File Exchange

image thumbnail


version 1.0 (179 KB) by

Algorithms based on optimal control theory to optimise the treatment strategies for a disease.

1 Download


TOP (Treatment Optimiser) consists of Matlab codes that implements algorithms to solve optimal control problems in the context of treatment optimization.
Finding optimal treatment strategies is a very important and non-trivial problem. A policy maker has to take into account a number of factors such as the health state of the patient, resource (monetary) constraints, constraints on the design of a treatment strategy etc.. In our work (Duwal et al. 2015), we presented and compared two treatment paradigms: diagnostic-guided and a pro-active treatment strategies exemplified for controlling HIV-1 replication in the light of resource constraints and evolutionary dynamics of drug resistance development. A diagnostic-guided strategy tailors treatment decisions on an individual basis guided by infrequent and possibly costly diagnostics. In contrast, a pro-active strategy suggests treatment decisions based on experience and projected outcomes. The latter allows switching treatments before drug resistance is detectable, in contrast to a diagnostic-guided strategy. However, pro-actively switching treatments may also lead to unnecessary treatment changes.

Mathematically, a diagnostic-guided strategy can be formulated as a closed-loop optimal control problem and the optimal solution can be efficiently solved using dynamical programming, e.g. by the policy iteration algorithm (Winkelmann et al. 2014). A pro-active strategy can be described as an open-loop optimal control problem. We developed an efficient dynamic programming algorithm based on a branch-and-bound technique (Duwal et al. 2015) allowing to solve this optimization problem efficiently.

Reference :
-) Optimal treatment strategies in the context of ‘treatment for prevention’ against HIV-1 in resource-poor settings. S. Duwal, S. Winkelmann, C. Schütte and M. von Kleist, PLoS Comput. Biol., 11, e1004200, 2015
-) Markov Control Processes with Rare State Observation: Theory and Application to Treatment Scheduling in HIV-1 S. Winkelmann, C. Schütte and M. von Kleist. Communications in Mathematical Sciences 12, 859, 2014

Comments and Ratings (0)



Description change




Graphics comparing Pro-active and Diagnostic guided Strategies

MATLAB Release
MATLAB 8.0 (R2012b)

MATLAB Online Live Editor Challenge

Win cash prizes and have your live script featured on our website

Learn more

Download apps, toolboxes, and other File Exchange content using Add-On Explorer in MATLAB.

» Watch video