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Sexually Transmitted Diseases And Sexual Behavior: Insights From Mathematical Models

Garnett, G. P., Anderson, R. M.·1996·Journal of Infectious Diseases

Abstract

The major role of mathematical models of transmission dynamics and population biology of sexually transmitted diseases is helping understand the influence of the many biologic, social, and behavioral factors that influence the incidence or prevalence of infection. Various models can examine heterogeneity in sexual behavior and determine how individual variation influences epidemiologic pattern within a population. In the cases of heterogeneity in sex acts and in sex partner numbers, heterogeneity acts to enhance the likelihood of the persistence of infection. Also important is the pattern of mixing or sexual contact within a community. Assortative mixing promotes rapid spread in high-sexual-activity classes but results in a lower endemic equilibrium state compared with random mixing. In these models, each facet of behavior is treated separately. The obvious next goal of modeling is to meld processes together into a single mathematical framework; however, quantitative epidemiologic information on each factor is still needed.

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Metadata

Authors
Garnett, G. P., & Anderson, R. M.
Venue
Journal of Infectious Diseases
Published
1996-10-01
Volume
174
Issue
Supplement 2
Pages
S150-S161
ISSN
  • 0022-1899 (ISSN-L)
  • 1537-6613
OpenAlex
W2001295760
Cited by
172
References
0
FWCI
5.87
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Sexually Transmitted Diseases And Sexual Behavior: Insights From Mathematical Models. https://app.cochat.ai/papers/10.1093/infdis/174.supplement_2.s150
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Garnett, G. P., & Anderson, R. M. (1996). Sexually Transmitted Diseases And Sexual Behavior: Insights From Mathematical Models. Journal of Infectious Diseases. https://doi.org/10.1093/infdis/174.supplement_2.s150

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.