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Human Sexual Networks

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Computational Complexity
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Article Outline

Glossary

Definition of the Subject

Introduction

Non‐Complex Models of Contagious Diseases

The Core Group Theory

Lessons Learned from the Early AIDS Epidemic

Clustering

The Effect of Geographical Space

The Long Tail

The Importance of Concurrent Relationship

Assortative Interaction

Data Sources

Future Directions

Bibliography

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Abbreviations

Basic reproduction rate :

The most common way to calculate the epidemic threshold is to calculate the basic reproduction rate, \( { R_0 } \), which is usually defined as the average number of secondary infections caused by one infectious individual that enters into a totally susceptible population. The basic reproduction rate may underestimate the risk of epidemic outbreaks if the variation in number of contacts is large, as is usually the case with sexual contacts.

Core group :

A subgroup of individuals in a population characterized by a high partner turnover rate and a high tendency for having sexual contacts within the group. The existence of a core group may push the population above the epidemic threshold.

Epidemic threshold :

The probability that an epidemic will occur is determined by the contagiousness of the disease, the duration of infectiousness, and the interaction structure in the population. Contagious diseases are nonlinear phenomena in the sense that small changes in any of these parameters may push the population from a state in which a large epidemic is not possible to a state in which an epidemic may easily occur if infection is introduced into the population. The specific point at which an epidemic is possible is referred to as the epidemic threshold.

Random homogeneous mixing :

When modeling outbreaks of contagious diseases in a population, the individuals are often assumed to have the same probability of interacting with everyone else in the population. This assumption has been shown to be less valid for sexually transmitted infections because they are characterized by a large variation in number of contacts.

Sexually transmitted infection:

Many contagious infections can be spread through sexual contact. Sexually transmitted infections are, however, generally defined as being spread through vaginal intercourse, anal intercourse, and oral sex. They include Chlamydia trachomatis, gonorrhea, and HIV. The reason why the expression “sexually transmitted infection” is used instead of “sexually transmitted disease” is that a state of infection and infectiousness do not necessarily result in disease.

Bibliography

  1. Anderson R, May RM (1991) Infectious diseases of humans. Oxford UniversityPress, Oxford

    Google Scholar 

  2. Barabási AL, Albert R (1999) Emergence of scaling in randomnetworks. Science 286(5439):509–512

    Google Scholar 

  3. Bearman PS, Moody J et al (2004) Chains of affection: The structure ofadolescent romantic and sexual networks. Am J Sociol 110(1):44–91

    Article  Google Scholar 

  4. Brewer DD, Potterat JJ, Garrett SB, Muth SQ, Roberts JM, Kazprzyk D, Montano DE,Darrow WW (2000) Prostitution and the sex discrepancy in reported number of sexual partners. Proc Natl Acad Sci USA97:12385–12388

    Article  Google Scholar 

  5. Colgate SA, Stanley EA et al (1989) Risk behavior‐based model of the cubicgrowth of acquired immunodeficiency syndrome in the United States. Proc Nat Acad Sci US 86(12):4793–4797

    Article  Google Scholar 

  6. Dezso Z, Barabasi AL (2002) Halting viruses in scale-free networks. Phys Rev EStat Nonlin Soft Matter Phys 65(5 Pt 2):055103

    Article  Google Scholar 

  7. Diekman O, Heesterbeek JAP (2000) Mathematical epidemiology of infectiousdisease. John Wiley and Son, Chichester

    Google Scholar 

  8. Foulkes MA (1998) Advances in HIV/AIDS statistical methodology over the pastdecade. Stat Med 17(1):1–25

    Article  MathSciNet  Google Scholar 

  9. Frank O (1971) Statistical inference in graphs. FOA,Stockholm

    Google Scholar 

  10. Freiesleben de Blasio B, Svensson B et al (2007) Preferential attachement insexual networks. PNAS 104(26):10762–10767

    Article  Google Scholar 

  11. Handcock MS, Jones JH (2004) Likelihood‐based inference for stochasticmodels of sexual network formation. Theor Popul Biol 65(4):413–22

    Article  MATH  Google Scholar 

  12. Harary F (1969) Graph theory. Addison‐Wesley,Reading

    Google Scholar 

  13. Hethcote H, Yorke JA (1984) Gonorrhea transmission dynamics andcontrol. Springer, New York

    MATH  Google Scholar 

  14. Hiltunen‐Back E, Haikala O et al (2003) Nationwide increase of Chlamydiatrachomatis infection in Finland – Highest rise among adolescent women and men. Sex Transm Dis30(10):737–741

    Google Scholar 

  15. Holme P, Edling CR et al (2004) Structure and time evolution of an Internetdating community. Social Netw 26(2):155–174

    Article  Google Scholar 

  16. Johnson AM, Mercer CH et al (2001) Sexual behaviour in Britain: partnerships,practices, and HIV risk behaviours. Lancet 358(9296):1835–1842

    Article  Google Scholar 

  17. Jones JH, Handcock MS (2003) An assessment of preferential attachment asa mechanism for human sexual network formation. Proc Biol Sci 270(1520):1123–1128

    Article  Google Scholar 

  18. Jones JH, Handcock MS (2003) Social networks: Sexual contacts and epidemicthresholds. Nature 423(6940):605–606; discussion 606

    Article  Google Scholar 

  19. Klovdahl AS (1985) Social networks and the spread of infectious diseases: theAIDS example. Soc Sci Med 21(11):1203–1216

    Article  Google Scholar 

  20. Kretzschmar M, Morris M (1996) Measures of concurrency in networks and thespread of infectious disease. Math Biosci 133(2):165–195

    Article  MATH  Google Scholar 

  21. Laumann EO, Gagnon JH et al (1994) The social organization ofsexuality. University of Chicago Press, Chicago

    Google Scholar 

  22. Lewin B (ed) (2000) Sex in Sweden. The Swedish National Institute of PublicHealth, Stockholm

    Google Scholar 

  23. Liljeros F, Edling CR et al (2001) The web of human sexual contacts. Nature411(6840):907–908

    Article  Google Scholar 

  24. Liljeros F, Edling CR et al (2003) Sexual networks: implications for thetransmission of sexually transmitted infections. Microbes Infect 5(2):189–196

    Article  Google Scholar 

  25. Lloyd AL, May RM (2001) Epidemiology. How viruses spread among computers andpeople. Science 292(5520):1316–1317

    Article  Google Scholar 

  26. Moody J (2002) The importance of relationship timing for diffusion. SocialForces 81(1):25–56

    Article  Google Scholar 

  27. Morris M (1993) Telling tails explain the discrepancy in sexual partnerreports. Nature 365(6445):437–440

    Article  Google Scholar 

  28. Morris M (ed) (2004) Network epidemiology: A handbook for survey designand data collection. Oxford University Press Inc, New York

    Google Scholar 

  29. Morris M, Goodreau S et al (2007) Sexual networks, concurrency, andSTD/HIV. In: Holmes KK, Sparling PF, Stamm WE (eds) Sexually transmitted diseases. McGraw‐Hill, New York

    Google Scholar 

  30. Morris M, Kretzschmar M (1995) Concurrent partnerships and transmissiondynamics in networks. Social Netw 17(3–4):299–318

    Article  Google Scholar 

  31. Morris M, Kretzschmar M (1997) Concurrent partnerships and the spread ofHIV. Aids 11(5):641–648

    Article  Google Scholar 

  32. Newman MEJ (2002) Assortative mixing in networks. Phys Rev Lett89(20):1–4

    Article  Google Scholar 

  33. Newman MEJ (2003) Mixing patterns in networks. Phys Rev E67(2):1–13

    Article  Google Scholar 

  34. Newman MEJ (2003) Properties of highly clustered networks. Phys Rev E68(2):026121

    Article  Google Scholar 

  35. Nordvik MK, Liljeros F (2006) Number of sexual encounters involvingintercourse and the transmission of sexually transmitted infections. Sex Transm Dis 33(6):342–349

    Article  Google Scholar 

  36. Nordvik MK, Liljeros F et al (2007) Spatial bridges and the spread ofChlamydia: the case of a county in Sweden. Sex Transm Dis 34(1):47–53

    Article  Google Scholar 

  37. Pastor-Satorras R, Vespignani A (2001) Epidemic dynamics and endemic states incomplex networks. Phys Rev E 63(066117):1–8

    Google Scholar 

  38. Pastor‐Satorras R, Vespignani A (2001) Epidemic spreading in scale-freenetworks. Phys Rev Lett 86:3200–3203

    Google Scholar 

  39. Pastor‐Satorras R, Vespignani A (2002) Epidemic dynamics in finite sizescale-free networks. Phys Rev E Stat Nonlin Soft Matter Phys 65(3 Pt 2A): 035108

    Google Scholar 

  40. Potterat JJ, Woodhouse DE et al (2004) Network dynamism: history and lessonsof the Colorado Springs study. In: Morris M (ed) Network epidemiology: A Handbook for survey d esign and data collection. Oxford University Press Inc, NewYork, pp 87–114

    Google Scholar 

  41. Price DJ (1976) A general theory of bibliometric and other cumulativeadvantage processes. J Am. Soc. Inform. Sci 27:292–306

    Google Scholar 

  42. Riolo CS, Koopman JS et al (2001) Methods and measures for the description ofepidemiologic contact networks. J Urban Health 78(3):446–457

    Article  Google Scholar 

  43. Schneeberger A, Mercer CH et al (2004) Scale-free networks and sexuallytransmitted diseases: a description of observed patterns of sexual contacts in Britain and Zimbabwe. Sex Transm Dis31(6):380–387

    Article  Google Scholar 

  44. Simon HA (1955) On a class of skew distribution functions. Biometrika42:425–440

    MathSciNet  MATH  Google Scholar 

  45. Szendroi B, CsĂĄnyi G (2004) Polynomial epidemics and clustering in contactnetworks. Proc Biol Sci Aug 7:271 Suppl 5:S364-6

    Google Scholar 

  46. Watts DJ, Strogatz SH (1998) Collective dynamics of‘small‐world’ networks. Nature 393(6684):440–442

    Article  Google Scholar 

  47. Wylie JL, Jolly A (2001) Patterns of chlamydia and gonorrhea infection insexual networks in Manitoba, Canada. Sex Transm Dis 28(1):14–24

    Article  Google Scholar 

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Liljeros, F. (2012). Human Sexual Networks. In: Meyers, R. (eds) Computational Complexity. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1800-9_98

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