Abu-Raddad LJ, Longini IM Jr. No HIV stage is dominant in driving the HIV epidemic in sub-Saharan Africa
Statistical Center for HIV/AIDS Research & Prevention (SCHARP), Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, LE-400, P.O. Box 19024, Seattle, WA 98109, USA. AIDS (London, England)
(Impact Factor: 5.55).
05/2008; 22(9):1055-61. DOI: 10.1097/QAD.0b013e3282f8af84
To estimate the role of each of the HIV progression stages in fueling HIV transmission in sub-Saharan Africa by using the recent measurements of HIV transmission probability per coital per HIV stage in the Rakai study.
A mathematical model, parameterized by empirical data from the Rakai, Masaka, and Four-City studies, was used to estimate the proportion of infections due to each of the HIV stages in two representative epidemics in sub-Saharan Africa. The first setting represents a hyperendemic HIV epidemic (Kisumu, Kenya) whereas the second setting represents a generalized but not hyperendemic HIV epidemic (Yaoundé, Cameroon).
We estimate that 17, 51, and 32% of HIV transmissions in Kisumu were due to index cases in their acute, latent, and late stages, respectively. In Yaoundé, the fractions were 25, 44, and 31%. We found that the relative contribution of each stage varied with the epidemic evolution with the acute stage prevailing early on when the infection is concentrated in the high-risk groups with the late stage playing a major role as the epidemic matured and stabilized. The latent stage contribution remained largely stable throughout the epidemic and contributed about half of all transmissions.
No HIV stage dominated the epidemical though the latent stage provided the largest contribution. The role of each stage depends on the phase of the epidemic and on the prevailing levels of sexual risk behavior in the populations in which HIV is spreading. These findings may influence the design and implementation of different HIV interventions.
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Available from: Steven Goodreau
- "Based on data presented by Wawer et al. , Hollingsworth et al. have estimated that the infectivity of an individual after sero-conversion is highest for a period of about 2.9 months after infection . Other data sources  and other re-analyses of the Wawer data   differ in their numerical details but confirm higher infectiousness during acute infection. This period of acute infection is followed by a long period of stable chronic infectivity and a final, late-stage rise before death  . "
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ABSTRACT: Circular migrations are the periodic movement of individuals between multiple locations, observed in parts of sub-Saharan Africa. Relationships between circular migrations and HIV are complex, entailing interactions between migration frequency, partnership structure, and exposure to acute HIV infection. Mathematical modeling is a useful tool for understanding these interactions. Two modeling classes have dominated the HIV epidemiology and policy literature for the last decade: one a form of compartmental models, the other network models. We construct models from each class, using ordinary differential equations and exponential random graph models, respectively. Our analysis suggests that projected HIV prevalence is highly sensitive to the choice of modeling framework. Assuming initial equal HIV prevalence across locations, compartmental models show no association between migration frequency and HIV prevalence or incidence, while network models show that migrations at frequencies shorter than the acute HIV period predict greater HIV incidence and prevalence compared to longer migration periods. These differences are statistically significant when network models are extended to incorporate a requirement for migrant men's multiple partnerships to occur in different locations. In settings with circular migrations, commonly-used forms of compartmental models appear to miss key components of HIV epidemiology stemming from interactions of relational and viral dynamics.
Available from: Diego Cuadros
- "A deterministic compartmental mathematical model was constructed based on extension of earlier models [16-18] to describe the heterosexual transmission of HIV in a given population . 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: The geographical structure of an epidemic is ultimately a consequence of the
drivers of the epidemic and the population susceptible to the infection. The
‘know your epidemic’ concept recognizes this geographical
feature as a key element for identifying populations at higher risk of HIV
infection where prevention interventions should be targeted. In an effort to
clarify specific drivers of HIV transmission and identify priority
populations for HIV prevention interventions, we conducted a comprehensive
mapping of the spatial distribution of HIV infection across sub-Saharan
The main source of data for our study was the Demographic and Health Survey
conducted in 20 countries from SSA. We identified and compared spatial
clusters with high and low numbers of HIV infections in each country using
Kulldorff spatial scan test. The test locates areas with higher and lower
numbers of HIV infections than expected under spatial randomness. For each
identified cluster, a likelihood ratio test was computed. A P-value
was determined through Monte Carlo simulations to evaluate the statistical
significance of each cluster.
Our results suggest stark geographic variations in HIV transmission patterns
within and across countries of SSA. About 14% of the population in SSA is
located in areas of intense HIV epidemics. Meanwhile, another 16% of the
population is located in areas of low HIV prevalence, where some behavioral
or biological protective factors appear to have slowed HIV transmission.
Our study provides direct evidence for strong geographic clustering of HIV
infection across SSA. This striking pattern of heterogeneity at the
micro-geographical scale might reflect the fact that most HIV epidemics in
the general population in SSA are not far from their epidemic threshold. Our
findings identify priority geographic areas for HIV programming, and support
the need for spatially targeted interventions in order to maximize the
impact on the epidemic in SSA.
Available from: link.springer.com
- "Observations of viremia during early HIV infection have revealed an early peak in viral load that is 2 logs (±1 log) higher than the setpoint viral load [48-52]. The time of the acute phase peak has been reported in the range of 12–31 days  or 5–19 days . "
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Population transmission models of antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) use simplistic assumptions – typically constant, homogeneous rates – to represent the short-term risk and long-term effects of drug resistance. In contrast, within-host models of drug resistance allow for more detailed dynamics of host immunity, latent reservoirs of virus, and drug PK/PD. Bridging these two levels of modeling detail requires an understanding of the “levers” – model parameters or combinations thereof – that change only one independent observable at a time. Using the example of accidental tenofovir-based pre-exposure prophyaxis (PrEP) use during HIV infection, we will explore methods of implementing host heterogeneities and their long-term effects on drug resistance.
We combined and extended existing models of virus dynamics by incorporating pharmacokinetics, pharmacodynamics, and adherence behavior. We identified two “levers” associated with the host immune pressure against the virus, which can be used to independently modify the setpoint viral load and the shape of the acute phase viral load peak. We propose parameter relationships that can explain differences in acute and setpoint viral load among hosts, and demonstrate their influence on the rates of emergence and reversion of drug resistance. The importance of these dynamics is illustrated by modeling long-lived latent reservoirs of virus, through which past intervals of drug resistance can lead to failure of suppressive drug regimens. Finally, we analyze assumptions about temporal patterns of drug adherence and their impact on resistance dynamics, finding that with the same overall level of adherence, the dwell times in drug-adherent versus not-adherent states can alter the levels of drug-resistant virus incorporated into latent reservoirs.
We have shown how a diverse range of observable viral load trajectories can be produced from a basic model of virus dynamics using immunity-related “levers”. Immune pressure, in turn, influences the dynamics of drug resistance, with increased immune activity delaying drug resistance and driving more rapid return to dominance of drug-susceptible virus after drug cessation. Both immune pressure and patterns of drug adherence influence the long-term risk of drug resistance. In the case of accidental PrEP use during infection, rapid transitions between adherence states and/or weak immunity fortifies the “memory” of previous PrEP exposure, increasing the risk of future drug resistance. This model framework provides a means for analyzing individual-level risks of drug resistance and implementing heterogeneities among hosts, thereby achieving a crucial prerequisite for improving population-level models of drug resistance.
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