title: A digital network approach to infer sex behavior in emerging HIV epidemics.
abstract: OBJECTIVE
  Improve the ability to infer sex behaviors more accurately using network data.
  
  
  METHODS
  A hybrid network analytic approach was utilized to integrate: (1) the plurality of reports from others tied to individual(s) of interest; and (2) structural features of the network generated from those ties. Network data was generated from digitally extracted cell-phone contact lists of a purposeful sample of 241 high-risk men in India. These data were integrated with interview responses to describe the corresponding individuals in the contact lists and the ties between them. HIV serostatus was collected for each respondent and served as an internal validation of the model's predictions of sex behavior.
  
  
  RESULTS
  We found that network-based model predictions of sex behavior and self-reported sex behavior had limited correlation (54% agreement). Additionally, when respondent sex behaviors were re-classified to network model predictions from self-reported data, there was a 30.7% decrease in HIV seroprevalence among groups of men with lower risk behavior, which is consistent with HIV transmission biology.
  
  
  CONCLUSIONS
  Combining the relative completeness and objectivity of digital network data with the substantive details of classical interview and HIV biomarker data permitted new analyses and insights into the accuracy of self-reported sex behavior.
authors.affiliation: Pritzker School of Medicine, University of Chicago, Chicago, Illinois, United States of America.
authors.forename: Abhinav
authors.initials: A
authors.lastname: Kapur
authors.affiliation: Department of Medicine, University of Chicago, Chicago, Illinois, United States of America; Department of Health Studies, University of Chicago, Illinois, Chicago, United States of America.
authors.forename: John A
authors.initials: JA
authors.lastname: Schneider
authors.affiliation: Department of Statistical Science, Duke University, Durham, North Carolina, United States of America.
authors.forename: Daniel
authors.initials: D
authors.lastname: Heard
authors.affiliation: Department of Statistical Science, Duke University, Durham, North Carolina, United States of America.
authors.forename: Sayan
authors.initials: S
authors.lastname: Mukherjee
authors.affiliation: Department of Health Studies, University of Chicago, Illinois, Chicago, United States of America.
authors.forename: Phil
authors.initials: P
authors.lastname: Schumm
authors.affiliation: Public Health Management Institute, SHARE-India, Hyderabad, Andhra Pradesh, India.
authors.forename: Ganesh
authors.initials: G
authors.lastname: Oruganti
authors.affiliation: Department of Sociology, University of Chicago, Chicago, Illinois, United States of America.
authors.forename: Edward O
authors.initials: EO
authors.lastname: Laumann
mesh_terms: D000293:Adolescent; D000328:Adult; D015415:Biomarkers / Q000032:analysis; D040421:Cell Phone; D006678:HIV / Q000378:metabolism; D015658:HIV Infections / Q000453:epidemiology* / Q000378:metabolism; D006801:Humans; D007407:Interviews as Topic; D008297:Male; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D057566:Self Report; D012725:Sexual Behavior; D012984:Software; D055815:Young Adult
chemical_list: D015415:Biomarkers