Network analysis has been a very helpful tool for sociologists exploring human behaviours since its emergence in the middle of the 20th century. Although many network analysis techniques have potential applications in HIV epidemiology, the statistical analysis of empirical and simulated data capturing human sexual behaviour and the spread and control of HIV most often does not employ such techniques. Network graphs provide a succinct yet comprehensive visualisation of sexual networks. Various plotting techniques make it possible to superimpose realised and/or potential HIV transmission pathways on the network graph, as well as to highlight the positions and attributes of “key” individuals and links within the network.

In contrast to classical analysis of individual-level data, which assesses associations between individual’s characteristics and an outcome of interest for that individual, network analysis offers a statistical framework to evaluate the importance of individual’s characteristics from other people to whom the individual is connected in the network, be it directly or indirectly through sexual relationships. Consequently, critical transmission pathways and key sub-populations that share network characteristics essential for propagation or disruption of the HIV transmission cycle may be identified. For example, De et al. discovered that individuals playing a central role in the spread of gonorrhoea do not necessarily have a large number of relationships, but have a high information centrality, i.e. a short average distance to all other people in the network (1).

As the full potential of network analysis for HIV epidemiology remains to be unlocked, so do the often powerful and refreshing implications of the results from HIV transmission and sexual network analyses: a more precise definition of key groups and transmission pathways could help to improve the effectiveness of HIV prevention interventions. Further, the efficiency with which information on HIV prevention and treatment diffuses in the population may be improved if the socially influential individuals and information flows are detected and prioritized. Both these considerations are increasingly relevant in light of the growing pressure on national and global funding for HIV prevention and treatment.

Finally, network analysis unifies individual-level and network-level information. Overall descriptions of the entire community or population can be generated from individual data; conversely, individual behaviour can be predicted or simulated from macroscopic attributes of the network.

A questionnaire survey exploring sexual histories is being conducted in the Cape Town area. Statistical analysis will extract the information of sexual networks and sexual behaviour from individual level data in three communities with a high burden of HIV. The statistical models will be used as input for SIMPACT, a software tool to simulate the spreading of HIV and impact of intervention under alternative sexual network structures and treatments strategies (2). Network analysis will play an important role in this study in two aspects. Firstly, it can help to understand and measure the patterns of sexual behaviour of South Africans. Furthermore, the influence of these patterns on the HIV epidemic and effectiveness of interventions can be simulated by SIMPACT. Secondly, network analysis and simulation can contribute in optimising impact of expansion of treatment by comparing potential outcomes of alternative expansion strategies.

Reference(s)

1. De P, Singh AE, Wong T, Yacoub W, Jolly AM. Sexual network analysis of a gonorrhoea outbreak. Sex Transm Infect. 2004; 80:280-5. Link to article  
2. Delva W, Beauclair R, Welte A, et al. Age-disparity, sexual connectedness and HIV infection in disadvantaged communities around Cape Town, South Africa: a study protocol. BMC Public Health. 2011, 11:616. Link to article