Stochastic simulation of HIV population dynamics through complex network modelling

P. M.A. Sloot; S. V. Ivanov; A. V. Boukhanovsky; D. A.M.C. Van De Vijver; C. A.B. Boucher

doi:10.1080/00207160701750583

Stochastic simulation of HIV population dynamics through complex network modelling

P. M.A. Sloot^*, S. V. Ivanov, A. V. Boukhanovsky, D. A.M.C. Van De Vijver, C. A.B. Boucher

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

51 Citations (Scopus)

Abstract

We propose a new way to model HIV infection spreading through the use of dynamic complex networks. The heterogeneous population of HIV exposure groups is described through a unique network degree probability distribution. The time evolution of the network nodes is modelled by a Markov process and gives insight in HIV disease progression. The results are validated against historical data of AIDS cases in the USA as recorded by the Center of Disease Control. We find a remarkably good correspondence between the number of simulated and registered HIV cases, indicating that our approach to modelling the dynamics of HIV spreading through a sexual network is a valid approach that opens up completely new ways of reasoning about various medication scenarios.

Original language	English
Pages (from-to)	1175-1187
Number of pages	13
Journal	International Journal of Computer Mathematics
Volume	85
Issue number	8
DOIs	https://doi.org/10.1080/00207160701750583
Publication status	Published - 7 Aug 2008
Externally published	Yes

Bibliographical note

Funding Information:
The authors would like to acknowledge the financial support of the European Union through the ViroLab project (www.virolab.org), EU project number: IST-027446. We thank Breanndán Ó Nualláin (University of Amsterdam) and Alex Vespignani (Indiana University) for proofreading the manuscript.

UN SDGs

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

Access to Document

10.1080/00207160701750583

Cite this

@article{4d70875606a44d7abc7725d3676d9a12,

title = "Stochastic simulation of HIV population dynamics through complex network modelling",

abstract = "We propose a new way to model HIV infection spreading through the use of dynamic complex networks. The heterogeneous population of HIV exposure groups is described through a unique network degree probability distribution. The time evolution of the network nodes is modelled by a Markov process and gives insight in HIV disease progression. The results are validated against historical data of AIDS cases in the USA as recorded by the Center of Disease Control. We find a remarkably good correspondence between the number of simulated and registered HIV cases, indicating that our approach to modelling the dynamics of HIV spreading through a sexual network is a valid approach that opens up completely new ways of reasoning about various medication scenarios.",

author = "Sloot, {P. M.A.} and Ivanov, {S. V.} and Boukhanovsky, {A. V.} and {Van De Vijver}, {D. A.M.C.} and Boucher, {C. A.B.}",

note = "Funding Information: The authors would like to acknowledge the financial support of the European Union through the ViroLab project (www.virolab.org), EU project number: IST-027446. We thank Breannd{\'a}n {\'O} Nuall{\'a}in (University of Amsterdam) and Alex Vespignani (Indiana University) for proofreading the manuscript.",

year = "2008",

month = aug,

day = "7",

doi = "10.1080/00207160701750583",

language = "English",

volume = "85",

pages = "1175--1187",

journal = "International Journal of Computer Mathematics",

issn = "0020-7160",

publisher = "Taylor & Francis Ltd",

number = "8",

}

TY - JOUR

T1 - Stochastic simulation of HIV population dynamics through complex network modelling

AU - Sloot, P. M.A.

AU - Ivanov, S. V.

AU - Boukhanovsky, A. V.

AU - Van De Vijver, D. A.M.C.

AU - Boucher, C. A.B.

N1 - Funding Information: The authors would like to acknowledge the financial support of the European Union through the ViroLab project (www.virolab.org), EU project number: IST-027446. We thank Breanndán Ó Nualláin (University of Amsterdam) and Alex Vespignani (Indiana University) for proofreading the manuscript.

PY - 2008/8/7

Y1 - 2008/8/7

N2 - We propose a new way to model HIV infection spreading through the use of dynamic complex networks. The heterogeneous population of HIV exposure groups is described through a unique network degree probability distribution. The time evolution of the network nodes is modelled by a Markov process and gives insight in HIV disease progression. The results are validated against historical data of AIDS cases in the USA as recorded by the Center of Disease Control. We find a remarkably good correspondence between the number of simulated and registered HIV cases, indicating that our approach to modelling the dynamics of HIV spreading through a sexual network is a valid approach that opens up completely new ways of reasoning about various medication scenarios.

AB - We propose a new way to model HIV infection spreading through the use of dynamic complex networks. The heterogeneous population of HIV exposure groups is described through a unique network degree probability distribution. The time evolution of the network nodes is modelled by a Markov process and gives insight in HIV disease progression. The results are validated against historical data of AIDS cases in the USA as recorded by the Center of Disease Control. We find a remarkably good correspondence between the number of simulated and registered HIV cases, indicating that our approach to modelling the dynamics of HIV spreading through a sexual network is a valid approach that opens up completely new ways of reasoning about various medication scenarios.

UR - http://www.scopus.com/inward/record.url?scp=47249141962&partnerID=8YFLogxK

U2 - 10.1080/00207160701750583

DO - 10.1080/00207160701750583

M3 - Article

AN - SCOPUS:47249141962

SN - 0020-7160

VL - 85

SP - 1175

EP - 1187

JO - International Journal of Computer Mathematics

JF - International Journal of Computer Mathematics

IS - 8

ER -

Stochastic simulation of HIV population dynamics through complex network modelling

Abstract

Bibliographical note

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this