The rate of the spread of HIV infections in a population is proportional to the number of individuals with multiple sex partners at the same time within the population. Although most individuals in a social network will have a few sex partners, a small percentage of individuals will have a significant number of sexual contacts. HIV spreads via the most sexually active individuals in a social network. The degree of the susceptibility of a person to HIV infection depends on whether he or she is in a short-term or long-term sexual relationship. Surveys on the behaviour of sexual partners in various countries demonstrate that couples in long-term monogamous relationships are less likely to adopt HIV prevention measures such as the use of condoms. On the other hand, individuals in short-term relationships are cautious during sexual encounters and tend to insist that their partner uses a condom. The scope of the spread of an HIV epidemic depends on factors such as the duration of relationships, attitudes towards HIV and frequency of risk behaviours. The propagation of HIV outside high-risk subgroups into low-risk populations comprising of long-term monogamous relationships depends on free links, which serve as vectors for the propagation of the virus. Although the spread of HIV in low-risk groups is slow, it results in significant infections because of the large population size. The dynamism of a sexual network determines the creation and sustenance of links between HIV agents. A highly active agent can establish several new contacts in a short period leading to the exposure of a significant portion of the network to the virus. The frequency of contact with an HIV agent or a node related to the agent depends on the link activity in a social network. For example, about 250 people contracted HIV through sexual encounters with “Patient O” or somebody in his social network. Although agents with a high number of links are unlikely to maintain their connections for long duration, the links allow the rapid spread of primary HIV infection via a well-connected network of short-term links.
An analysis of the statistics on HIV infections in both the industrialized and developing countries illustrates a relationship between the spread of HIV infections and particular risk behaviours. Culture and traditions Africa amplify the effects of risk behaviours on the spread of HIV. The use of social network models will help in the simulation and evaluation of epidemiologic and behavioural data on the pattern of HIV infections in the U.K and South Africa. The analysis of the distribution of risk behaviours between men and women in the United Kingdom and South Africa demonstrates that a significant number of men in South African have more than one sex partner. The data shows that only a few South African women have multiple sex partners. The application of the power law in the interpretation of the distribution of sex partners for South African men resulted in a network with a significant means of the spread of HIV. The evaluation of the scale-free tail of distribution of sex partners for South African women illustrates a higher number of sex partners for women compared to men in South Africa. The simulation of the network model illustrates that the high rate of HIV infection in South Africa is a consequence of the high number of women with several sex partners due to the polygamous behaviour of South African men. The maximum number of sex partners for British women is lower than that for men. The report contrasts with the South African statistics, which demonstrate that the maximum number of partners for South African women is triple the number reported by men (Schneeberger et al. 2004, p. 386). The simulation of data on sex partners and HIV infection among MSMs in London in a small-world network closely resembles the simulation of HIV infection in a scale-free network of heterosexuals in South Africa. Data on the two groups demonstrates that the chief cause of the high HIV infection is the tendency to have multiple sex partners (Liljeros et al. 2003, p. 193). The comparison of HIV infection in the United Kingdom and South Africa uses a sample that is representative of the behaviour patterns of the core group. The study illustrates that polygamous behaviours in South African men is the chief cause of the widespread HIV infection in the country. On the other hand, HIV infection in the United Kingdom is low due to few sex partners among heterosexual.
Knowledge of scale-free networks is crucial in the management of a contagion. For example, quarantining the dominant nodes in a disease transmission network helps to control the spread of the disease. Long distances between individuals (nodes) slow the spread of a sexually transmitted disease due to the disconnection in sexual relationships within a social network. The disease would have to propagate over long distances in a grid network to infect a significant number of individuals. For example, the distribution of an STI in Britain will depend on the duration and probability of infection of the STI. On the other hand, polygamous tendencies in sub-Saharan countries will enhance the diffusion of the STI. Health care providers and policy makers can exploit the properties of scale-free networks to mitigate the spread of a sexually transmitted disease. HIV intervention measures should include programs to influence changes from risk behaviours. Targeting the most sexually active individuals and educating them on the benefits monogamous sexual practices will help to curtail the widespread HIV infection in sub-Saharan Countries. Policy makers should focus on outreach programs that ensure efficient administration of education and interventions such as condoms to actors in the dominant nodes. The efficient control of the spread of a sexually transmitted disease requires the targeting of well-connected nodes. Programs aimed at reaching all the nodes in a social network are unlikely to control a contagion (Bonacich & Lu 2012, p. 135). A simulation of HIV infections among1000 South Africans will consider that 18 percent of the respondents are HIV+. The number of infected nodes in a scale-free network will represent the percentage of HIV infection among males and females in a sample. For example, the scale-free network for a country with a 63 percent HIV infection in females will have 113 nodes to represent the HIV+ females and 67 nodes to symbolize the HIV+ males. A simulation involving the allocation of HIV interventions to random nodes results in a 14 percent decline in the infected nodes over a period of 3 years. On the other hand, a simulation involving the preferential assignment of HIV interventions to highly connected nodes leads to a 34 percent decline in the number of infected nodes. The experiment demonstrates that the highly connected nodes have significant instances of sexual interaction.
Reference List
Bonacich, P & Lu, P 2012, Introduction to mathematical sociology, Princeton University Press, Princeton.
Liljeros, F., Edling, C., & Amaral, L 2003, ‘Sexual networks: Implications for the transmission of sexually transmitted infections’, Microbes and Infection, vol. 5, no. 2, pp. 189-196.
Schneeberger, A., Mercer, C., Gregson, J., Ferguson, N., Nyamukapa, C., & Anderson, R 2004, ‘Scale-Free Networks and Sexually Transmitted Diseases’, Sexually Transmitted Diseases, vol. 31, no. 6, pp. 380–387.