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Each year, there are nearly 4 million youth experiencing homelessness (YEH)in the United States with HIV prevalence ranging from 3 to 11.5%. Peer changeagent (PCA) models for HIV prevention have been used successfully in manypopulations, but there have been notable failures. In recent years, networkinterventionists have suggested that these failures could be attributed to PCAselection procedures. The change agents themselves who are selected to do thePCA work can often be as important as the messages they convey. To address thisconcern, we tested a new PCA intervention for YEH, with three arms: (1) an armusing an artificial intelligence (AI) planning algorithm to select PCA, (2) apopularity arm--the standard PCA approach--operationalized as highest degreecentrality (DC), and (3) an observation only comparison group (OBS). PCA modelsthat promote HIV testing, HIV knowledge, and condom use are efficacious forYEH. Both the AI and DC arms showed improvements over time. AI-based PCAselection led to better outcomes and increased the speed of interventioneffects. Specifically, the changes in behavior observed in the AI arm occurredby 1 month, but not until 3 months in the DC arm. Given the transient nature ofYEH and the high risk for HIV infection, more rapid intervention effects aredesirable.

Preliminary Results from a Peer-Led, Social Network Intervention,  Augmented by Artificial Intelligence to Prevent HIV among Youth Experiencing  Homelessness