Monogamous networks and the spread of sexually transmitted diseases.

Patterns of sexual mixing and heterogeneity in the number of sexual partners can have a huge effect on the spread of a sexually transmitted disease (STD). The sexual mixing network identifies all partnerships within a population over a given period and is a powerful tool in the study of such infections. Previous models assumed all links within the network to be concurrent active partnerships. We present a novel modelling approach in which we adapt the notion of a sexual contact network to a monogamous population by allowing the nature of the links to change. We use the underlying network to represent potential sexual partnerships, only some of which are active at any one time. Thus serial monogamy can be modelled while maintaining the patterns of mixing displayed by the population.

[1]  A. Renton,et al.  Heterosexual HIV transmission and STD prevalence: predictions of a theoretical model. , 1998, Sexually transmitted infections.

[2]  M. Altmann,et al.  Susceptible-infected-removed epidemic models with dynamic partnerships , 1995, Journal of mathematical biology.

[3]  R. May,et al.  Infectious Diseases of Humans: Dynamics and Control , 1991, Annals of Internal Medicine.

[4]  R. Rothenberg,et al.  Using Social Network and Ethnographic Tools to Evaluate Syphilis Transmission , 1998, Sexually transmitted diseases.

[5]  M. Keeling,et al.  The effects of local spatial structure on epidemiological invasions , 1999, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[6]  J. Wylie,et al.  Patterns of Chlamydia and Gonorrhea Infection in Sexual Networks in Manitoba, Canada , 2001, Sexually transmitted diseases.

[7]  A. Ghani,et al.  Sampling biases and missing data in explorations of sexual partner networks for the spread of sexually transmitted diseases. , 1998, Statistics in medicine.

[8]  K Dietz,et al.  Epidemiological models for sexually transmitted diseases , 1988, Journal of mathematical biology.

[9]  M. Newman.  Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  M Kretzschmar,et al.  Measures of concurrency in networks and the spread of infectious disease. , 1996, Mathematical biosciences.

[11]  David A. Rand,et al.  Correlation Equations and Pair Approximations for Spatial Ecologies , 1999 .

[12]  Matt J Keeling,et al.  Modeling dynamic and network heterogeneities in the spread of sexually transmitted diseases , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[13]  K. Holmes,et al.  Sexual Mixing Patterns of Patients Attending Sexually Transmitted Diseases Clinics , 1996, Sexually transmitted diseases.

[14]  Wendy Macdowall,et al.  Sexual behaviour in Britain: partnerships, practices, and HIV risk behaviours , 2001, The Lancet.

[15]  R M May,et al.  The influence of concurrent partnerships on the dynamics of HIV/AIDS. , 1992, Mathematical biosciences.

[16]  K Dietz,et al.  The effect of pair formation and variable infectivity on the spread of an infection without recovery. , 1998, Mathematical biosciences.

[17]  J. Wadsworth,et al.  Estimating the sexual mixing patterns in the general population from those in people acquiring gonorrhoea infection: theoretical foundation and empirical findings. , 1995, Journal of epidemiology and community health.

[18]  Sexual histories, partnerships and networks associated with the transmission of gonorrhoea , 1998, International journal of STD & AIDS.

[19]  C. Mode.  A stochastic model for the development of an AIDS epidemic in a heterosexual population. , 1991, Mathematical biosciences.

[20]  L. Amaral,et al.  The web of human sexual contacts , 2001, Nature.

[21]  R. Rothenberg,et al.  Sociometric risk networks and risk for HIV infection. , 1997, American journal of public health.

[22]  R. Anderson,et al.  Sexually transmitted diseases and sexual behavior: insights from mathematical models. , 1996, The Journal of infectious diseases.

[23]  J. Potterat,et al.  Sexual networks and sexually transmitted infections: A tale of two cities , 2001, Journal of Urban Health.

[24]  R M May,et al.  A preliminary study of the transmission dynamics of the human immunodeficiency virus (HIV), the causative agent of AIDS. , 1986, IMA journal of mathematics applied in medicine and biology.

[25]  D. Rand,et al.  Correlation models for childhood epidemics , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[26]  D A Rand,et al.  A moment closure model for sexually transmitted disease transmission through a concurrent partnership network , 2000, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[27]  S. Kippax,et al.  Modelling the effect of combination antiretroviral treatments on HIV incidence , 2001, AIDS.

[28]  Samuel Leinhardt,et al.  Social Networks: A Developing Paradigm , 1977 .

[29]  Jacqueline McGlade,et al.  Advanced Ecological Theory , 1999 .

[30]  M. Kretzschmar,et al.  Modeling prevention strategies for gonorrhea and Chlamydia using stochastic network simulations. , 1996, American journal of epidemiology.

[31]  N. Ferguson,et al.  More Realistic Models of Sexually Transmitted Disease Transmission Dynamics: Sexual Partnership Networks, Pair Models, and Moment Closure , 2000, Sexually transmitted diseases.

[32]  M. Kretzschmar,et al.  Concurrent partnerships and the spread of HIV , 1997, AIDS.

[33]  A. Klovdahl,et al.  Networks and pathogens. , 2001, Sexually transmitted diseases.

[34]  M. Keeling,et al.  Disease evolution on networks: the role of contact structure , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[35]  S. Blower,et al.  HIV transmission in sexual networks: an empirical analysis , 1995, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[36]  Alden S. Klovdahl,et al.  Mapping a social network of heterosexuals at high risk for HIV infection , 1994, AIDS.