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.

Keywords:

• complex networks
• HIV
• infection spreading
• computer simulation
• complex systems

Acknowledgements

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.