Modelling and control of HIV dynamics

Landi, Alberto; Mazzoldi, Alberto; Andreoni, Chiara; Bianchi, Matteo; Cavallini, Andrea; Laurino, Marco; Ricotti, Leonardo; Iuliano, Rodolfo; Matteoli, Barbara; Ceccherini‐Nelli, Luca

doi:10.1016/j.cmpb.2007.08.003

Cited by 41 publications

(23 citation statements)

References 21 publications

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“…As secondary aspect, neither model (3) nor (5) discriminate between the effects of Protease Inhibitors (PI) and Reverse Transcriptase Inhibitors (RTI) used (together) in HAART protocols. This observation led Landi et al [9] to propose a variant of (4) in which infection rate constant β is replaced by an additional state which (slightly) grows linearly with time. Such a state, referred to as virulence, makes the system evolution uncontrollable after many years (decades).…”

Section: B Model 2: Including Immune System Response Variablesmentioning

confidence: 97%

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Parameters identification of HIV dynamic models for HAART treated patients: A comparative study

Laurino

Landi

Pannocchia

2014

2014 American Control Conference

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We present a comparative study of parameters identification of HIV dynamic models for naive patients that are treated with two different HAART (Highly Active Anti-Retroviral Therapy) protocols during a period of 48 weeks. Three HIV models of increasing complexity (in terms of number of state variables and parameters) have been chosen, and for each one the model parameters are computed by solving a nonlinear optimization problem via sequential quadratic programming (SQP). Model parameters are divided into “group dependent”, common to all patients treated with same HAART protocol, and “patient dependent”, specific for each patient, and are estimated in a way that an overall cost function comprising the fitting error of CD4+ concentration and viral load measurements. A preliminary parameter space grid search algorithm is performed in order to find a suitable initial guess for the SQP algorithm. Numerical results indicate that all considered models can give a good matching despite the scarcity of available measurements for each patient, and in this limited situation the minimal model appears to be (slightly) more effective than the other models

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Section: B Model 2: Including Immune System Response Variablesmentioning

confidence: 97%

“…The first variant represents a classical model in literature (e.g. [5], [6]), the other one includes the virulence based approach [7]- [9]). …”

Section: Introductionmentioning

confidence: 99%

Parameters identification of HIV dynamic models for HAART treated patients: A comparative study

Laurino

Landi

Pannocchia

2014

2014 American Control Conference

Self Cite

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show abstract

“…Various models have been proposed to mathematically describe the Human Immunodeficiency Virus (HIV) infection and its interaction with the human immune system (Wodarz and Nowak, 1999;Nowak and May, 2001;Landi et al, 2008), and optimal control of this system has been addressed in several works (Wodarz and Nowak, 1999;Heris and Khaloozadeh, 2011;Zurakowski and Teel, 2006;Ko et al, 2006). The simplest one is the three-state dynamical model (Wodarz and Nowak, 1999;Landi et al, 2008) for HIV infection, which has been presented and studied in several references.…”

Section: Hiv Infection Modelmentioning

confidence: 99%

“…Hence, the units of the parameters and those of the states are ignored. This model is used in previous works (Ko et al, 2006;Wodarz and Nowak, 1999;Landi et al, 2008) for the analysis of HIV infection and the development of new optimal control and treatment strategies. According to the literature, in these cases the general behavior profile of the system is more important than exact numerical matching among the simulation results and the real system trajectory.…”

Section: Hiv Infection Modelmentioning

confidence: 99%