Metro networks face operational challenges due to increasing ridership and system growth, particularly in managing delay propagation. Epidemiology models have recently been an interesting method in transportation research for studying delays. This study, therefore, aims to see if the Susceptible-infectious-susceptible (SIS) model is suitable to help model delay propagation in a metro network through its ability to reproduce the vulnerability of metro stations for specific instances. Using data from the Washington Metro Network, delay propagation instances were grouped, and the model was trained and tested using a differential evolution algorithm. The results indicate that the vulnerability values as calculated from the data do not follow the expected trend. Also, the model can predict the vulnerability values for the first group more accurately than the second group. However, limitations such as underestimation and overestimation of station vulnerabilities, and sensitivity to training data and parameters were observed. These challenges stemmed from the dynamics between specific parameters, the mismatch in the order of magnitude of model components, and the lack of additional factors.