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 journalArticleAcademicpeer-review

49 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 languageEnglish
Pages (from-to)1175-1187
Number of pages13
JournalInternational Journal of Computer Mathematics
Volume85
Issue number8
DOIs
Publication statusPublished - 7 Aug 2008
Externally publishedYes

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.

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