Literature DB >> 19835430

Modeling TB and HIV co-infections.

Lih-Ing W Roeger1, Zhilan Feng, Carlos Castillo-Chavez.   

Abstract

Tuberculosis (TB) is the leading cause of death among individuals infected with the human immunodeficiency virus (HIV). The study of the joint dynamics of HIV and TB present formidable mathematical challenges due to the fact that the models of transmission are quite distinct. Furthermore, although there is overlap in the populations at risk of HIV and TB infections, the magnitude of the proportion of individuals at risk for both diseases is not known. Here, we consider a highly simplified deterministic model that incorporates the joint dynamics of TB and HIV, a model that is quite hard to analyze. We compute independent reproductive numbers for TB (R1) and HIV (R2) and the overall reproductive number for the system, R=max{R1, R2}. The focus is naturally (given the highly simplified nature of the framework) on the qualitative analysis of this model. We find that if R < 1 then the disease-free equilibrium is locally asymptotically stable. The TB-only equilibrium ET is locally asymptotically stable if R1 < 1 and R2 < 1. However, the symmetric condition, R1 < 1 and R2 > 1, does not necessarily guarantee the stability of the HIV-only equilibrium EH, and it is possible that TB can coexist with HIV when R2 > 1. In other words, in the case when R1 < 1 and R2 > 1 (or when R1 > 1 and R2 > 1), we are able to find a stable HIV/TB coexistence equilibrium. Moreover, we show that the prevalence level of TB increases with R2 > 1 under certain conditions. Through simulations, we find that i) the increased progression rate from latent to active TB in co-infected individuals may play a significant role in the rising prevalence of TB; and ii) the increased progression rates from HIV to AIDS have not only increased the prevalence level of HIV while decreasing TB prevalence, but also generated damped oscillations in the system.

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Year:  2009        PMID: 19835430     DOI: 10.3934/mbe.2009.6.815

Source DB:  PubMed          Journal:  Math Biosci Eng        ISSN: 1547-1063            Impact factor:   2.080


  16 in total

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4.  Coinfection Dynamics of Two Diseases in a Single Host Population.

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5.  Tuberculosis in Cape Town: An age-structured transmission model.

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Journal:  Epidemics       Date:  2015-10-20       Impact factor: 4.396

6.  The role of screening and treatment in the transmission dynamics of HIV/AIDS and tuberculosis co-infection: a mathematical study.

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7.  A metapopulation model of tuberculosis transmission with a case study from high to low burden areas.

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8.  Quantifying TB transmission: a systematic review of reproduction number and serial interval estimates for tuberculosis.

Authors:  Y Ma; C R Horsburgh; L F White; H E Jenkins
Journal:  Epidemiol Infect       Date:  2018-07-04       Impact factor: 4.434

Review 9.  How can mathematical models advance tuberculosis control in high HIV prevalence settings?

Authors:  R M G J Houben; D W Dowdy; A Vassall; T Cohen; M P Nicol; R M Granich; J E Shea; P Eckhoff; C Dye; M E Kimerling; R G White
Journal:  Int J Tuberc Lung Dis       Date:  2014-05       Impact factor: 2.373

10.  Changing risk behaviours and the HIV epidemic: a mathematical analysis in the context of treatment as prevention.

Authors:  Bojan Ramadanovic; Krisztina Vasarhelyi; Ali Nadaf; Ralf W Wittenberg; Julio S G Montaner; Evan Wood; Alexander R Rutherford
Journal:  PLoS One       Date:  2013-05-06       Impact factor: 3.240

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