Book contents
- Frontmatter
- Contents
- Introduction
- Participants
- Non-Participant Contributors
- Part 1 Transmissible diseases with long development times and vaccination strategies
- Part 2 Dynamics of immunity (development of disease within individuals)
- Part 3 Population heterogeneity (mixing)
- Modeling heterogeneous mixing in infectious disease dynamics
- Behavior change and non-homogeneous mixing
- Sources and use of empirical observations to characterise networks of sexual behaviour
- Invited Discussion
- Invited Discussion
- Per-contact probabilities of heterosexual transmission of HIV, estimated from partner study data
- Heterosexual spread of HIV with biased sexual partner selection
- Dynamic simulation of sexual partner networks: which network properties are important in sexually transmitted disease (STD) epidemiology?
- The spread of an STD on a dynamic network of sexual contacts
- Network measures for epidemiology
- Spatial heterogeneity and the spread of infectious diseases
- Data analysis for estimating risk factor effects using transmission models
- Homosexual role behaviour and the spread of HIV
- Homogeneity tests for groupings of AIDS patient classifications
- Risk factors for heterosexual transmission of HIV
- The effect of behavioural change on the prediction of R0 in the transmission of AIDS
- The saturating contact rate in epidemic models
- A Liapunov function approach to computing R0
- Stochastic models for the eradication of poliomyelitis: minimum population size for polio virus persistence
- Part 4 Consequences of treatment interventions
- Part 5 Prediction
The spread of an STD on a dynamic network of sexual contacts
Published online by Cambridge University Press: 04 August 2010
- Frontmatter
- Contents
- Introduction
- Participants
- Non-Participant Contributors
- Part 1 Transmissible diseases with long development times and vaccination strategies
- Part 2 Dynamics of immunity (development of disease within individuals)
- Part 3 Population heterogeneity (mixing)
- Modeling heterogeneous mixing in infectious disease dynamics
- Behavior change and non-homogeneous mixing
- Sources and use of empirical observations to characterise networks of sexual behaviour
- Invited Discussion
- Invited Discussion
- Per-contact probabilities of heterosexual transmission of HIV, estimated from partner study data
- Heterosexual spread of HIV with biased sexual partner selection
- Dynamic simulation of sexual partner networks: which network properties are important in sexually transmitted disease (STD) epidemiology?
- The spread of an STD on a dynamic network of sexual contacts
- Network measures for epidemiology
- Spatial heterogeneity and the spread of infectious diseases
- Data analysis for estimating risk factor effects using transmission models
- Homosexual role behaviour and the spread of HIV
- Homogeneity tests for groupings of AIDS patient classifications
- Risk factors for heterosexual transmission of HIV
- The effect of behavioural change on the prediction of R0 in the transmission of AIDS
- The saturating contact rate in epidemic models
- A Liapunov function approach to computing R0
- Stochastic models for the eradication of poliomyelitis: minimum population size for polio virus persistence
- Part 4 Consequences of treatment interventions
- Part 5 Prediction
Summary
The fact that AIDS is mainly a sexually transmitted disease has brought human sexual behaviour into the focus of attention and with it the underlying social structure of the population. The problem of how to incorporate the determinants of the sexual contact structure into a mathematical model of disease transmission has been one of the central questions in AIDS-modelling in recent years. While most of this work up to now has been based on the methodology of differential equations, lately there has been some interest in so-called network models. The basic idea of the network approach is that a population and its sexual contact structure can be described by a graph, where the vertices represent individuals and the edges existing sexual relations.
A simulation model based on the network approach has been developed in Kretzschmar et al. (1990,1994). The model describes a stochastic pair formation and dissolution process in a heterosexual population. Infection can be transmitted in contacts between an infected and a susceptible individual. A major problem in analyzing results from network simulations is the question of what are the appropriate quantities to measure and compare. I have chosen, amongst others, to look at the degree distribution of the ‘cumulative’ network over a given time of observation, because this can be determined with a certain accuracy in sociological surveys. One can then study how the number of infected individuals in the course of the epidemic depends on the mean and variance of this degree distribution.
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- Models for Infectious Human DiseasesTheir Structure and Relation to Data, pp. 281 - 282Publisher: Cambridge University PressPrint publication year: 1996