Literature DB >> 27087760

A generating function approach to HIV transmission with dynamic contact rates.

E O Romero-Severson1, G D Meadors2, E M Volz3.   

Abstract

The basic reproduction number, R0, is often defined as the average number of infections generated by a newly infected individual in a fully susceptible population. The interpretation, meaning, and derivation of R0 are controversial. However, in the context of mean field models, R0 demarcates the epidemic threshold below which the infected population approaches zero in the limit of time. In this manner, R0 has been proposed as a method for understanding the relative impact of public health interventions with respect to disease eliminations from a theoretical perspective. The use of R0 is made more complex by both the strong dependency of R0 on the model form and the stochastic nature of transmission. A common assumption in models of HIV transmission that have closed form expressions for R0 is that a single individual's behavior is constant over time. In this paper we derive expressions for both R0 and probability of an epidemic in a finite population under the assumption that people periodically change their sexual behavior over time. We illustrate the use of generating functions as a general framework to model the effects of potentially complex assumptions on the number of transmissions generated by a newly infected person in a susceptible population. We find that the relationship between the probability of an epidemic and R0 is not straightforward, but, that as the rate of change in sexual behavior increases both R0 and the probability of an epidemic also decrease.

Entities:  

Keywords:  HIV; R0; branching process; generating functions; transmission model

Year:  2014        PMID: 27087760      PMCID: PMC4831738          DOI: 10.1051/mmnp/20149208

Source DB:  PubMed          Journal:  Math Model Nat Phenom        ISSN: 0973-5348            Impact factor:   4.157


  19 in total

1.  The web of human sexual contacts.

Authors:  F Liljeros; C R Edling; L A Amaral; H E Stanley; Y Aberg
Journal:  Nature       Date:  2001-06-21       Impact factor: 49.962

2.  Infection dynamics on scale-free networks.

Authors:  R M May; A L Lloyd
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2001-11-19

3.  On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations.

Authors:  O Diekmann; J A Heesterbeek; J A Metz
Journal:  J Math Biol       Date:  1990       Impact factor: 2.259

4.  Episodic HIV Risk Behavior Can Greatly Amplify HIV Prevalence and the Fraction of Transmissions from Acute HIV Infection.

Authors:  Xinyu Zhang; Lin Zhong; Ethan Romero-Severson; Shah Jamal Alam; Christopher J Henry; Erik M Volz; James S Koopman
Journal:  Stat Commun Infect Dis       Date:  2012-11-01

5.  Rates of HIV-1 transmission per coital act, by stage of HIV-1 infection, in Rakai, Uganda.

Authors:  Maria J Wawer; Ronald H Gray; Nelson K Sewankambo; David Serwadda; Xianbin Li; Oliver Laeyendecker; Noah Kiwanuka; Godfrey Kigozi; Mohammed Kiddugavu; Thomas Lutalo; Fred Nalugoda; Fred Wabwire-Mangen; Mary P Meehan; Thomas C Quinn
Journal:  J Infect Dis       Date:  2005-03-30       Impact factor: 5.226

6.  27 years of the HIV epidemic amongst men having sex with men in the Netherlands: an in depth mathematical model-based analysis.

Authors:  Daniela Bezemer; Frank de Wolf; Maarten C Boerlijst; Ard van Sighem; T Deirdre Hollingsworth; Christophe Fraser
Journal:  Epidemics       Date:  2010-04-09       Impact factor: 4.396

7.  Per-contact risk of human immunodeficiency virus transmission between male sexual partners.

Authors:  E Vittinghoff; J Douglas; F Judson; D McKirnan; K MacQueen; S P Buchbinder
Journal:  Am J Epidemiol       Date:  1999-08-01       Impact factor: 4.897

8.  Modeling prevention strategies for gonorrhea and Chlamydia using stochastic network simulations.

Authors:  M Kretzschmar; Y T van Duynhoven; A J Severijnen
Journal:  Am J Epidemiol       Date:  1996-08-01       Impact factor: 4.897

9.  Statistical analysis of the stages of HIV infection using a Markov model.

Authors:  I M Longini; W S Clark; R H Byers; J W Ward; W W Darrow; G F Lemp; H W Hethcote
Journal:  Stat Med       Date:  1989-07       Impact factor: 2.373

10.  Amplified transmission of HIV-1: comparison of HIV-1 concentrations in semen and blood during acute and chronic infection.

Authors:  Christopher D Pilcher; George Joaki; Irving F Hoffman; Francis E A Martinson; Clement Mapanje; Paul W Stewart; Kimberly A Powers; Shannon Galvin; David Chilongozi; Syze Gama; Matthew A Price; Susan A Fiscus; Myron S Cohen
Journal:  AIDS       Date:  2007-08-20       Impact factor: 4.177

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  1 in total

1.  Strong influence of behavioral dynamics on the ability of testing and treating HIV to stop transmission.

Authors:  Christopher J Henry; James S Koopman
Journal:  Sci Rep       Date:  2015-04-22       Impact factor: 4.379

  1 in total

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