Modeling gonorrhea and HIV co-interaction

Mushayabasa, Steady; Tchuenche, Jean M.; Bhunu, C. P.; Ngarakana-Gwasira, Ethel T.

doi:10.1016/j.biosystems.2010.09.008

Cited by 42 publications

(24 citation statements)

References 19 publications

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“…A variety of mathematical models for coinfections with multiple specific diseases, such as HIV/TB [43, 48, 6, 47], HIV/gonorrhea [40], HIV/malaria [1, 39], malaria and meningitis [29], general diseases [5, 7, 35, 28], and microparasites (viruses, bacteria, protozoa, fungi) [54, 10, 3, 4, 61], have been developed and analyzed in the past few years. Ferguson et al [16] and Kawaguchi et al [27] presented models to describe the coinfection of two serotypes of dengue virus in a human community.…”

Section: Introductionmentioning

confidence: 99%

Coinfection dynamics of two diseases in a single host population

Gao¹,

Porco²,

Ruan³

2016

Journal of Mathematical Analysis and Applications

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A susceptible-infectious-susceptible (SIS) epidemic model that describes the coinfection and cotransmission of two infectious diseases spreading through a single population is studied. The host population consists of two subclasses: susceptible and infectious, and the infectious individuals are further divided into three subgroups: those infected by the first agent/pathogen, the second agent/pathogen, and both. The basic reproduction numbers for all cases are derived which completely determine the global stability of the system if the presence of one agent/pathogen does not affect the transmission of the other. When the constraint on the transmissibility of the dually infected hosts is removed, we introduce the invasion reproduction number, compare it with two other types of reproduction number and show the uniform persistence of both diseases under certain conditions. Numerical simulations suggest that the system can display much richer dynamics such as backward bifurcation, bistability and Hopf bifurcation.

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Section: Introductionmentioning

confidence: 99%

Coinfection dynamics of two diseases in a single host population

Gao¹,

Porco²,

Ruan³

2016

Journal of Mathematical Analysis and Applications

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“…Our approach is to represent the spread of risk behaviour as a social contagion [51], [52] and to use a two-disease model [53], [54] in which we treat both HIV and risk behaviour as infectious processes. In the context of HIV, two-disease models have been developed previously to study co-infections by HIV and TB [55]–[64] or HIV and gonorrhoea [65], [66]. We are not aware of other studies that applied this approach to HIV and risk behaviour.…”

Section: Introductionmentioning

confidence: 99%

Changing Risk Behaviours and the HIV Epidemic: A Mathematical Analysis in the Context of Treatment as Prevention

et al. 2013

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BackgroundExpanding access to highly active antiretroviral therapy (HAART) has become an important approach to HIV prevention in recent years. Previous studies suggest that concomitant changes in risk behaviours may either help or hinder programs that use a Treatment as Prevention strategy.AnalysisWe consider HIV-related risk behaviour as a social contagion in a deterministic compartmental model, which treats risk behaviour and HIV infection as linked processes, where acquiring risk behaviour is a prerequisite for contracting HIV. The equilibrium behaviour of the model is analysed to determine epidemic outcomes under conditions of expanding HAART coverage along with risk behaviours that change with HAART coverage. We determined the potential impact of changes in risk behaviour on the outcomes of Treatment as Prevention strategies. Model results show that HIV incidence and prevalence decline only above threshold levels of HAART coverage, which depends strongly on risk behaviour parameter values. Expanding HAART coverage with simultaneous reduction in risk behaviour act synergistically to accelerate the drop in HIV incidence and prevalence. Above the thresholds, additional HAART coverage is always sufficient to reverse the impact of HAART optimism on incidence and prevalence. Applying the model to an HIV epidemic in Vancouver, Canada, showed no evidence of HAART optimism in that setting.ConclusionsOur results suggest that Treatment as Prevention has significant potential for controlling the HIV epidemic once HAART coverage reaches a threshold. Furthermore, expanding HAART coverage combined with interventions targeting risk behaviours amplify the preventive impact, potentially driving the HIV epidemic to elimination.

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“…Here we analyze the global stability by using a comparison theorem [21,22]. Theorem 2: The disease free equilibrium 0 E of the system…”

Section: Global Stability Of Disease Free Equilibrium (Dfe)mentioning

confidence: 99%

Epidemic Model of HIV/AIDS Transmission Dynamics with Different Latent Stages Based on Treatment

Singh¹

2016

AJAM

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The mathematical model for analyzing the transmission dynamics of HIV/AIDS epidemic with treatment is studied by considering the three latent compartments for slow, medium and fast progresses of developing the AIDS. By constructing the system of differential equations for the different population groups namely susceptible, three types of latent individuals, symptomatic stage group and full blown AIDS individuals, the mathematical analysis is carried out in order to understand the dynamics of disease spread. By determining the basic reproduction number (R 0 ), the model examines the two equilibrium points (i) the disease free equilibrium and (ii) the endemic equilibrium. It is established that if < 1, the disease free equilibrium is locally and globally asymptotically stable. The stability of endemic equilibrium has also been discussed.

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Modeling gonorrhea and HIV co-interaction

Cited by 42 publications

References 19 publications

Coinfection dynamics of two diseases in a single host population

Coinfection dynamics of two diseases in a single host population

Changing Risk Behaviours and the HIV Epidemic: A Mathematical Analysis in the Context of Treatment as Prevention

Epidemic Model of HIV/AIDS Transmission Dynamics with Different Latent Stages Based on Treatment

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