Literature DB >> 1875096

A general solution of the problem of mixing of subpopulations and its application to risk- and age-structured epidemic models for the spread of AIDS.

S Busenberg1, C Castillo-Chavez.   

Abstract

A central aspect in the study of the dynamics of sexually transmitted diseases is that of mixing. The study of the effects of social structure in disease dynamics has received considerable attention over the last few years as a result of the AIDS epidemic. In this paper, we formulate a generalization of the Blythe and Castillo-Chavez social/sexual framework for human interactions through the incorporation of age structure, and derive an explicit expression in terms of a preference function for the general solution to this formulation. We emphasize the role played by proportionate mixing, the only separable solution to this mixing framework, through the discussion of several specific cases, and we formulate an age-structured epidemic model for a single sexually active homosexual population, stratified by risk and age, with arbitrary risk- and age-dependent mixing as well as variable infectivity. In the special case of proportionate mixing in age and risk, an explicit expression for the basic reproductive number is computed.

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Mesh:

Year:  1991        PMID: 1875096     DOI: 10.1093/imammb/8.1.1

Source DB:  PubMed          Journal:  IMA J Math Appl Med Biol        ISSN: 0265-0746


  23 in total

1.  Disease risk mitigation: the equivalence of two selective mixing strategies on aggregate contact patterns and resulting epidemic spread.

Authors:  Benjamin R Morin; Charles Perrings; Simon Levin; Ann Kinzig
Journal:  J Theor Biol       Date:  2014-08-21       Impact factor: 2.691

2.  SIR DYNAMICS WITH ECONOMICALLY DRIVEN CONTACT RATES.

Authors:  Benjamin R Morin; Eli P Fenichel; Carlos Castillo-Chavez
Journal:  Nat Resour Model       Date:  2013-11       Impact factor: 1.182

3.  An age-structured epidemic model of rotavirus with vaccination.

Authors:  E Shim; Z Feng; M Martcheva; C Castillo-Chavez
Journal:  J Math Biol       Date:  2006-08-17       Impact factor: 2.259

4.  Age-structured homogeneous epidemic systems with application to the MSEIR epidemic model.

Authors:  Hisashi Inaba
Journal:  J Math Biol       Date:  2006-10-21       Impact factor: 2.259

5.  Pair formation.

Authors:  K P Hadeler
Journal:  J Math Biol       Date:  2011-07-08       Impact factor: 2.259

6.  SIS and SIR Epidemic Models Under Virtual Dispersal.

Authors:  Derdei Bichara; Yun Kang; Carlos Castillo-Chavez; Richard Horan; Charles Perrings
Journal:  Bull Math Biol       Date:  2015-10-21       Impact factor: 1.758

7.  Multi-patch and multi-group epidemic models: a new framework.

Authors:  Derdei Bichara; Abderrahman Iggidr
Journal:  J Math Biol       Date:  2017-11-17       Impact factor: 2.259

Review 8.  One model to rule them all? Modelling approaches across OneHealth for human, animal and plant epidemics.

Authors:  Adam Kleczkowski; Andy Hoyle; Paul McMenemy
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-06-24       Impact factor: 6.237

9.  Mathematical epidemiology is not an oxymoron.

Authors:  Fred Brauer
Journal:  BMC Public Health       Date:  2009-11-18       Impact factor: 3.295

10.  Pros and cons of estimating the reproduction number from early epidemic growth rate of influenza A (H1N1) 2009.

Authors:  Hiroshi Nishiura; Gerardo Chowell; Muntaser Safan; Carlos Castillo-Chavez
Journal:  Theor Biol Med Model       Date:  2010-01-07       Impact factor: 2.432

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