HIV-1 transmission during early infection in men who have sex with men: a phylodynamic analysis

Abstract

Background: Conventional epidemiological surveillance of infectious diseases is focused on characterization of incident infections and estimation of the number of prevalent infections. Advances in methods for the analysis of the population-level genetic variation of viruses can potentially provide information about donors, not just recipients, of infection. Genetic sequences from many viruses are increasingly abundant, especially HIV, which is routinely sequenced for surveillance of drug resistance mutations. We conducted a phylodynamic analysis of HIV genetic sequence data and surveillance data from a US population of men who have sex with men (MSM) and estimated incidence and transmission rates by stage of infection.

Methods and findings: We analyzed 662 HIV-1 subtype B sequences collected between October 14, 2004, and February 24, 2012, from MSM in the Detroit metropolitan area, Michigan. These sequences were cross-referenced with a database of 30,200 patients diagnosed with HIV infection in the state of Michigan, which includes clinical information that is informative about the recency of infection at the time of diagnosis. These data were analyzed using recently developed population genetic methods that have enabled the estimation of transmission rates from the population-level genetic diversity of the virus. We found that genetic data are highly informative about HIV donors in ways that standard surveillance data are not. Genetic data are especially informative about the stage of infection of donors at the point of transmission. We estimate that 44.7% (95% CI, 42.2%-46.4%) of transmissions occur during the first year of infection.

Conclusions: In this study, almost half of transmissions occurred within the first year of HIV infection in MSM. Our conclusions may be sensitive to un-modeled intra-host evolutionary dynamics, un-modeled sexual risk behavior, and uncertainty in the stage of infected hosts at the time of sampling. The intensity of transmission during early infection may have significance for public health interventions based on early treatment of newly diagnosed individuals.

Figure 1. HIV transmission model and phylogeny.

Top: A flow diagram describing the mathematical model fitted to surveillance time series and the HIV-1 phylogeny. Arrows of different colors represent the time-dependent rates at which transitions occur. Infected individuals progress from EHI to AIDS and may also become diagnosed (“D.”), as represented by black and green arrows. Orange arrows represent natural mortality. Incidence occurs at the rate λ(t) (red arrow). A more detailed diagram is shown in Figure S6. Bottom: HIV-1 phylogeny comprising virus samples from 662 patients and ancestral states estimated using the methods in . The tree has been randomly downsampled to include 250 terminals for perspective. Colors at the terminals of the phylogeny represent the estimated stage of infection of the host at the time of sampling based on clinical data. Colors on the interior of the phylogeny represent the estimated stage of infection of the host harboring virus that is ancestral to the sample. Yellow corresponds to lineages that are likely to represent infections from outside of the DMA MSM risk group.

Figure 2. Estimated transmission patterns through time.

Lines show the MLE, and shaded regions show the 95% credible interval. (A) Estimated number of transmissions originating from individuals in different stages of infection: EHI, chronic infection, and AIDS. (B) The estimated fraction of transmissions attributable to EHI, chronic infection, and AIDS. (C) Estimated number of transmissions originating from diagnosed and undiagnosed individuals through time. (D) The estimated fraction of transmissions attributable to diagnosed and undiagnosed infections through time. Estimated credible intervals reflect the fit of a single model to the data and do not incorporate uncertainty due to model misspecification error.

Figure 3. HIV diagnoses and CD4 cell count through time.

(A) The sample proportion (points) and estimated proportion (lines) of diagnoses that are concurrent with AIDS diagnosis over time, and the estimated proportion of diagnoses that are not concurrent with AIDS. The diameter of points is proportional to the number of diagnoses used to calculate the proportions. (B) The sample mean (points) and estimated mean (lines) CD4 cell count in newly diagnosed individuals over time. The mean is calculated from CD4 counts aggregated by year. The diameter of points is proportional to the number of CD4 counts used to calculate the mean. Inset: The mean CD4 cell count by stage of infection, which gives the best fit (least squares) to the observed trend in mean CD4 count over time.

Figure 4. Estimated HIV diagnoses and HIV incidence through time.

(A) Actual (blue) and estimated (red) cumulative HIV diagnoses in DMA MSM. (B) Estimated incidence of infection over time. The red line shows estimated incidence from surveillance time series data. The blue line shows estimated incidence using the back-calculation method.