Dual Infection with HIV and Malaria Fuels the Spread of Both Diseases in Sub-Saharan Africa

University of North Carolina at Chapel Hill, North Carolina, United States
Science (Impact Factor: 33.61). 02/2007; 314(5805):1603-6. DOI: 10.1126/science.1132338
Source: PubMed


Mounting evidence has revealed pathological interactions between HIV and malaria in dually infected patients, but the public health implications of the interplay have remained unclear. A transient almost one-log elevation in HIV viral load occurs during febrile malaria episodes; in addition, susceptibility to malaria is enhanced in HIV-infected patients. A mathematical model applied to a setting in Kenya with an adult population of roughly 200,000 estimated that, since 1980, the disease interaction may have been responsible for 8,500 excess HIV infections and 980,000 excess malaria episodes. Co-infection might also have facilitated the geographic expansion of malaria in areas where HIV prevalence is high. Hence, transient and repeated increases in HIV viral load resulting from recurrent co-infection with malaria may be an important factor in promoting the spread of HIV in sub-Saharan Africa.

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Available from: Laith J Abu-Raddad, Jul 23, 2014
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    • "The World Health Organization [5] reports that people living with HIV are around 30 times more likely to develop TB than persons without HIV and also that TB is the most common occurring illness among people living with HIV. Other syndemics involving infectious diseases have been described in the literature: HIV and malaria syndemic [6]; the helminthic infections, malaria, and HIV/AIDS syndemic [7]; the pertussis, influenza, and tuberculosis syndemic [8]; and the HIV and sexually transmitted disease (STD) syndemic [9]. "
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    DESCRIPTION: Traditional biomedical approaches treat diseases in isolation, but the importance of synergistic disease interactions is now recognized. As a first step we present and analyze a simple coinfection model for two diseases affecting simultaneously a population. The host population is affected by the \emph{primary disease}, a long-term infection whose dynamics is described by a SIS model with demography, which facilitates individuals acquiring a second disease, \emph{secondary (or \emph{opportunistic}) disease}. The secondary disease is instead a short-term infection affecting only the primary-infected individuals. Its dynamics is also represented by a SIS model with no demography. To distinguish between short and long-term infection the complete model is written as a two time scales system. The primary disease acts at the slow time scale while the secondary disease does at the fast one, allowing a dimension reduction of the system and making its analysis tractable. We show that an opportunistic disease outbreak might change drastically the outcome of the primary epidemic process, although it does among the outcomes allowed by the primary disease. We have found situations in which either acting on the opportunistic disease transmission or recovery rates or controlling the susceptible and infected population size allow to eradicate/promote disease endemicity.
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    • "The same assumption is made for an individual actively infected with TB and is asymptomatic with HIV/AIDS. Similarly AIDS individuals are more infectious than individuals asymptomatic with HIV due to high viral load[13]. "
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    ABSTRACT: Tuberculosis is a common opportunistic infection that cause death in HIV/AIDS patients. Due to the high cost of treatment, protection against infection may be preferable in scarce resource settings. In this paper, we consider a de-terministic model incorporating protection from infection for both tuberculo-sis(TB) disease and HIV/AIDS. Two cases are considered, namely, the case of maximum protection against TB and the case of maximum protection against HIV/AIDS. In both cases, an endemic state is shown to exist provided that 5216 Joyce K. Nthiiri et al. the reproduction number is greater than unity. By use of a suitable Lya-punov function, the endemic states are shown to be globally asymptotically stable. Numerical simulations indicate that enhanced protection against a disease lowers new incidences of the disease, hence low disease prevalence rates. Therefore, public awareness campaign efforts on protective measures should be enhanced.
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    • "The antibody dependent enhancement in dengue represents a paradigmatic example, where cross-reactive antibodies following a previous infection increase the virulence of a subsequently infecting strain [7]. Other examples include influenza versus Streptococcus pneumoniae [9], and Malaria versus HIV [10]. Besides immunological mechanisms, ecological aspects can also represent a source of both competition and cooperation among pathogens. "
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    ABSTRACT: Different pathogens spreading in the same host population often generate complex co-circulation dynamics because of the many possible interactions between the pathogens and the host immune system, the host life cycle, and the space structure of the population. Here we focus on the competition between two acute infections and we address the role of host mobility and cross-immunity in shaping possible dominance/co-dominance regimes. Host mobility is modelled as a network of traveling flows connecting nodes of a metapopulation, and the two-pathogen dynamics is simulated with a stochastic mechanistic approach. Results depict a complex scenario where, according to the relation among the epidemiological parameters of the two pathogens, mobility can either be non-influential for the competition dynamics or play a critical role in selecting the dominant pathogen. The characterisation of the parameter space can be explained in terms of the trade-off between pathogen's spreading velocity and its ability to diffuse in a sparse environment. Variations in the cross-immunity level induce a transition between presence and absence of competition. The present study disentangles the role of the relevant biological and ecological factors in the competition dynamics, and provides relevant insights into the spatial ecology of infectious diseases.
    Scientific Reports 12/2014; 5. DOI:10.1038/srep07895 · 5.58 Impact Factor
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