should be conducted. If effective, the model would be particularly useful when incidence of TB increases in a resource limited setting, in which efficient prioritization of investigation is desired. Overall, the current study has important implications in promoting the approach of evidence-based practice in control of TB.