This paper describes a model of the immunologic response of latent viruses and a donor kidney in a renal transplant recipient. An optimal control problem with state variable inequality constraints is considered to maintain the balance between over-suppression where latent viruses are reactivated and under-suppression where the transplanted kidney is rejected. A feedback methodology based on the model predictive control (MPC) method is proposed to design (sub)optimal treatment regimes. In addition, the problem of implementing the MPC methodology and nonlinear Kalman filter with inaccurate or incomplete observation data and long measurement periods is addressed. The results of numerical simulations show that a (sub)optimal dynamic immunosuppression therapy method can help strike a balance between the over-suppression and under-suppression of the immune system.
All Science Journal Classification (ASJC) codes
- Modelling and Simulation
- Computational Theory and Mathematics
- Computational Mathematics