Sensitivity analysis of HIV infection response to treatment

Samira Khalili; Antonios Armaou

doi:10.1109/cdc.2006.377635

Sensitivity analysis of HIV infection response to treatment

Samira Khalili
, Antonios Armaou

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

This work focuses on the analysis of HIV infection dynamics during the initial stages of infection when the viral load is low and random fluctuations may have a significant effect on the dynamics of the disease. The ability of deterministic models to accurately describe the expected behavior of such processes is limited. Stochastic simulations, which are not hampered by this limitation, are used to determine the probability of successful infection in an average patient. Specifically, a stochastic model based on Gillespie's algorithm is derived and is employed to determine the sensitivity of HIV infection to different treatment strategies and the effect of latency in treatment initiation on virus clearance probability. The model is subsequently revised to include mutation in the virus genome and the response to treatment is analyzed.

Original language	English (US)
Title of host publication	Proceedings of the 45th IEEE Conference on Decision and Control 2006, CDC
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2069-2075
Number of pages	7
ISBN (Print)	1424401712, 9781424401710
DOIs	https://doi.org/10.1109/cdc.2006.377635
State	Published - 2006
Event	45th IEEE Conference on Decision and Control 2006, CDC - San Diego, CA, United States Duration: Dec 13 2006 → Dec 15 2006

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Other

Other	45th IEEE Conference on Decision and Control 2006, CDC
Country/Territory	United States
City	San Diego, CA
Period	12/13/06 → 12/15/06

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

Access to Document

10.1109/cdc.2006.377635

Cite this

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title = "Sensitivity analysis of HIV infection response to treatment",

abstract = "This work focuses on the analysis of HIV infection dynamics during the initial stages of infection when the viral load is low and random fluctuations may have a significant effect on the dynamics of the disease. The ability of deterministic models to accurately describe the expected behavior of such processes is limited. Stochastic simulations, which are not hampered by this limitation, are used to determine the probability of successful infection in an average patient. Specifically, a stochastic model based on Gillespie's algorithm is derived and is employed to determine the sensitivity of HIV infection to different treatment strategies and the effect of latency in treatment initiation on virus clearance probability. The model is subsequently revised to include mutation in the virus genome and the response to treatment is analyzed.",

author = "Samira Khalili and Antonios Armaou",

year = "2006",

doi = "10.1109/cdc.2006.377635",

language = "English (US)",

isbn = "1424401712",

series = "Proceedings of the IEEE Conference on Decision and Control",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2069--2075",

booktitle = "Proceedings of the 45th IEEE Conference on Decision and Control 2006, CDC",

address = "United States",

note = "45th IEEE Conference on Decision and Control 2006, CDC ; Conference date: 13-12-2006 Through 15-12-2006",

}

Khalili, S & Armaou, A 2006, Sensitivity analysis of HIV infection response to treatment. in Proceedings of the 45th IEEE Conference on Decision and Control 2006, CDC., 4177828, Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc., pp. 2069-2075, 45th IEEE Conference on Decision and Control 2006, CDC, San Diego, CA, United States, 12/13/06. https://doi.org/10.1109/cdc.2006.377635

Sensitivity analysis of HIV infection response to treatment. / Khalili, Samira; Armaou, Antonios.
Proceedings of the 45th IEEE Conference on Decision and Control 2006, CDC. Institute of Electrical and Electronics Engineers Inc., 2006. p. 2069-2075 4177828 (Proceedings of the IEEE Conference on Decision and Control).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Sensitivity analysis of HIV infection response to treatment

AU - Khalili, Samira

AU - Armaou, Antonios

PY - 2006

Y1 - 2006

N2 - This work focuses on the analysis of HIV infection dynamics during the initial stages of infection when the viral load is low and random fluctuations may have a significant effect on the dynamics of the disease. The ability of deterministic models to accurately describe the expected behavior of such processes is limited. Stochastic simulations, which are not hampered by this limitation, are used to determine the probability of successful infection in an average patient. Specifically, a stochastic model based on Gillespie's algorithm is derived and is employed to determine the sensitivity of HIV infection to different treatment strategies and the effect of latency in treatment initiation on virus clearance probability. The model is subsequently revised to include mutation in the virus genome and the response to treatment is analyzed.

AB - This work focuses on the analysis of HIV infection dynamics during the initial stages of infection when the viral load is low and random fluctuations may have a significant effect on the dynamics of the disease. The ability of deterministic models to accurately describe the expected behavior of such processes is limited. Stochastic simulations, which are not hampered by this limitation, are used to determine the probability of successful infection in an average patient. Specifically, a stochastic model based on Gillespie's algorithm is derived and is employed to determine the sensitivity of HIV infection to different treatment strategies and the effect of latency in treatment initiation on virus clearance probability. The model is subsequently revised to include mutation in the virus genome and the response to treatment is analyzed.

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U2 - 10.1109/cdc.2006.377635

DO - 10.1109/cdc.2006.377635

M3 - Conference contribution

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SN - 9781424401710

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 2069

EP - 2075

BT - Proceedings of the 45th IEEE Conference on Decision and Control 2006, CDC

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 45th IEEE Conference on Decision and Control 2006, CDC

Y2 - 13 December 2006 through 15 December 2006

ER -

Sensitivity analysis of HIV infection response to treatment

Abstract

Publication series

Other

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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