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 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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    • "A deterministic compartmental mathematical model was constructed , based on extension of earlier models (Abu-Raddad and Longini, 2008; Abu-Raddad et al., 2006), to describe the heterosexual transmission of HIV in a given population (Supplementary Material (SM)). The model consists of a system of coupled nonlinear differential equations, and stratifies the population according to HIV status, stage of infection and sexual risk group. "
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    ABSTRACT: Background: HIV prevalence is decreasing in much of sub-Saharan Africa (SSA), but the drivers of the decline are subject to much dispute. Using mathematical modeling as a tool for hypothesis generation, we demonstrate how the hypothesis that the drop in prevalence reflects declines in sexual risk behavior is self-consistent. We characterize these potential declines in terms of their scale, duration, and timing, and theorize on how small changes in sexual behavior at the individual-level could have driven large declines in HIV prevalence. Materials and methods: A population-level deterministic compartmental model was constructed to describe the HIV epidemics in 24 countries in SSA with sufficient trend data. The model was parameterized by national HIV prevalence and HIV natural history and transmission data. The temporal evolution of sexual risk behavior was characterized using established tools and uncertainty and sensitivity analyses on the results were conducted. Results: Declines in the scale of sexual risk behavior between 31.8% (Botswana) and 89.3% (Liberia) can explain the declining HIV prevalence across countries. The average decline across countries was 68.9%. The transition in sexual risk behavior lasted between 2.7 (Botswana) and 16.6 (Gabon) years with an average of 8.2 years. The turning point year of the transition occurred between 1993 (Burundi) and 2001 (Namibia), but clustered around 1995 for most countries. The uncertainty and sensitivity analyses affirmed our model predictions. Conclusion: The hypothesis that HIV prevalence declines in SSA have been driven by declines in sexual risk behavior is self-consistent and provides a convincing narrative for an evolving HIV epidemiology in this region. The hypothesized declines must have been remarkable in their intensity, rapidity, and synchronicity to explain the temporal trends in HIV prevalence. These findings provide contextual support for the hypothesis that changes in sexual behavior that materialized in the 1990s are a dominant driver of the recent decreases in HIV prevalence.
    Epidemics 09/2014; 8:9–17. DOI:10.1016/j.epidem.2014.06.001 · 1.87 Impact Factor
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