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
Dynamic simulation of sexual partner networks: which network properties are important in sexually transmitted disease (STD) epidemiology?
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
Conventional deterministic models of infection spread through populations aggregate individuals into compartments and study the dynamics of the resulting simplified system (Anderson and May 1991, Hethcote and Van Ark 1992). In this paper we explore whether knowledge of contact networks at an individual level can add to our epidemiological understanding in the particular setting of STDs. In the case of STDs the limited number and well defined nature of sexual contacts between people allows the description of the networks along which an STD can spread (Klovdahl et al. 1992, 1994). To this end a simple model describing the sexual behaviour of individuals is developed which generates sexual partner networks. The spread of a sexually transmitted disease (STD) through the population is simulated, and the characteristics of the network are related to the resultant spread of the STD. The model constructed contains many assumptions about the mechanisms controlling the sexual partnership formation behaviour, which are varied to generate a large range of possible networks. A central aim of this work is the development of the model as a tool to assist in the analysis of behavioural data. From simulations the parameters which are most influential in STD epidemiology can be identified. Samples can be taken from this network in a way which mirrors methods of sampling used in behavioural research.
The model
Individuals within the population, which can be varied in size, are treated as discrete entities with particular characteristics related to their sexual behaviour. Currently these include: sex; desired number of sexual partners per unit of time; desired duration of sexual partnerships; and a preference function for choosing sexual partners on the basis of their desired number of partners.
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- Models for Infectious Human DiseasesTheir Structure and Relation to Data, pp. 278 - 280Publisher: Cambridge University PressPrint publication year: 1996