The researchers have developed a network model allows them to predict the likelihood of new HIV
infections and identity those individuals at greatest risk.
Communicable diseases, such as HIV, are spread through social or sexual networks and the structure of these networks determine the spread of infection.
Understanding their spread can help inform public health measures and policy. In the past networks have been determined through conducting interviews and tracing partners activity. However, these methods are of limited value for infections such as HIV, with long incubation times, and low transmission rate per contact.
“Not everyone who is HIV infected is equally likely to transmit the infection to others. There are clusters of more active diseases transmission. We can use this information to target treatment interventions to those most likely to transmit the virus to others and markedly reduce the number of infections” says Professor Susan Little at UC San Diego, lead author of the paper.
published in PLOS ONE, is the first of its kind and combined methods from classical and molecular epidemiology to characterise local transmission network of HIV in San Diego, California.
Prof. Little's team analysed HIV-1 sequence data from 478 recently HIV-1
infected persons and 170 their sexual contacts between 1996 and 2011. Sequence data was collected as part of the routine HIV genetic testing used to determine if a virus is drug resistant.
A transmission network score (TNS) was developed to estimate the risk of HIV transmission from a newly diagnosed individual to a new partner and to target prevention interventions. Participants with a high TNS were significantly more likely than those with low TNS to develop close links to another person within their first year of HIV infection, suggesting onwards transmission say the authors.
The team found that viruses from two people that were highly genetically similar does not independently prove transmission occurred, only that individuals are part of a closely connected transmission network.
The study also showed that the TNS was highly correlated with transmission risk behaviours and outcome, showing the network model can be used to identify and target intervention antiretroviral therapy
(ART) to those at greatest risk.
Using the model, the team showed that by deploying ART to those with highest TNS resulted in a significantly greater likelihood of reduced new HIV-1 transmissions than providing ART to the same number of randomly selected individuals.
“The more we understand the structure and dynamics of HIV transmission network, the better we can identify these hot spots of transmission” says Prof. Little.
“Such TNS guided treatment and prevention interventions could markedly lower rates of new HIV infection in our community”
Article Source: Using HIV Networks to Inform Real Time Prevention Interventions
Little SJ, Kosakovsky Pond SL, Anderson CM, Young JA, Wertheim JO, et al. (2014) Using HIV Networks to Inform Real Time Prevention Interventions. PLoS ONE 9(6): e98443. doi: 10.1371/journal.pone.0098443