Article Outline
Glossary
Definition of the Subject
Introduction
NonâComplex 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 âsexually transmitted infectionâ is used instead of âsexually transmitted diseaseâ is that a state of infection and infectiousness do not necessarily result in disease.
Bibliography
Anderson R, May RM (1991) Infectious diseases of humans. Oxford UniversityPress, Oxford
BarabĂĄsi AL, Albert R (1999) Emergence of scaling in randomnetworks. Science 286(5439):509â512
Bearman PS, Moody J et al (2004) Chains of affection: The structure ofadolescent romantic and sexual networks. Am J Sociol 110(1):44â91
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 USA97:12385â12388
Colgate SA, Stanley EA et al (1989) Risk behaviorâbased model of the cubicgrowth of acquired immunodeficiency syndrome in the United States. Proc Nat Acad Sci US 86(12):4793â4797
Dezso Z, Barabasi AL (2002) Halting viruses in scale-free networks. Phys Rev EStat Nonlin Soft Matter Phys 65(5 Pt 2):055103
Diekman O, Heesterbeek JAP (2000) Mathematical epidemiology of infectiousdisease. John Wiley and Son, Chichester
Foulkes MA (1998) Advances in HIV/AIDS statistical methodology over the pastdecade. Stat Med 17(1):1â25
Frank O (1971) Statistical inference in graphs. FOA,Stockholm
Freiesleben de Blasio B, Svensson B et al (2007) Preferential attachement insexual networks. PNAS 104(26):10762â10767
Handcock MS, Jones JH (2004) Likelihoodâbased inference for stochasticmodels of sexual network formation. Theor Popul Biol 65(4):413â22
Harary F (1969) Graph theory. AddisonâWesley,Reading
Hethcote H, Yorke JA (1984) Gonorrhea transmission dynamics andcontrol. Springer, New York
HiltunenâBack E, Haikala O et al (2003) Nationwide increase of Chlamydiatrachomatis infection in Finland â Highest rise among adolescent women and men. Sex Transm Dis30(10):737â741
Holme P, Edling CR et al (2004) Structure and time evolution of an Internetdating community. Social Netw 26(2):155â174
Johnson AM, Mercer CH et al (2001) Sexual behaviour in Britain: partnerships,practices, and HIV risk behaviours. Lancet 358(9296):1835â1842
Jones JH, Handcock MS (2003) An assessment of preferential attachment asa mechanism for human sexual network formation. Proc Biol Sci 270(1520):1123â1128
Jones JH, Handcock MS (2003) Social networks: Sexual contacts and epidemicthresholds. Nature 423(6940):605â606; discussion 606
Klovdahl AS (1985) Social networks and the spread of infectious diseases: theAIDS example. Soc Sci Med 21(11):1203â1216
Kretzschmar M, Morris M (1996) Measures of concurrency in networks and thespread of infectious disease. Math Biosci 133(2):165â195
Laumann EO, Gagnon JH et al (1994) The social organization ofsexuality. University of Chicago Press, Chicago
Lewin B (ed) (2000) Sex in Sweden. The Swedish National Institute of PublicHealth, Stockholm
Liljeros F, Edling CR et al (2001) The web of human sexual contacts. Nature411(6840):907â908
Liljeros F, Edling CR et al (2003) Sexual networks: implications for thetransmission of sexually transmitted infections. Microbes Infect 5(2):189â196
Lloyd AL, May RM (2001) Epidemiology. How viruses spread among computers andpeople. Science 292(5520):1316â1317
Moody J (2002) The importance of relationship timing for diffusion. SocialForces 81(1):25â56
Morris M (1993) Telling tails explain the discrepancy in sexual partnerreports. Nature 365(6445):437â440
Morris M (ed) (2004) Network epidemiology: AÂ handbook for survey designand data collection. Oxford University Press Inc, New York
Morris M, Goodreau S et al (2007) Sexual networks, concurrency, andSTD/HIV. In: Holmes KK, Sparling PF, Stamm WE (eds) Sexually transmitted diseases. McGrawâHill, New York
Morris M, Kretzschmar M (1995) Concurrent partnerships and transmissiondynamics in networks. Social Netw 17(3â4):299â318
Morris M, Kretzschmar M (1997) Concurrent partnerships and the spread ofHIV. Aids 11(5):641â648
Newman MEJ (2002) Assortative mixing in networks. Phys Rev Lett89(20):1â4
Newman MEJ (2003) Mixing patterns in networks. Phys Rev E67(2):1â13
Newman MEJ (2003) Properties of highly clustered networks. Phys Rev E68(2):026121
Nordvik MK, Liljeros F (2006) Number of sexual encounters involvingintercourse and the transmission of sexually transmitted infections. Sex Transm Dis 33(6):342â349
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):47â53
Pastor-Satorras R, Vespignani A (2001) Epidemic dynamics and endemic states incomplex networks. Phys Rev E 63(066117):1â8
PastorâSatorras R, Vespignani A (2001) Epidemic spreading in scale-freenetworks. Phys Rev Lett 86:3200â3203
PastorâSatorras R, Vespignani A (2002) Epidemic dynamics in finite sizescale-free networks. Phys Rev E Stat Nonlin Soft Matter Phys 65(3 Pt 2A): 035108
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 87â114
Price DJ (1976) A general theory of bibliometric and other cumulativeadvantage processes. JÂ Am. Soc. Inform. Sci 27:292â306
Riolo CS, Koopman JS et al (2001) Methods and measures for the description ofepidemiologic contact networks. JÂ Urban Health 78(3):446â457
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):380â387
Simon HA (1955) On a class of skew distribution functions. Biometrika42:425â440
Szendroi B, CsĂĄnyi G (2004) Polynomial epidemics and clustering in contactnetworks. Proc Biol Sci Aug 7:271 Suppl 5:S364-6
Watts DJ, Strogatz SH (1998) Collective dynamics ofâsmallâworldâ networks. Nature 393(6684):440â442
Wylie JL, Jolly A (2001) Patterns of chlamydia and gonorrhea infection insexual networks in Manitoba, Canada. Sex Transm Dis 28(1):14â24
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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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