Using social networks to understand and overcome implementation barriers in the global HIV response

Despite the development of several efficacious HIV prevention and treatment methods in the past decades, HIV continues to spread globally. Uptake of interventions is non-randomly distributed across populations, and such inequality is socially patterned both statically (due to homophily) and dynamically (due to social selection and influence). Social network analysis (SNA) methods, including egocentric, sociocentric, and respondent-driven sampling, provide tools to measure most-at-risk populations, to understand how epidemics spread, and to evaluate intervention take-up. SNA informed designs can improve intervention effectiveness by reaching otherwise inaccessible populations and improve efficiency by maximizing spillovers to at-risk but susceptible individuals through social ties; they thus have the potential to be both more effective and less unequal in their effects than SNA-naïve approaches. While SNA-informed designs are often resource-intensive, they are uniquely able to help reach those most in need of HIV prevention and treatment interventions. Increased collection of social network data during both research and implementation work would provide important information to improve the roll-out of existing studies in the present and to inform the design of more data-efficient, SNA-informed interventions in the future.