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    Parsing social network survey data from hidden populations using stochastic context-free grammars

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    Name:
    Parsing social network survey ...
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    Genre
    Journal Article
    Date
    2009-09-07
    Author
    Poon, AFY
    Brouwer, KC
    Strathdee, SA
    Firestone-Cruz, M
    Lozada, RM
    Pond, SLK
    Heckathorn, DD
    Frost, SDW
    Subject
    Algorithms
    Data Collection
    Drug Users
    Female
    Humans
    Language
    Male
    Markov Chains
    Mexico
    Models, Statistical
    Public Health
    Social Support
    Stochastic Processes
    Substance Abuse, Intravenous
    Syphilis
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    Permanent link to this record
    http://hdl.handle.net/20.500.12613/5565
    
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    DOI
    10.1371/journal.pone.0006777
    Abstract
    Background: Human populations are structured by social networks, in which individuals tend to form relationships based on shared attributes. Certain attributes that are ambiguous, stigmatized or illegal can create a ÔhiddenÕ population, so-called because its members are difficult to identify. Many hidden populations are also at an elevated risk of exposure to infectious diseases. Consequently, public health agencies are presently adopting modern survey techniques that traverse social networks in hidden populations by soliciting individuals to recruit their peers, e.g., respondent-driven sampling (RDS). The concomitant accumulation of network-based epidemiological data, however, is rapidly outpacing the development of computational methods for analysis. Moreover, current analytical models rely on unrealistic assumptions, e.g., that the traversal of social networks can be modeled by a Markov chain rather than a branching process. Methodology/Principal Findings: Here, we develop a new methodology based on stochastic context-free grammars (SCFGs), which are well-suited to modeling tree-like structure of the RDS recruitment process. We apply this methodology to an RDS case study of injection drug users (IDUs) in Tijuana, México, a hidden population at high risk of blood-borne and sexually-transmitted infections (i.e., HIV, hepatitis C virus, syphilis). Survey data were encoded as text strings that were parsed using our custom implementation of the inside-outside algorithm in a publicly-available software package (HyPhy), which uses either expectation maximization or direct optimization methods and permits constraints on model parameters for hypothesis testing. We identified significant latent variability in the recruitment process that violates assumptions of Markov chain-based methods for RDS analysis: firstly, IDUs tended to emulate the recruitment behavior of their own recruiter; and secondly, the recruitment of like peers (homophily) was dependent on the number of recruits. Conclusions: SCFGs provide a rich probabilistic language that can articulate complex latent structure in survey data derived from the traversal of social networks. Such structure that has no representation in Markov chain-based models can interfere with the estimation of the composition of hidden populations if left unaccounted for, raising critical implications for the prevention and control of infectious disease epidemics. © 2009 Poon et al.
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    http://dx.doi.org/10.34944/dspace/5547
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