The European Physical Journal Special Topics

, Volume 222, Issue 6, pp 1377–1387 | Cite as

Combining social and genetic networks to study HIV transmission in mixing risk groups

  • Narges ZarrabiEmail author
  • Mattia C. F. Prosperi
  • Robbert G. Belleman
  • Simona Di Giambenedetto
  • Massimiliano Fabbiani
  • Andrea De Luca
  • Peter M. A. Sloot
Regular Article Simultaneous Dynamics ON and OF Networks


Reconstruction of HIV transmission networks is important for understanding and preventing the spread of the virus and drug resistant variants. Mixing risk groups is important in network analysis of HIV in order to assess the role of transmission between risk groups in the HIV epidemic. Most of the research focuses on the transmission within HIV risk groups, while transmission between different risk groups has been less studied. We use a proposed filter-reduction method to infer hypothetical transmission networks of HIV by combining data from social and genetic scales. We modified the filtering process in order to include mixing risk groups in the model. For this, we use the information on phylogenetic clusters obtained through phylogenetic analysis. A probability matrix is also defined to specify contact rates between individuals form the same and different risk groups. The method converts the data form each scale into network forms and combines them by overlaying and computing their intersection. We apply this method to reconstruct networks of HIV infected patients in central Italy, including mixing between risk groups. Our results suggests that bisexual behavior among Italian MSM and IDU partnership are relatively important in heterosexual transmission of HIV in central Italy.


Risk Group European Physical Journal Special Topic Transmission Network Average Path Length Inject Drug User 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© EDP Sciences and Springer 2013

Authors and Affiliations

  • Narges Zarrabi
    • 1
    Email author
  • Mattia C. F. Prosperi
    • 2
  • Robbert G. Belleman
    • 1
  • Simona Di Giambenedetto
    • 3
  • Massimiliano Fabbiani
    • 3
  • Andrea De Luca
    • 3
  • Peter M. A. Sloot
    • 1
    • 4
    • 5
  1. 1.Computational ScienceUniversity of AmsterdamAmsterdamThe Netherlands
  2. 2.College of Medicine, Department of Pathology, Immunology and Laboratory Medicine, Emerging Pathogens InstituteUniversity of FloridaGainesvilleUSA
  3. 3.Clinic of Infectious DiseasesCatholic University of Sacred HeartRomeItaly
  4. 4.National Research University ITMOSt. PetersburgRussia
  5. 5.Nanyang Technological UniversitySingaporeSingapore

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