The magnitude of the HIV epidemic in sub-Saharan Africa has been difficult to explain. One explanation for the extensive spread of HIV emphasizes the importance of persons with acute and early HIV infection, who are highly infectious but rarely aware of their infection status.42
Amplified transmission in this stage of infection has been ascribed to high viral loads3, 4
and an apparent increase in viral infectivity.43
In rhesus macaques, the ratio of infectious virions to total virions is up to 750 times as high during AHI as during CHI.43
In Ugandan HIV-serodiscordant couples, transmission rates were approximately 25 times as high during early versus asymptomatic infection.6
Phylogenetically-defined infection clusters16-18, 44
and documented acute-to-acute transmission events45, 46
further support the potential importance of early infection.
We undertook the current study to understand the contribution of EHI to the HIV epidemic in Lilongwe, Malawi, where we have conducted studies to identify patients with AHI,47-49
characterize their sexual behaviours,50
and measure viral load changes during EHI.3
We estimated that EHI index cases are responsible for 19%-52% of HIV transmissions in Lilongwe, with a mode of 38%. Our results suggest that the initial period of elevated transmissibility may be fairly long (~5 months), and that transmissibility during EHI is 30 times as great as during CHI.
Mathematical modelling estimates of the importance of EHI have varied widely, 6, 9, 10, 12-15, 31, 51
due in part to differing assumptions and a paucity of data for parameter definitions. Endemic-phase estimates of the proportion of new infections due to EHI in sub-Saharan Africa have ranged from 7% to 31% in modelling studies.6, 13
Phylogenetic studies conducted among MSM or mixed populations in western settings have estimated that 25%-49% of incident infections are due to EHI.16-18, 44
A strength of our model is the extensive use of local data for model parameterization. We used sexual behaviour data from Lilongwe30
to define contact patterns specific to the setting of interest and viral load data from AHI patients in Lilongwe3
to provide high resolution in the time-course of transmission probabilities during EHI. We fitted the model to local HIV prevalence data using a Bayesian melding approach. The close parameterization of the model based on data from the setting of interest allows us to expect results with greater reliability and applicability – at least in Lilongwe and similar settings – than could be expected from a model using a combination of parameter values derived from disparate populations.
Our model was enhanced in several other ways in comparison to previous models addressing AHI/EHI. To model contact patterns relevant to HIV transmission, we explicitly incorporated both steady pairs and casual contacts, with consideration of both high-risk and low-risk groups. The Bayesian melding approach allowed us to account for input and result uncertainty. Finally, we conducted sensitivity analyses to assess intervention effects under a range of predicted EHI contributions.
The idea of using ART as a transmission prevention strategy has gained remarkable attention.19, 41
This idea emerges from the reduction of HIV replication in the genital tracts of persons on ART,52, 53
and apparent suppression of HIV transmission in serodiscordant couples when the index case receives ART.54-56
A widely cited mathematical model has concluded that annual test-and-treat strategies could virtually eliminate the epidemic in South Africa,19
but the importance of EHI appears to have been underestimated in that model,22, 24, 25
and other modelling studies examining the potential benefits of ART have been less optimistic about the test-and-treat approach.57, 58
Accordingly, we examined the effects of behavioural and/or biomedical interventions that might drastically reduce sexual transmission at different stages of HIV. One such intervention was assumed to improve survival and reduce transmissibility from the onset of CHI, approximating an annual test-and-treat strategy.19
Our results suggest that even highly effective behavioural and/or biological interventions – including “test-and-treat” – are unlikely to eliminate HIV in Lilongwe and similar settings unless people with EHI are included. Even if the contribution of EHI to ongoing transmission is as low as ~20% (the lower credible limit in our analysis), intervention only during CHI is unlikely to eliminate HIV unless nearly all CHI cases experience life-long transmission suppression. If CHI-only intervention coverage is imperfect, additional EHI interventions can lead to dramatic improvement. Our results suggest that strategies preventing transmission from both CHI and
EHI cases provide the greatest chance for marked, durable reductions in HIV incidence and prevalence. Sensitivity analyses in which CHI interventions were assumed to provide even greater survival benefits, or to start at times more typical of current clinical practice, provided even greater support for inclusion of interventions during EHI.
Interventions directed toward patients with EHI have unique challenges. While antibody tests may detect some post-acute EHI cases, a “test-and-treat” approach for specifically identifying EHI cases would require a very brief interval for repeat testing (~3-6 months), and reliance on antibody tests would result in missed AHI cases. Large-scale programs of quarterly or semi-annual HIV testing would be difficult to implement and sustain. Both biological and behavioural interventions intended for EHI may require more targeted approaches, such as partner notification or campaigns aimed at encouraging HIV testing among individuals with recent risky behaviour and acute retroviral symptoms.59
These case-finding strategies, in combination with pooling of blood samples,47
targeted HIV RNA screening,48, 60
and/or newer HIV detection tests1, 61
could increase the numbers of EHI cases detected, even in resource-limited settings. Encouragingly, our best-fitting value of 4·8 months for the period of elevated transmissibility suggests that interventions provided during the first few months of infection, rather than the first few weeks, may have substantial public health benefit. In earlier work we demonstrated that the HIV concentration in seminal plasma remained elevated for more than two months after infection, consistent with this idea.3
Interventions initiated during EHI may also have unique benefits that are not explicitly captured in our model. Adherence to biological or behavioural interventions initiated during EHI may remain high at least through the most infectious period,50
potentially maximizing cost-effectiveness and minimizing the detrimental effect of waning adherence observed with some interventions.62, 63
Additionally, at least some studies suggest a clinical benefit from initiation of treatment during EHI.64
Mathematical models of HIV transmission depend heavily on assumptions about sexual behaviour. The input values for sexual behaviour parameters in our model were based on a cross-sectional study conducted among STD clinic patients in Lilongwe30
that found long-term monogamy to be common, with only 14% reporting long-term concurrency, sporadic concurrency, or consecutive, monogamous partnerships in rapid succession. Although based on a relatively small sample (n=186) with generalizability likely limited to similar settings, these data represent some of the most detailed information available on partnership durations and gaps in sub-Saharan Africa. In our best-fitting model simulation, “steady” partnerships in the higher-risk group were 1.3 months in duration, with intervening gaps of 11 days; by contrast, steady partnership durations and gaps in the lower-risk group were 2.5 years and 1.5 years, respectively. Therefore, the difference between “steady” and “casual” partners appears to be considerably less distinct for higher-risk than lower-risk individuals, potentially explaining why some behavioural parameters resulting from the model fit vary in somewhat unexpected ways across groups. For example, despite the higher casual contact rate that we initially posited for higher-risk singles, the best-fitting parameter set suggests a lower rate, possibly because the shorter gap between “steady” partnerships translates to less time as a single in the high-risk group. We also note that the partner change rate in our higher-risk group was slower than in the highest-risk groups of several previous HIV epidemic models,13, 65
and that none of our behavioural parameters was particularly extreme, despite being collected in an STI clinic population. The latter result may be due to the high prevalence of STIs in Malawi,66, 67
which likely results in considerable overlap between STI clinic populations and the “general” population.
The importance of sexual partner concurrency in the HIV epidemic of sub-Saharan Africa has been emphasized and debated.68-72
Intuitively, concurrency seems potentially important for transmission during EHI, because long-term monogamy would limit the high transmissibility of newly infected persons to a single partnership. Our model captured only a simple form of concurrency – one-off encounters outside of pairs; it did not include long-term concurrency. However, our best-fitting parameter set included short gaps between partners (11 days) in a relatively sizeable higher-risk group. These short gap lengths are consistent with (and based on) our data from Malawi,30
and provide an alternate explanation for rapid HIV spread and the corresponding importance of EHI; however, the potential contribution of concurrency cannot be excluded.
All mathematical models have limitations. In our model, individuals and pairs were restricted to a given risk group, and only a very simple form of concurrency was captured, as noted above. Additionally, our model did not incorporate population age structure or male-female behavioural asymmetry. The inclusion of behavioural heterogeneity across two separate risk groups may capture some age-related behavioural variation, but the results give an average picture for the sexually active population overall. Nevertheless, our division of EHI into numerous intervals, our inclusion of more than one risk group, and our incorporation of both steady and casual contacts likely reflect transmission dynamics more accurately than previous models that have assumed only one-off contacts occurring at random within populations. Additional considerations, such as drug resistance, side effects, behavioural disinhibition, and cost, must also be carefully appraised before implementing specific treatment-based interventions.
In summary, our analyses suggest that EHI remains a critical factor in the ongoing HIV epidemic in Lilongwe. This result suggests that acute and early HIV infection can be important not only in the earliest phases of HIV epidemics, but also in more mature epidemics. Consequently, prevention approaches directed at all stages of HIV will likely be necessary to ensure a durable effect on the epidemic in Lilongwe and similar settings. As plans for “treatment as prevention” are developed, our results suggest that strategies for detection and management of patients with acute and early HIV must be included.