Identification and experimental validation of an HIV model for HAART treated patients

Authors:
G. Pannocchia;E. Morano;M. Laurino;S. Nozza;G. Tambussi;A. Landi
Affiliations:
-;-;-;-;-;-
Venue:
Computer Methods and Programs in Biomedicine
Year:
2013

Citing 9
Cited 0

Modelling and control of HIV dynamics

Computer Methods and Programs in Biomedicine
HIV dynamics: Modeling, data analysis, and optimal treatment protocols

Journal of Computational and Applied Mathematics - Special issue: Mathematics applied to immunology
A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients

Computer Methods and Programs in Biomedicine
Computer simulation of structured treatment interruption for HIV infection

Computer Methods and Programs in Biomedicine
On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics

SIAM Review
Brief Estimation of HIV/AIDS parameters

Automatica (Journal of IFAC)
Optimal dosing of cancer chemotherapy using model predictive control and moving horizon state/parameter estimation

Computer Methods and Programs in Biomedicine
An integer programming approach for optimal drug dose computation

Computer Methods and Programs in Biomedicine
Optimal robust control of drug delivery in cancer chemotherapy: A comparison between three control approaches

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

The objective of this paper is to identify the parameters of a human immunodeficiency virus (HIV) evolution model from a clinical data set of patients treated with two different highly active antiretroviral therapy (HAART) protocols. After introducing a model with six state variables, a preliminary step considers the reduction of the number of parameters to be identified by means of sensitivity analysis, and then identifiability items are discussed. A nonlinear optimization-based procedure for identification is developed, which divides the unknown parameters into two families: the group dependent and the patient dependent parameters. Numerical results show that the identified model can be individually adapted to each patient and this result is promising for predicting the effects (e.g., failures or successes) of therapeutic actions.