Model-based evaluation of the impact of a potential HIV cure on HIV transmission dynamics

Abstract

The development of an HIV cure is a global health priority, with the target product profile (TPP) for an HIV cure guiding research efforts. Using a mathematical model calibrated to data from men who have sex with men (MSM) in the Netherlands, we assessed whether an effective cure could help end the HIV epidemic. Following the TPP, we evaluated two scenarios: (i) HIV remission, where the virus is suppressed in an individual without ongoing antiretroviral therapy (ART) but may rebound, and (ii) HIV eradication, which aims to completely remove the virus from the individual. Here, we show that sustained HIV remission (without rebound) or HIV eradication could reduce new HIV infections compared to a scenario without a cure. In contrast, transient HIV remission with a risk of rebound could increase new infections if rebounds are not closely monitored, potentially undermining HIV control efforts. Our findings emphasize the critical role of cure characteristics in maximizing cure benefits for public health and highlight the need to align HIV cure research with public health objectives to end the HIV epidemic.

Fig. 1. Model fit to HIV surveillance data for MSM in the Netherlands.

a Incidence of new HIV diagnoses per 100,000 persons per year, b estimated total number of undiagnosed HIV infections, including importations from abroad, c incidence of new HIV infections acquired in the country per 100,000 persons per year, and d contribution to the mean new HIV infections by status of the source of HIV infection. The red dots and the error bars correspond to the mean estimates and the 95% confidence intervals reported by SHM. The mean trajectories estimated from the model are shown as orange lines. The shaded regions correspond to 95% credible intervals based on 100 samples from the joint posterior parameter distribution.

Fig. 2. Schematic of the transmission model for two cure scenarios.

Transitions are shown for one risk group. Recruitment into and exit from the sexually active population are not shown. A detailed description of the model equations, parameters, and assumptions for a HIV remission and b HIV eradication is given in the Supplementary Material.

Fig. 3. Projections of HIV dynamics under the HIV remission scenario.

a New HIV infections, b new rebounds in individuals achieving HIV remission, c HIV prevalence (proportion of individuals with HIV), and d cure coverage (proportion of eligible individuals achieving HIV remission) for different times until viral rebound. The legend for different curves shown in d corresponds to all panels. The red vertical arrows indicate the cure introduction. The mean trajectories from the model are shown as solid lines. The shaded regions correspond to 95% credible intervals based on 100 samples from the joint posterior parameter distribution. Different shades of blue correspond to different times until viral rebound. The projections of the model without a cure are shown in orange. Parameters: efficacy of 90% (proportion of individuals for whom the intervention was successful), annual uptake of 90% (proportion of eligible individuals receiving the intervention each year), and a 3-month diagnostic delay of rebounds in individuals who achieved HIV remission.

Fig. 4. Impact of HIV remission on HIV dynamics under varied intervention characteristics.

The color bar corresponds to a mean change in cumulative HIV infections relative to the no-cure scenario and b mean cumulative rebounds from the introduction of HIV remission scenario in 2026 to the end of the simulation in 2036.

Fig. 5. Projections of HIV dynamics under the HIV eradication scenario.

a New HIV infections (primary infections in naive individuals), b new HIV re-infections (secondary infections in cured individuals), c HIV prevalence (proportion of individuals with HIV), and d cure coverage (proportion of cured individuals among all eligible) for different cure uptakes. The legend for different curves shown in d corresponds to all panels. The red vertical arrows indicate the cure introduction. The mean trajectories from the model are shown as solid lines. The shaded regions correspond to 95% credible intervals based on 100 samples from the joint posterior parameter distribution. Different shades of green correspond to different cure uptakes. The projections of the model without a cure are shown in orange. Parameters: efficacy of 90% (proportion of individuals for whom the intervention was successful) and a 3-month diagnostic delay of re-infections in individuals who achieved HIV eradication.

Fig. 6. Impact of the HIV eradication on HIV dynamics under varied cure characteristics.

The color bar corresponds to a mean change in cumulative HIV infections (primary infections in naive individuals) relative to the no-cure scenario and b mean cumulative HIV re-infections (secondary infections in cured individuals) from the introduction of HIV eradication scenario in 2026 to the end of the simulation in 2036. The color bar scale in (a) is the same as that in Fig. 4a for direct comparison.