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
Spatial heterogeneity and the spread of infectious diseases
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
Numerous factors influence the likelihood of contact between susceptible and infectious people, including participation in different social activities, cultural barriers such as membership of particular ethnic groups with associated customs, or separation due to geographic distance. These factors guarantee that contact among individuals within a population is distinctly nonrandom. Results from several theoretical studies show that nonrandom mixing among subgroups has many consequences for the outcome of epidemic spread, including affecting the time at which a disease is introduced into different subgroups and the speed of propagation and severity of an epidemic.
Most recent models for the spread of infectious diseases in human populations incorporate nonrandom patterns of mixing across subgroups and include a parameter for contact between groups that depends on the subgroups from which the susceptible and infective individuals derive. This parameter represents only the end result of the mixing process, leaving implicit the mechanism by which contact occurs. Here we describe a model that explicitly incorporates the mechanism for contact among individuals from different subgroups. Contact between individuals occurs as a result of the mobility of participants across either geographic or social space. Because it is simpler to visualize, we limit our discussion here to geographic mobility. Models for behavioral mobility are straightforward adaptations of this process (e.g. Sattenspiel and Castillo-Chavez 1990, Jacquez et al 1989).
Consider a population that is distributed among n regions. Individuals from region i leave the region at a rate σi per unit time. These visitors are then distributed among the n – 1 destinations with probabilities vij to each destination j.
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- Information
- Models for Infectious Human DiseasesTheir Structure and Relation to Data, pp. 286 - 289Publisher: Cambridge University PressPrint publication year: 1996
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