title: Modeling-error robustness of a viral-load preconditioning strategy for HIV treatment switching.
abstract: In previous work, we have developed optimal-control based approaches that seek to minimize the risk of subsequent virological failure by "pre-conditioning" the viral load during therapy switches. In this paper, we use Monte-Carlo methods to evaluate the sensitivity of an open-loop implementation of these approaches to modeling errors. To account for hidden parameter dependencies, we use parameter distributions obtained from the convergence of Bayesian parameter estimation techniques applied to sets of clinical data obtained during serial therapy interruptions as the distribution from which the Monte-Carlo method samples.
authors: PhD student in Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA rutaoluo@udel.edu.
authors: Rutao
authors: R
authors: Luo
authors: Professor of Electrical Engineering, Penn State University Great Valley, 30E. Swedesford Road, Malvern, PA 19355, USA mjp5@gv.psu.edu.
authors: Michael J
authors: MJ
authors: Piovoso
authors: Assistant Professor of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA ryanz@udel.edu.
authors: Ryan
authors: R
authors: Zurakowski