Preventing HIV Spread in Homeless Populations Using PSINET

Homeless youth are prone to HIV due to their engagement in high risk behavior. Many agencies conduct interventions to educate/train a select group of homeless youth about HIV prevention practices and rely on word-of-mouth spread of information through their social network. Previous work in strategic selection of intervention participants does not handle uncertainties in the social network's structure and in the evolving network state, potentially causing significant shortcomings in spread of information. Thus, we developed PSINET, a decision support system to aid the agencies in this task. PSINET includes the following key novelties: (i) it handles uncertainties in network structure and evolving network state; (ii) it addresses these uncertainties by using POMDPs in influence maximization; (iii) it provides algorithmic advances to allow high quality approximate solutions for such POMDPs. Simulations show that PSINET achieves ~0% more information spread over the current state-of-the-art. PSINET was developed in collaboration with My Friend's Place (a drop-in agency serving homeless youth in Los Angeles) and is currently being reviewed by their officials.

[1]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[2]  Leslie Pack Kaelbling,et al.  Learning Policies for Partially Observable Environments: Scaling Up , 1997, ICML.

[3]  Reinhard Diestel,et al.  Graph Theory , 1997 .

[4]  R. Winett,et al.  Randomised, controlled, community-level HIV-prevention intervention for sexual-risk behaviour among homosexual men in US cities , 1997, The Lancet.

[5]  Masahiro Kimura,et al.  Tractable Models for Information Diffusion in Social Networks , 2006, PKDD.

[6]  Andreas Krause,et al.  Cost-effective outbreak detection in networks , 2007, KDD '07.

[7]  E. Rice The Positive Role of Social Networks and Social Networking Technology in the Condom-Using Behaviors of Homeless Young People , 2010, Public health reports.

[8]  Wei Chen,et al.  Scalable influence maximization for prevalent viral marketing in large-scale social networks , 2010, KDD.

[9]  Joel Veness,et al.  Monte-Carlo Planning in Large POMDPs , 2010, NIPS.

[10]  Andreas Krause,et al.  Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization , 2010, J. Artif. Intell. Res..

[11]  Tamara G. Kolda,et al.  Community structure and scale-free collections of Erdös-Rényi graphs , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  N. Milburn,et al.  Position-specific HIV risk in a large network of homeless youths. , 2012, American journal of public health.

[13]  Wheeler Ruml,et al.  Anticipatory On-Line Planning , 2012, ICAPS.

[14]  N. Milburn,et al.  Mobilizing homeless youth for HIV prevention: a social network analysis of the acceptability of a face-to-face and online social networking intervention. , 2012, Health education research.

[15]  Jean-Charles Pomerol,et al.  Multicriterion Decision in Management: Principles and Practice , 2012 .

[16]  David Hsu,et al.  DESPOT: Online POMDP Planning with Regularization , 2013, NIPS.

[17]  Malte Helmert,et al.  Trial-Based Heuristic Tree Search for Finite Horizon MDPs , 2013, ICAPS.

[18]  Patrik Haslum,et al.  Plan Quality Optimisation via Block Decomposition , 2013, IJCAI.

[19]  Arun G. Chandrasekhar,et al.  The Diffusion of Microfinance , 2012, Science.

[20]  E. Rice,et al.  How should network-based prevention for homeless youth be implemented? , 2013, Addiction.

[21]  Leandro Soriano Marcolino,et al.  Diverse Randomized Agents Vote to Win , 2014, NIPS.

[22]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..