Predicting the impact of a nonsterilizing vaccine against human immunodeficiency virus

Abstract

Studies of human immunodeficiency virus (HIV) vaccines in animal models suggest that it is difficult to induce complete protection from infection (sterilizing immunity) but that it is possible to reduce the viral load and to slow or prevent disease progression following infection. We have developed an age-structured epidemiological model of the effects of a disease-modifying HIV vaccine that incorporates the intrahost dynamics of infection, a transmission rate and host mortality that depend on the viral load, the possible evolution and transmission of vaccine escape mutant viruses, a finite duration of vaccine protection, and possible changes in sexual behavior. Using this model, we investigated the long-term outcome of a disease-modifying vaccine and utilized uncertainty analysis to quantify the effects of our lack of precise knowledge of various parameters. Our results suggest that the extent of viral load reduction in vaccinated infected individuals (compared to unvaccinated individuals) is the key predictor of vaccine efficacy. Reductions in viral load of about 1 log(10) copies ml(-1) would be sufficient to significantly reduce HIV-associated mortality in the first 20 years after the introduction of vaccination. Changes in sexual risk behavior also had a strong impact on the epidemic outcome. The impact of vaccination is dependent on the population in which it is used, with disease-modifying vaccines predicted to have the most impact in areas of low prevalence and rapid epidemic growth. Surprisingly, the extent to which vaccination alters disease progression, the rate of generation of escape mutants, and the transmission of escape mutants are predicted to have only a weak impact on the epidemic outcome over the first 25 years after the introduction of a vaccine.

FIG. 1.

Outline of the model. The schematic illustration of the model indicates the different compartments, movement between compartments, and the dynamics of aging and disease progression within compartments (see Appendix). As indicated on the right, squares indicate age classes. (See Materials and Methods for detailed description.)

FIG. 2.

Effects of disease-modifying vaccine. After infection in an unvaccinated individual, both the viral load and mortality rise with time (solid line). Disease-modifying vaccines may act primarily by reducing the viral load after infection (dashed line) or by reducing disease progression (dashed-dotted line). A lower viral load in vaccinated individuals compared to unvaccinated individuals leads to reduced transmission (since transmission is dependent on the viral load) (top) and reduced mortality (bottom). If disease progresses at a normal rate, then viral loads and mortality rise at the same rate as in unvaccinated individuals, but starting at a lower baseline level (dashed line). Therefore, viral loads and mortality in a vaccinated individual will eventually rise to be equivalent to those seen in early natural infections, after a delay approximately equal to the reduction in viral load divided by the annual increase in viral load (0.09 log₁₀ copies ml⁻¹ year⁻¹). If vaccination slows disease progression, then the viral load and mortality may be stable or rise slowly (dashed-dotted line). The reduction in viral load induced by vaccination will reduce the virological infectiousness of infected individuals, whereas a delay in the rise in viral load will reduce behavioral infectiousness since people do not achieve high viral loads until they are older and less sexually active.

FIG. 3.

Predicting the effects of disease-modifying vaccination. (A) One thousand simulations were performed for each of two cases: (i) a population given a viral-load-reducing vaccine that reduced the viral load between 0.5 and 1.5 log₁₀ copies ml⁻¹ while the progression rate varied between 80 and 120% of normal (left) and (ii) a population given a progression-slowing vaccine that reduced the viral load between 0.1 and 0.5 log₁₀ copies ml⁻¹ and the disease progression rate from 0 to 80% of normal (right). The means (⧫), medians (solid bars), 25th and 75th percentiles (open bars), and outliers (lines) of the proportions of deaths averted (top) and infections averted (bottom) compared to the unvaccinated control are shown. Shaded areas indicate those simulations where more deaths and infections occurred with vaccination than without. Thus, the number of simulations in the shaded areas divided by the total number of simulations gives the fraction of simulations with worse outcome with vaccination; these numbers are quoted in the text. (B) The proportion of vaccinated individuals (top) and proportion of infections involving a vaccine escape mutant virus (bottom) at different times after the commencement of vaccination with the viral-load-reducing vaccine used for panel A.

FIG. 4.

Sensitivity analysis. The relationships between the viral load reductions (left), increases in sexually risky behavior (center), and proportions of individuals vaccinated at 10 years (right) and HIV mortality and incidence at 25 years for the viral-load-reducing (A) and progression-slowing (B) vaccines are shown. Each dot represents the outcome of one simulation with parameters chosen at random from the range given in Table 1. The results for 1,000 simulations of each scenario are shown. Cumulative incidence and mortality data are expressed as percentages of the unvaccinated control values (therefore, values of >100% indicate an increase in cumulative HIV incidence as a result of vaccination). The proportion of individuals vaccinated varied with the vaccination rate and the rate of loss of vaccine protection.

FIG. 5.

Effects of vaccination on different populations. One hundred new populations were created by randomly varying the baseline population parameters, and the effects of vaccination were assessed by simulating 100 vaccination scenarios in each new population introduced at a 0.5% prevalence (as described in Materials and Methods). The average percentages of deaths (top) and infections (bottom) averted due to vaccination after 10 years (A) and 25 years (B) were plotted against the doubling times of the epidemics immediately prior to the introduction of vaccination. Each point represents the results for a different population. To investigate the effects of initial HIV prevalence, we studied vaccination introduced at an initial HIV prevalence of 0.5, 1, 2, 4, 8, or 16% (colored as indicated) (C). The average proportions of deaths (top) and infections (bottom) averted for each population at each initial HIV prevalence were plotted against the doubling time of the epidemic immediately prior to the introduction of vaccination.