Epidemiology and Social
No HIV stage is dominant in driving the HIV epidemic in sub-Saharan Africa
Abu-Raddad, Laith Ja,b,c; Longini, Ira M Jra,b,d
From the aVaccine and Infectious Disease Institute, USA
bProgram of Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center, Seattle, USA
cCenter for Studies in Demography and Ecology, USA
dDepartment of Biostatistics, School of Public Health and Community Medicine, University of Washington, Seattle, Washington, USA.
Received 7 June, 2007
Revised 20 December, 2007
Accepted 9 January, 2008
Correspondence to Laith J. Abu-Raddad, 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. E-mail: email@example.com
Objective: 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.
Methods: 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).
Results: 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.
Conclusion: 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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The epidemiological role of the HIV progression stages – acute, latent, and late – in the heterosexual HIV epidemic in sub-Saharan Africa has been a subject of debate over the last few years. On the basis of the observed high concentrations of HIV-1 RNA in semen and blood plasma during acute infection , and the relationship between plasma viral load and transmission probability per coital act , it has been argued that the acute hyperinfectiousness may play a large and disproportionate role in the epidemic  and that newly infected index partners may account for as much as half of HIV transmissions . Conversely, other researchers have argued that the moderate levels of viral load in the late stage and its relatively long duration of roughly 2 years, suggest that the late stage may be dominant in the infectious spread citing an observed decrease in HIV transmission following the administration of the highly active antiretroviral therapy (HAART) , and an analysis of the San Francisco epidemic [5,6]. Lastly, growing evidence suggests that HIV coinfections such as herpes simplex viruses type 2 (HSV-2)  and malaria , which cause transient increases in HIV viral load predominantly in the latent stage [3,9,10], by virtue of the long duration of this stage and status of the immune system ; may be among the leading causes of the epidemic in sub-Saharan Africa.
The debate over the role of each stage is essentially a debate over which interventions are effective in controlling the HIV spread. Many HIV interventions such as behavioral interventions, male circumcision, and topical microbicides can generally be effective in curtailing HIV acquisition and transmission irrespective of the HIV stage of the index partner. However, the effectiveness of other interventions such as cytotoxic lymphocyte (CTL) vaccines and HAART, can strongly depend on the infectiousness profile of the index partner and the magnitude of the contribution of each of the HIV stages in fueling transmission.
Previous work has examined various issues of the role of HIV natural history in the infectious spread [5,6,11–17]. Two key conclusions emerged. First, the acute stage of infection plays a disproportionate role relative to its duration when the epidemic is concentrated in high-risk groups such as that of San Francisco [14,15]. Second, the relative weight of the contribution of each of the HIV stages depends on the mathematical relation between HIV transmission probability per coital act and HIV viral load. A linear relationship, in which transmission probability increases with viral load at a fixed rate, implies that the epidemic is dominated by HIV seropositives in their acute or late stages. Meanwhile, a logarithmic relationship where transmission probability increases with viral load at a slowing rate, indicates that the HIV latent stage plays the leading role [11,18]. Considering the evidence from discordant partnerships in the Rakai study , mother-to-child transmission , and in-vitro plasma infectivity , it is believed that the relationship between transmission probability and viral load is closer to being logarithmic.
Recently, the Rakai study  provided the most careful and direct measurement of HIV transmission probability per coital act per HIV stage of progression. This empirical evidence affirmed earlier work that suggested a U-shaped curve for HIV transmission probability per act, per stage of progression . With the publication of the Rakai results, we have the necessary data to conduct an assessment of the epidemiological role of the different stages. We have carried out this assessment through simulations of two HIV epidemics in sub-Saharan Africa with sharply different prevalence levels. We also investigated whether the presence of biological cofactors such as male circumcision  or genital herpes , may affect our predictions as they introduce heterogeneity in HIV acquisition. Finally, we highlighted some of the implications of our predictions on HIV intervention policies that aim to target effects arising from specific HIV stages.
A deterministic compartmental model was constructed to describe the HIV epidemic in Kisumu, Kenya and Yaoundé, Cameroon [Supporting information is available from author on request.]. The model assumptions are listed in Table 1 along with their references, and the model stratifies the population into compartments according to status and stage of HIV infection, sexual-risk activity group, and exposure to a biological cofactor that affects HIV acquisition using five coupled nonlinear ordinary differential equations for each of the four risk groups in the model [Supporting information available from author on request.]. Three kinds of biological cofactors are considered: a biological cofactor that reduces susceptibility to HIV acquisition in all risk groups (male circumcision-like cofactor ), a biological cofactor that enhances susceptibility to HIV acquisition in all risk groups (HSV-2-like cofactor ), and a biological cofactor that enhances susceptibility to HIV acquisition only in high-risk groups (bacterial sexually transmitted infections (STIs)-like cofactor ). Note that bacterial STIs tend to cluster in high-risk groups but have relatively lower prevalence in the general population .
HIV pathogenesis is described by three stages of acute, latent, and late, and our model assumes negligible antiretroviral therapy coverage in the population. The transmission probabilities are extracted from the measurements of Wawer et al.  by collapsing the substrata in their classification of incident, prevalent, and late stages into the three stages of acute, latent, and late . There are, however, two distinctions with respect to the Wawer et al. analysis of the stages. We chose the first stage in which both seroconversions occurred as our definition for the acute stage and merged the rest of the incident stages with the latent stage. The other distinction is that we recalculated the transmission probabilities using the binomial model for the partnership transmission probability assuming uniform partnership duration and coital frequency . This was done as a means to correct for the bias in Wawer et al. analysis toward underestimating the transmission probability per coital act particularly in the acute stage. Wawer et al. estimated the transmission probability by dividing the number of transmissions by the reported number of coital acts.
The durations of the acute, latent, and late stages are assumed to be 2.5 months (acute), 7.59 years (latent), and 2 years (late). These choices are based on the transmission probability classification in Wawer et al. , and the measured time from seroconversion to death in the Masaka cohort in Uganda .
The population is divided into four sexual-risk classes of low, low-to-moderate, moderate-to-high, and high-risk groups. The mixing between the risk groups has assortative and proportional components. The risk groups are defined using the data of the Four-City study . Female sex workers and their male clients constitute the high-risk group. Meanwhile, the populations with more than one nonspousal, one nonspousal, and no nonspousal partnerships in the previous year characterize the intermediate-to-high, low-to-intermediate, and low-risk groups respectively. The duration of the sexual lifespan is set at 35 years to conform with the 15–49 years age groups that is typically used to define the sexually active population by the WHO as well as other HIV studies .
Results and analysis
Figure 1 shows our predictions for Kisumu in terms of prevalence, proportion of infections due to each HIV stage (PIS), as well as the cumulative PIS since 1980. The PISv is defined as the fraction of incident HIV infections transmitted from index partners in their ν stage, relative to the total incident infections, at any given time in the epidemic. The
is defined as the fraction of the cumulative incident HIV infections since 1980 that are due to transmissions from index partners in their ν stage, relative to the total cumulative incident infections since 1980, at any given time in the epidemic.
In the early phase of the epidemic, the acute stage was responsible for almost half of HIV infections, but the contribution declined steadily as the epidemic evolved to a minimum of 8% earlier this decade. In contrast, the late-stage contribution was at 15% in 1980, but increased rapidly in the early 1990s as the high-risk population that acquired the infection in the 1980s progressed to late stage. The latent stage, however, maintained a contribution of about 50% most of the time after starting at 39%. Cumulatively from 1980 to 2007, 17, 51, and 32% of the infections were due to the acute, latent, and late stages, while by the endemic equilibrium around the year 2050, with 13, 52, and 35% were due to the three stages respectively. We found this pattern to hold within each risk group though the higher the risk the more pronounced is the impact of acute infection early in the epidemic.
Equation (Uncited)Image Tools
Our predictions for Yaoundé, which indicate a similar pattern to that of Kisumu are shown in Fig. 2. The acute stage nevertheless played a substantially larger role consistent with the epidemic being more concentrated in the high-risk groups as opposed to Kisumu. Cumulatively from 1980 to 2007, 25, 44, and 31% of the infections were due to the acute, latent, and late stages, while by the endemic equilibrium around the year 2080, 18, 46, and 36% were due to the three stages respectively. It must be stressed here that the different roles of the stages in Kisumu as compared with Yaoundé are not behind the differences in HIV seroprevalence between the two cities. Other factors such as male circumcision and genital herpes may explain a large part of the gap in prevalence .
The large contribution of acute infection in both settings early in the epidemic is due to the relatively large fraction of the infected population that is in the acute stage; the higher infectivity of this population which is strongly amplified by the high risk of the index and receiving partners, and the large susceptible population where most potentially infectious contacts end up as susceptible people. The fractions of the HIV-infected population that are in each of the HIV stages in Kisumu and Yaoundé are displayed in Fig. 3. The time variation of the populations in the acute and advanced stages explains in part the large contribution of the acute stage early in the epidemic and the acceleration of the advanced stage contribution as the epidemic matured.
We performed two kinds of sensitivity and uncertainty analyses to assess the robustness of our predictions first to alternative assumptions in the model structure and second to the uncertainty in the behavioral and HIV progression parameters used to parameterize the model [Supporting information available from author on request.]. We found that our predictions are largely invariable both to the structural changes in the model and to the assumed variations in the behavioral and progression parameters.
Equation (Uncited)Image Tools
The above results highlight how the roles of acute and late stages highly depend on the epidemic phase and level of sexual risk behavior in the groups where HIV is spreading, and that no HIV stage dominated the infectious spread in sub-Saharan Africa where the infection has been spreading generally in both the high-risk groups as well as the general population. Yet, the latent stage appears to be the largest driver of the epidemic in sub-Saharan Africa as a consequence of the prolonged duration of this stage that, despite the lower infectivity per act, will contribute to the majority of exposures, simply because of the longer duration. This result indicates the importance of assessing the determinants of transmission probability and levels of viral load in this stage, including the role of HIV coinfections [9,10] as well as the role of acute infection in dictating the virologic set point a few months following the seroconversion .
Equation (Uncited)Image Tools
We found that the HIV stage dynamics is largely determined by the timing between HIV transmissions across sexual partnerships. When new partnerships are formed rapidly, that is, the higher is the sexual risk behavior, the role of the acute stage is more pronounced, as the index partner is more likely to transmit the infection to more partners and partners of partners, although still being in the short-acute stage. Accordingly, the acute stage contributed substantially more to the epidemic in Yaoundé compared with Kisumu, as the epidemic expansion was more concentrated in the high-risk groups [30,31].
Equation (Uncited)Image Tools
Even though the contribution of acute infection is small in a mature epidemic, its contribution in stable partnerships formed before seroconversion of the index partner, and with the susceptible partner being monogamous, is still substantial at 40% . Nevertheless, the population characterized by such partnerships is only part of the population in which HIV is spreading. Acute infection transmissions in the low-risk population face ‘dead-ends’ as it is much less likely that index cases will form new partnerships during the short-acute stage.
Our results indicate that acute infection can still fuel a large fraction of HIV infections in epidemics concentrated in high-risk groups such as during the early phase of the epidemics in Kisumu and Yaoundé. This result corroborates the findings of the impact of acute infection in studies conducted in high-risk groups [33–35]. This also highlights how designing HIV interventions that aim to target the role of each of the stages strongly depends on the nature of the risk groups where the intervention is to be implemented. The dependence on the level of risk behavior may explain the varying clustering of acute infection individuals based on the mode of transmission, whether intravenous drug use (IDU) or homosexual or heterosexual contacts, that has been seen in HIV molecular epidemiology studies .
Our calculations assume a reduction in sexual activity in the late stage, in terms of reduction in coital frequency, by 30% based on the observed reduction in coital frequency in the Rakai study  (Table 1). It is possible that in addition to a reduction in coitus, late-stage infected people may also reduce their risk of exposure through changes in their sexual partnership formation structure in view of the increased presence of comorbidities in this stage. Such reduction would reduce the contribution of the late-stage infection and increase that of the acute and latent stages.
The predictions presented here assume a distribution from onset of infection to AIDS and death in sub-Saharan Africa according to the direct measurements of the Masaka cohort . Recent evidence suggests that the progression from infection to AIDS and death depends on the HIV-1 virus subtype where, for example, infection by subtype D leads to faster progression than infection with subtype A [36,37]. Such differences in progression rates can affect our predictions where a faster progression to AIDS may reduce the contribution of the latent stage as it is more likely to shorten the duration of this stage than those of the acute and advanced stages [Supporting information available from author on request.].
Our results suggests the importance of long-term follow-up for HIV CTL vaccine trials  as a beneficial impact of the vaccine only in the first 2 years post-infection may not be sufficient to capture the effectiveness of the vaccine as an intervention tool. This would also help clarify the role of acute infection in dictating the level of viral load in the latent stage. The large contribution of the latent stage alludes that interventions that target the determinants of viremia in this stage , such as HIV coinfections [7,8], may be beneficial in controlling HIV transmissions. The relatively large contribution of the late stage in mature epidemics, such as in sub-Saharan Africa, signifies that HAART may indeed be helpful in reducing HIV spread particularly if administered earlier than is the current practice. Detailed studies of the impact and effectiveness of these interventions might be of utility for prioritizing prevention strategies.
In conclusion, the lack of dominance of any stage in the epidemic hints that it is unlikely a single intervention measure targeting a specific HIV stage would be very effective against HIV. Only synergistic interventions, tailored to the nature and phase of the epidemic in a given community, can effectively curtail HIV spread. The sizable contribution of each of the stages suggests that intervention measures that reduce HIV acquisition and transmission irrespective of HIV stage, such as male circumcision  or possibly HSV-2 suppressive therapy , may provide effective tools in controlling HIV spread.
We thank Drs Steven M. Goodreau and Susan Cassels for valuable discussions. This publication resulted from the research supported by the University of Washington Center for AIDS Research (CFAR), an NIH-funded program (P30 AI 27757).
There are no conflicts of interest.
1. Pilcher CD, Tien HC, Eron JJ Jr, Vernazza PL, Leu SY, Stewart PW, et al
. Brief but efficient: acute HIV infection and the sexual transmission of HIV. J Infect Dis 2004; 189:1785–1792.
2. Quinn TC, Wawer MJ, Sewankambo N, Serwadda D, Li CJ, Wabwire-Mangen F, et al
. Viral load and heterosexual transmission of human immunodeficiency virus type 1. N Engl J Med 2000; 342:921–929.
3. Cohen MS, Pilcher CD. Amplified HIV transmission and new approaches to HIV prevention. J Infect Dis 2005; 191:1391–1393.
4. Montaner JS, Hogg R, Wood E, Kerr T, Tyndall M, Levy AR, Harrigan PR. The case for expanding access to highly active antiretroviral therapy to curb the growth of the HIV epidemic. Lancet 2006; 368:531–536.
5. Rapatski BL, Suppe F, Yorke JA. HIV epidemics driven by late disease stage transmission. J Acquir Immune Defic Syndr 2005; 38:241–253.
6. Rapatski BL, Suppe F, Yorke JA. Reconciling different infectivity estimates for HIV-1. J Acquir Immune Defic Syndr 2006; 43:253–256.
7. Nagot N, Delany-Moretlwe S, Mayaud P. Antiherpetic therapy for HIV infection: linking prevention and care. Future HIV Therapy 2007; 1:131–136.
8. Abu-Raddad LJ, Patnaik P, Kublin JG. Dual infection with HIV and malaria fuels the spread of both diseases in sub-Saharan Africa. Science 2006; 314:1603–1606.
9. Kublin JG, Patnaik P, Jere CS, Miller WC, Hoffman IF, Chimbiya N, et al
. Effect of Plasmodium falciparum
malaria on concentration of HIV-1-RNA in the blood of adults in rural Malawi: a prospective cohort study. Lancet 2005; 365:233–240.
10. Nagot N, Ouedraogo A, Foulongne V, Konate I, Weiss HA, Vergne L, et al
. Reduction of HIV-1 RNA levels with therapy to suppress herpes simplex virus. N Engl J Med 2007; 356:790–799.
11. Coutinho FA, Lopez LF, Burattini MN, Massad E. Modelling the natural history of HIV infection in individuals and its epidemiological implications. Bull Math Biol 2001; 63:1041–1062.
12. Hyman JM, Li J, Stanley EA. The differential infectivity and staged progression models for the transmission of HIV. Math Biosci 1999; 155:77–109.
13. Ahlgren DJ, Gorny MK, Stein AC. Model-based optimization of infectivity parameters: a study of the early epidemic in San Francisco. J Acquir Immune Defic Syndr 1990; 3:631–643.
14. Jacquez JA, Koopman JS, Simon CP, Longini IM Jr. Role of the primary infection in epidemics of HIV infection in gay cohorts. J Acquir Immune Defic Syndr 1994; 7:1169–1184.
15. Koopman JS, Jacquez JA, Welch GW, Simon CP, Foxman B, Pollock SM, et al
. The role of early HIV infection in the spread of HIV through populations. J Acquir Immune Defic Syndr Hum Retrovirol 1997; 14:249–258.
16. Levin BR, Bull JJ, Stewart FM. The intrinsic rate of increase of HIV/AIDS: epidemiological and evolutionary implications. Math Biosci 1996; 132:69–96.
17. Xiridou M, Geskus R, de Wit J, Coutinho R, Kretzschmar M. Primary HIV infection as source of HIV transmission within steady and casual partnerships among homosexual men. AIDS 2004; 18:1311–1320.
18. Massad E, Burattini MN, Coutinho FA, Lopez LF. Which phase of the natural history of HIV infection is more transmissible? Int J STD AIDS 2002; 13:430–431.
19. Garcia PM, Kalish LA, Pitt J, Minkoff H, Quinn TC, Burchett SK, et al
. Maternal levels of plasma human immunodeficiency virus type 1 RNA and the risk of perinatal transmission. Women and Infants Transmission Study Group. N Engl J Med 1999; 341:394–402.
20. Vella S, Galluzzo MC, Giannini G, Pirillo MF, Andreotti M, Tomino C, et al
. Plasma HIV-1 copy number and in vitro infectivity of plasma prior to and during combination antiretroviral treatment. Antiviral Res 2000; 47:189–198.
21. Wawer MJ, Gray RH, Sewankambo NK, Serwadda D, Li X, Laeyendecker O, et al
. Rates of HIV-1 transmission per coital act, by stage of HIV-1 infection, in Rakai, Uganda. J Infect Dis 2005; 191:1403–1409.
22. Leynaert B, Downs AM, de Vincenzi I. Heterosexual transmission of human immunodeficiency virus: variability of infectivity throughout the course of infection. European Study Group on Heterosexual Transmission of HIV. Am J Epidemiol 1998; 148:88–96.
23. Auvert B, Taljaard D, Lagarde E, Sobngwi-Tambekou J, Sitta R, Puren A. Randomized, controlled intervention trial of male circumcision for reduction of HIV infection risk: the ANRS 1265 Trial. PLoS Med 2005; 2:e298.
24. Freeman EE, Weiss HA, Glynn JR, Cross PL, Whitworth JA, Hayes RJ. Herpes simplex virus 2 infection increases HIV acquisition in men and women: systematic review and meta-analysis of longitudinal studies. AIDS 2006; 20:73–83.
25. Korenromp EL, de Vlass SJ, Nagelkerke NJ, Habbema JD. Estimating the magnitude of STD cofactor effects on HIV transmission: how well can it be done? Sex Transm Dis 2001; 28:613–621.
26. Brunham RC, Plummer FA. A general model of sexually transmitted disease epidemiology and its implications for control. Med Clin North Am 1990; 74:1339–1352.
27. Morgan D, Whitworth J. The natural history of HIV-1 infection in Africa. Nat Med 2001; 7:143–145.
28. Buve A, Carael M, Hayes RJ, Auvert B, Ferry B, Robinson NJ, et al
. The multicentre study on factors determining the differential spread of HIV in four African cities: summary and conclusions. AIDS 2001; 15(Suppl 4):S127–S131.
29. Hubert JB, Burgard M, Dussaix E, Tamalet C, Deveau C, Le Chenadec J, et al
. Natural history of serum HIV-1 RNA levels in 330 patients with a known date of infection. The SEROCO Study Group. AIDS 2000; 14:123–131.
30. Buve A, Carael M, Hayes RJ, Auvert B, Ferry B, Robinson NJ, et al
. Multicentre study on factors determining differences in rate of spread of HIV in sub-Saharan Africa: methods and prevalence of HIV infection. AIDS 2001; 15(Suppl 4):S5–S14.
31. Morison L, Weiss HA, Buve A, Carael M, Abega SC, Kaona F, et al
. Commercial sex and the spread of HIV in four cities in sub-Saharan Africa. AIDS 2001; 15(Suppl 4):S61–S69.
32. Hayes RJ, White RG. Amplified HIV transmission during early-stage infection. J Infect Dis 2006; 193:604–605, author reply 605–606.
33. Brenner BG, Roger M, Routy JP, Moisi D, Ntemgwa M, Matte C, et al
. High rates of forward transmission events after acute/early HIV-1 infection. J Infect Dis 2007; 195:951–959.
34. Pao D, Fisher M, Hue S, Dean G, Murphy G, Cane PA, et al
. Transmission of HIV-1 during primary infection: relationship to sexual risk and sexually transmitted infections. AIDS 2005; 19:85–90.
35. Yerly S, Vora S, Rizzardi P, Chave JP, Vernazza PL, Flepp M, et al
. Acute HIV infection: impact on the spread of HIV and transmission of drug resistance. AIDS 2001; 15:2287–2292.
36. Kiwanuka N, Laeyendecker O, Robb M, Kigozi G, Arroyo M, McCutchan F, et al. Effect of human immunodeficiency virus type 1 (HIV-1) subtypes on disease progression in persons from Rakai, Uganda, with incident HIV-1 infection. J Infect Dis
2008 (in press).
37. Lutalo T, Gray RH, Wawer M, Sewankambo N, Serwadda D, Laeyendecker O, et al
. Survival of HIV-infected treatment-naïve individuals with documented dates of seroconversion in Rakai, Uganda. AIDS 2007; 21(Suppl 6):S15–S19.
38. Gilbert PB, DeGruttola VG, Hudgens MG, Self SG, Hammer SM, Corey L. What constitutes efficacy for a human immunodeficiency virus vaccine that ameliorates viremia: issues involving surrogate end points in phase 3 trials. J Infect Dis 2003; 188:179–193.
39. Gray RH, Li X, Wawer MJ, Serwadda D, Sewankambo NK, Wabwire-Mangen F, et al
. Determinants of HIV-1 load in subjects with early and later HIV infections, in a general-population cohort of Rakai, Uganda. J Infect Dis 2004; 189:1209–1215.
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