Stochastic simulation of HIV population dynamics through complex network modelling

Authors:
P. M. A. Sloot;S. V. Ivanov;A. V. Boukhanovsky;D. A. M. C. Van De Vijver;C. A. B. Boucher
Affiliations:
Section Computational Science, University of Amsterdam, The Netherlands;St. Petersburg State University, St. Petersburg, Russian Federation;St. Petersburg State University, St. Petersburg, Russian Federation;Department of Virology, University Medical Centre, Utrecht, The Netherlands;Department of Virology, University Medical Centre, Utrecht, The Netherlands
Venue:
International Journal of Computer Mathematics - COMPLEX NETWORKS
Year:
2008

Citing 2
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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.