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.

HIV decision support: from molecule to man

“…The obvious feature in figure 6 is a substantial decline of AIDS cases after introduction of HAART (1996). Our model correctly reproduces this decline and the temporal separation of the peaks of the AIDS epidemic in the heterosexual and MSM populations (see Sloot et al 2008a).…”

Advances in dynamic temporal networks: Understanding the temporal dynamics of complex adaptive networks

“…Networks evolve over time and show a remarkable robustness and resilience to "environmental pressure". Examples are Protein-Protein Networks [15], Slime Mold Networks [1], Phylogenetic-Social Networks [41], Sexual Networks [28,29,35], "Bio" networks [22], Drug Addiction Networks [16] and Criminal Networks [17]. This robustness and resilience is often realized through dynamic modification of the network topology as well as the behavioural and functional changes of the links and nodes.…”

“…[19,24,26,33]) into a new focus of investigation. Several important problems ranging from virology [4], to contact networks [34,35] and ecological theory [23] similarly invite a treatment of dynamic temporal networks on their own. A list of early works is given in [10].…”

Visual Exploration of Complex Network Data Using Affective Brain-Computer Interface

“…For the purpose of the experiments, a network of sexual contacts used for HIV simulation [15] was visualised using developed software. The network for that case contains individuals (nodes) and their contacts (edges).…”