In the epidemiology of infectious diseases, much of researchers’ concerns center around how disease spreads or, more optimistically, how an outbreak may be mitigated by other social and biological factors. Researchers with REACH have been innovatively applying the simulation abilities of social network modeling to better understand those factors that may alter the spread of infectious diseases in certain communities. Most recently, Kirk Dombrowski, Bilal Khan, and Patrick Habecker contributed to this rather revolutionary understanding of epidemiology through their analysis of the interaction of network structure and virus natural history in diminishing the spread of HIV within social networks of injection drug users.
Through their analysis, REACH researchers attempt to explain a “firewall effect” in social networks of people who use injection drugs (PWID) which has the ability to diminish the outbreak of HIV within the community. The firewall phenomenon, first proposed by Friedman et al., finds that “structural features of PWID risk networks alone could combine with the specific epidemiological natural history of HIV-1 virus to insulate uninfected segments of the network from highly transmissible, early stage infections.” Dombrowki, Khan, and Habecker studied more in-depth the existence of a firewall effect in social networks as well as possible risk characteristics of networks in the transmission of HIV throughout communities of injection drug users in New York. Their results were published earlier this year in an article in the Aids and Behavior journal.
Results of this study show that broader network characteristics significantly affect the spread of HIV following initially intense outbreaks. REACH researchers found that following HIV outbreaks, networks reach a rough limit between 40 and 55 percent of the population before HIV infection basically stops spreading through the network, preventing prevalence rates from reaching population saturation. Moreover, the study found that the firewalling effect preventing further outbreak in at-risk networks may be influenced or diminished by other social factors, leading to an increase in HIV prevalence in the network.
Further investigation on the effect of macro network structures on disease spreading is necessary, but this study has produced meaningful results that are sure to spark more interest in network modeling of epidemiology. This research is vital and urgent to understand the spread of such acute, serious infectious outbreaks like those of HIV, especially in communities of injection drug users who are at an increased risk for infection. Even more optimistically, the work by REACH researchers displays instances in which networks of PWID can use strategies involved with self-organizing to prevent the devastating spread of HIV.