With aging populations and rising healthcare demands, efficient patient transportation has become a critical challenge, particularly in the context of infection control. In this paper an extended mixed-integer linear programming (MILP) model for optimizing patient transport in urban environments is presented, with a focus on the separate transportation of infectious and non-infectious individuals. The model incorporates time windows, maximum allowable ride durations, and mandatory vehicle disinfection requirements. Experimental results obtained using CPLEX demonstrate that incorporating infection control measures significantly influences both route planning and computational complexity. The proposed approach provides a scalable foundation for future multi-vehicle extensions and cost-based optimization strategies.