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
Invited Discussion
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
We have had three very interesting and very different papers this morning. So to be fair, I thought that I would raise for discussion three issues that are problems for both the data collectors and the transmission modellers.
The first problem is how to classify individuals into risk groups on the basis of their reported sexual behaviour. An individual's sexual behaviour is not a stable, easily measurable characteristic like gender or age. Furthermore, it cannot be viewed in isolation, because it involves the formation of partnerships and consequently is affected by the behaviour of others in the population. It may be extremely difficult to decide how to characterize an individual on the basis of reported risk behaviour at any one time; for example, should this be regarded as a constant or as a random variable (so that the reported behaviour at any moment is an observation on an underlying stochastic process)? Deciding upon a suitable classification scheme may require an analysis of the variability of the risk behaviour of each individual over a long period of time. It may be more appropriate to characterize this by averaging over a period of several years, rather than a particular, short time interval. It has been shown that the behaviour of individuals with high levels of sexual activity tends to be highly variable over time. Hence if individuals are characterised by their behaviour in a short time interval, then low activity individuals will generally be correctly classified (because of their low variability), but high-activity individuals may be misclassified.
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- Models for Infectious Human DiseasesTheir Structure and Relation to Data, pp. 263 - 264Publisher: Cambridge University PressPrint publication year: 1996