HIV Prevention among Homeless Youth by Influence Maximization

Two young men laughing

HIV Prevention among Homeless Youth by Influence Maximization

  

PROJECT OVERVIEW

Influence Maximization for Social Good Using Social Networks to Spread Health Based Information

This project focuses on the development of decision support systems for homeless youth drop-in center staff who need to find the most influential homeless youth to raise awareness about HIV (and other sexually transmitted infections) among their peers, and to drive the homeless youth community towards safer behaviors and lifestyles.

Over the past three years, we have been collaborating with drop-in agency staff and youth experiencing homelessness at Safe Place for Youth (SPY), the Los Angeles LGBT Center’s Youth Center on Highland, and My Friend’s Place.

ABOUT HIV/AIDS

HIV is a very serious infection that sees no race, no color, no gender, no economic background and not even a specific age group. It can affect anyone, at any time if they put themselves in a situation where they could be at risk. Internationally, 37 million people are living with HIV.

HIV has an extremely high incidence among homeless youth, as they are more likely to engage in high HIV-risk behaviors (e.g., unprotected sexual activity, injection drug use) than other sub-populations. In fact, previous studies show that homeless youth are at 16X greater risk of HIV infection than stably housed populations. Thus, any attempt at eradicating HIV crucially depends on our success at minimizing rates of HIV infection among homeless youth.

In order to reduce the transmission of HIV, we are using a peer leader social network-based intervention, for information dissemination about HIV. Therefore, the key question is how can we identify the most “influential” youth, to be trained as peer leaders, from the social network of homeless youth? We want to find the youth who can spread awareness about HIV and induce behavior change among their peers, in the quickest and most efficient possible manner.

  

INFLUENCE MAXIMIZATION
FOR SOCIAL GOOD

Have You Heard? (HYH) focuses on the study of information diffusion in social networks of hard to reach populations (such as homeless youth) in order to spread information and raise general levels of awareness about dangerous diseases (such as HIV) among such populations.

The end goal of this project is to reduce rates of HIV infection among disadvantaged populations by influencing and inducing behavior change in homeless youth populations that drives them towards safer practices, such as regular HIV testing. The goal is not only to model these influence spread phenomena, but to also develop decision support systems (and the necessary tools/algorithms/mechanisms) using which information can be spread in the social networks of homeless youth in the most efficient manner. One of our primary foci in this project is to develop algorithms and tools that can be deployed in the real world.

Over the past three years, we have been collaborating with drop-in agency staff and youth experiencing homelessness at Safe Place for Youth (SPY), the Los Angeles LGBT Center’s Youth Center on Highland, and My Friend’s Place.

  

RESEARCH OVERVIEW

Diagram

We, in our work, try to help homeless drop-in centers use their resources more effectively by modeling this entire problem of selecting the most influential youth as an Influence Maximization Problem, which is a widely studied problem in the field of Artificial Intelligence. However, most previous work in this area has failed at addressing some key challenges that show up in the real world. Two major challenges are:


1) Constructing social networks of homeless youth is a big challenge, since these youth are a hard-to-reach population, and mapping out their social circles requires a lot of time and money.  Their networks are constantly changing, and there is not a constant/consistent roster like a classroom of school children.

2) Even if we are able to construct these networks, there is always noise in the data collection procedure, which leads to uncertainty about the true structure of the social network. This uncertainty needs to be accounted before deciding who is “influential” in the social network and who isn’t.

To overcome these challenges, we use a combination of state-of-the-art techniques from Artificial Intelligence, Sequential Planning, Decision Theory and Mathematics (that we developed in our lab). In order to find out more about our techniques and algorithms, please have a look at our publications.

  

MAIN STUDY

We conducted a 3-year study, which enrolled 718 young people experiencing homelessness at 3 Los Angeles area drop-in centers–My Friend’s Place, Safe Place for Youth, and the Los Angeles LGBT Center’s Youth Center on Highland.  This study is using the CHANGE algorithm, created by our team members.

CHANGE uses social network data is that is gathered via in-person interviews with a subsample of the youth.  Specifically, we randomly sample about 10% of the youth and then, for each of them, randomly sample one of their friends.  This allows us to begin to identify the network of homeless young persons accessing services at each drop-in center.   We use a combination of state-of-the-art techniques from Artificial Intelligence, Sequential Planning, Decision Theory and Mathematics to then identify who are the most influential youth, based on their positions within the network, to then train as peer leaders.
 

Flowchart of techniques used in the CHANGE algorithm

Diagram about observations, CHANGE agent and Actions

The results from this trial show that youth in the CHANGE arm of the intervention experience statistically significant improvements in their risk of engaging in unprotected sex compared to youth enrolled in an observation-only control group. We also observe a tendency towards larger and faster decrease in rates of unprotected sex in youth in the CHANGE arm compared to youth who receive an intervention where the highest degree nodes in the network are chosen as peer leaders.

Diagram about observations, CHANGE agent and Actions

 

  

PILOT STUDY

We deployed an algorithm named HEALER in the real world at Safe Place for Youth–a homeless drop-in center in Venice Beach, Los Angeles. Safe Place for Youth provides free food and clothing to homeless youth of the ages 12-25, three times a week. We enrolled 62 homeless youth from this shelter into our study and we conducted three test interventions. We used HEALER’s Facebook application to generate the network (see figure below) that connected these youth.

Diagram


 

 

 








Each number here is a homeless youth (their names have been replaced by numbers to protect their anonymity), and the edges between them represent their friendships. The results from this pilot were very promising. We found that HEALER was able to spread information to almost 66% of homeless youth in the network (one month after interventions had ended). This shows that HEALER is successful at finding the most influential youth in the network.

More importantly, we found that due to HEALER’s interventions, there was a 25% self-reported increase in the number of homeless youth who get tested for HIV regularly.

Thus, HEALER was successful in inducing behavior change among the youth as well.

Computer room

  

CLINICAL TRIAL
Clinical trial of an AI-augmented intervention for HIV prevention in youth experiencing homelessness 

Having developed an artificial intelligence system to optimize social network interventions in a community health setting, to maximize the spread of information through peer leaders, in this work,  we conducted a field trial enrolling 713 Youth Experiencing Homelessness (YEH) at drop-in centers in a large US city. The field trial compared interventions planned with the algorithm to those where the highest-degree nodes in the youths’ social network were recruited as peer leaders (the standard method in public health) and to an observation-only control group. Results from the field trial show that youth in the AI group experience statistically significant reductions in key risk behaviors for HIV transmission, while those in the other groups do not. This provides, to our knowledge, the first empirical validation of the usage of AI methods to optimize social network interventions for health. We also discuss lessons learned over the course of this project which may inform future attempts to use AI in community-level interventions.

Read more in our AAAI 2021 paper.

  

  

SPONSORS

California HIV/AIDS Research Program
The California HIV/AIDS Research Program (CHRP) logo

Army Research Office

 

PREVIOUS WORK

 

RELATED
PUBLICATIONS

Bryan Wilder, Laura Onasch-Vera, Juliana Hudson, Jose Luna, Nicole Wilson, Robin Petering, Darlene Woo, Milind Tambe, and Eric Rice. 2018. “End-to-End Influence Maximization in the Field.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).Abstract
2018_26_teamcore_aamas_deployment.pdf
This work is aims to overcome the challenges in deploying influence maximization to support community driven interventions. Influence maximization is a crucial technique used in preventative health interventions, such as HIV prevention amongst homeless youth. Drop-in centers for homeless youth train a subset of youth as peer leaders who will disseminate information about HIV through their social networks. The challenge is to find a small set of peer leaders who will have the greatest possible influence. While many algorithms have been proposed for influence maximization, none can be feasibly deployed by a service provider: existing algorithms require costly surveys of the entire social network of the youth to provide input data, and high performance computing resources to run the algorithm itself. Both requirements are crucial bottlenecks to widespread use of influence maximization in real world interventions. To address the above challenges, this innovative applications paper introduces the CHANGE agent for influence maximization. CHANGE handles the end-to-end process of influence maximization, from data collection to peer leader selection. Crucially, CHANGE only surveys a fraction of the youth to gather network data and minimizes computational cost while providing comparable performance to previously proposed algorithms. We carried out a pilot study of CHANGE in collaboration with a drop-in center serving homeless youth in a major U.S. city. CHANGE surveyed only 18% of the youth to construct its social network. However, the peer leaders it selected reached just as many youth as previously field-tested algorithms which surveyed the entire network. This is the first real-world study of a network sampling algorithm for influence maximization. Simulation results on real-world networks also support our claims.
Lily Hu, Bryan Wilder, Amulya Yadav, Eric Rice, and Milind Tambe. 2018. “Activating the 'Breakfast Club': Modeling Influence Spread in Natural-World Social Networks.” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18).Abstract
2018_23_teamcore_aamas_ajm.pdf
While reigning models of diffusion have privileged the structure of a given social network as the key to informational exchange, real human interactions do not appear to take place on a single graph of connections. Using data collected from a pilot study of the spread of HIV awareness in social networks of homeless youth, we show that health information did not diffuse in the field according to the processes outlined by dominant models. Since physical network diffusion scenarios often diverge from their more well-studied counterparts on digital networks, we propose an alternative Activation Jump Model (AJM) that describes information diffusion on physical networks from a multi-agent team perspective. Our model exhibits two main differentiating features from leading cascade and threshold models of influence spread: 1) The structural composition of a seed set team impacts each individual node’s influencing behavior, and 2) an influencing node may spread information to non-neighbors. We show that the AJM significantly outperforms existing models in its fit to the observed node-level influence data on the youth networks. We then prove theoretical results, showing that the AJM exhibits many well-behaved properties shared by dominant models. Our results suggest that the AJM presents a flexible and more accurate model of network diffusion that may better inform influence maximization in the field.
Amulya Yadav, Bryan Wilder, Eric Rice, Robin Petering, Jaih Craddock, Amanda Yoshioka-Maxwell, Mary Hemler, Laura Onasch-Vera, Milind Tambe, and Darlene Woo. 2018. “Bridging the Gap Between Theory and Practice in Influence Maximization: Raising Awareness about HIV among Homeless Youth.” In International Joint Conference on Artificial Intelligence (IJCAI).Abstract
2018_14_teamcore_bridging_gap_theory.pdf
This paper reports on results obtained by deploying HEALER and DOSIM (two AI agents for social influence maximization) in the real-world, which assist service providers in maximizing HIV awareness in real-world homeless-youth social networks. These agents recommend key ”seed” nodes in social networks, i.e., homeless youth who would maximize HIV awareness in their real-world social network. While prior research on these agents published promising simulation results from the lab, the usability of these AI agents in the real-world was unknown. This paper presents results from three real-world pilot studies involving 173 homeless youth across two different homeless shelters in Los Angeles. The results from these pilot studies illustrate that HEALER and DOSIM outperform the current modus operandi of service providers by ∼160% in terms of information spread about HIV among homeless youth.
Eric Rice, Amanda Yoshioka-Maxwell, Robin Petering, Laura Onasch-Vera, Jaih Craddock, Milind Tambe, Amulya Yadav, Bryan Wilder, Darlene Woo, Hailey Winetrobe, and Nicole Wilson. 2018. “Piloting the Use of Artificial Intelligence to Enhance HIV Prevention Interventions for Youth Experiencing Homelessness.” Journal of the Society for Social Work and Research, Volume 9, Number 4., 9, 4.Abstract
piloting_the_use_of_artificial_intelligenceto_enhance_hiv_prevention_interventionsfor_youth_experiencing_homelessness.pdf
Youth experiencing homelessness are at risk for HIV and need interventions to prevent risky sex behaviors. We tested the feasibility of using artificial intelligence (AI) to select peer change agents (PCAs) to deliver HIV prevention messages among youth experiencing homelessness. Method: We used a pretest– posttest quasi-experimental design. In the AI condition (n 5 62), 11 PCAs were selected via AI algorithm; in the popularity comparison (n 5 55), 11 PCAs were selected 6 months later based on maximum degree centrality (most ties to others in the network). All PCAs were trained to promote HIV testing and condom use among their peers. Participants were clients at a drop-in center in Los Angeles, CA. HIV testing and condom use were assessed via a self-administered, computer-based survey at baseline (n 5 117), 1 month (n 5 86, 74%), and 3 months (n 5 70, 60%). Results: At 3 months, rates of HIV testing increased among participants in the AI condition relative to the comparison group (18.8% vs. 8.1%), as did condom use during anal sex (12.1% vs. 3.3%) and vaginal sex (29.2% vs. 23.7%). Conclusions: AI-enhanced PCA intervention is a feasible method for engaging youth experiencing homelessness in HIV prevention
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