Interplay Between Hiv/Aids Epidemics and Demographic Structures Based on Sexual Contact Networks

Abstract

In this article, we propose a network spread model for HIV epidemics, wherein each individual is represented by a node of the transmission network and the edges are the connections between individuals along which the infection may spread. The sexual activity of each individual, measured by its degree, is not homogeneous but obeys a power-law distribution. Due to the heterogeneity of activity, the infection can persistently exist at a very low prevalence, which has been observed in real data but can not be illuminated by previous models with homogeneous mixing hypothesis. Furthermore, the model displays a clear picture of hierarchical spread: In the early stage the infection is adhered to these high-risk persons, and then, diffuses toward low-risk population. The prediction results show that the development of epidemics can be roughly categorized into three patterns for different countries, and the pattern of a given country is mainly determined by the average sex-activity and transmission probability per sexual partner. In most cases, the effect of HIV epidemics on demographic structure is very small. However, for some extremely countries, like Botswana, the number of sex-active people can be depressed to nearly a half by AIDS.

Stochastic simulation of HIV population dynamics through complex network modelling

“…In particular, for HIV infected nodes μ may vary from μ = 0.15 for individuals without treatment to μ = 0.08 for individuals with treatment [6]. Combining expressions (2) and (3) one can express the epidemic model with homogeneous mixing in the form of a discrete Markov chain with a corresponding state vector given by: E = [s, ρ, r] T :…”

“…The demographic impact cannot be neglected. For instance, in Reference [6] the population in SIR models is randomly refreshed to correspond to the age structure for a given country. Another approach has been proposed in Reference [10] where a demographic rate has been introduced into a set of coupled differential equations for the related SIR model.…”

“…Each of these groups has distinctive features and generally they are simulated independently. Some models are used for a general population [22], but others for different exposure groups like heterosexual [6] or homo/bisexual [1] groups only. (2) The HIV epidemic is inhomogeneous over time.…”

“…(5) Store the current nodes state and generate a new random network. (6) Shift current time to one step forward and repeat (2)-(5). (7) Repeat (1)-(7) till statistical significance has been obtained.…”

“…Heterogeneity in these models can only be implemented through different transmission probabilities or different number of contacts for people in risk groups [3,[24][25][26]. An advantage of network-based epidemical models is the natural way in which heterogeneity can be expressed through network degree probability distributions and assortative or disassortative mixing [4,6,13]. The goal of the work presented here is to show that complex network (CN) based modelling techniques provide a universal and natural way to describe any kind of infection spreading and specifically HIV.…”