Dynamics and Dispersal of Local Human Immunodeficiency Virus Epidemics Within San Diego and Across the San Diego-Tijuana Border

Abstract

Background: Evolutionary analyses of well-annotated human immunodeficiency virus (HIV) sequence data can provide insights into viral transmission patterns and associated factors. Here, we explored the transmission dynamics of the HIV-1 subtype B epidemic across the San Diego (US) and Tijuana (Mexico) border region to identify factors that could help guide public health policy.

Methods: HIV pol sequences were collected from people with HIV in San Diego County and Tijuana between 1996-2018. A multistep phylogenetic approach was used to characterize the dynamics of spread. The contributions of geospatial factors and HIV risk group to the local dynamics were evaluated.

Results: Phylogeographic analyses of the 2034 sequences revealed an important contribution of local transmission in sustaining the epidemic, as well as a complex viral migration network across the region. Geospatial viral dispersal between San Diego communities occurred predominantly among men who have sex with men, with central San Diego being the main source (34.9%) and recipient (39.5%) of migration events. HIV migration was more frequent from San Diego county towards Tijuana than vice versa. Migrations were best explained by the driving time between locations.

Conclusions: The US-Mexico border may not be a major barrier to the spread of HIV, which may stimulate coordinated transnational intervention approaches. Whereas a focus on central San Diego has the potential to avert most spread, the substantial viral migration independent of central San Diego shows that county-wide efforts will be more effective. Combined, this work shows that epidemiological information gleaned from pathogen genomes can uncover mechanisms that underlie sustained spread and, in turn, can be a building block of public health decision-making.

Keywords: Bayesian discrete phylogeography; HIV; generalized linear model; phylogeography.

Figure 1.

Lineage dispersal events between locations (ie, San Diego communities and the city of Tijuana) and between risk groups. A) The thickness of the arrows reflects the average number of inferred migration events between locations, and the color of the arrows indicates the corresponding BF_adj support. B) For all migration events between locations with BF_adj ≥3, the thickness of the arrows reflects the average number of inferred migration events within or between risk groups, and the color of the arrows indicates the group mixing patterns. Results were obtained from discrete models including clades with SH branch support ≥0.9. Tijuana is colored in darker gray. See also Supplementary Figure S5 for results from discrete models including clades with SH branch support ≥0.7. Abbreviations: BF_adj, adjusted Bayes factor; HTS, heterosexual; MSM, men who have sex with men; PWID, people who inject drugs; SH, Shimodaira Hasegawa.

Figure 2.

(A) Relative contribution of the various migration links to the spread of HIV-1 subtype B in the San Diego–Tijuana area and (B) the relative contribution of risk groups to the spread of HIV-1 B in San Diego and the city of Tijuana. A) We present the results from the discrete phylogeographic analysis including clades with SH support ≥0.9. The Sankey plot represents the average proportion of migration events from each source location (“from”) toward the recipient location (“to”). The left side of the plot shows the origin location, and the right side of the plot shows the destination location. We here only report migration events associated with a BF_adj support ≥3. All corresponding BFs are presented in Table 1. B) Results are based on the clade identification using SH branch support ≥0.9 and accounting for migration links associated with a BF_adj ≥3. The Sankey plot represents the proportion of migration events from each source risk group (“from”) toward the recipient risk group (“to”). Results from the discrete phylogeographic reconstruction based on clades with SH support ≥0.7 are presented in Supplementary Figure S3. All corresponding BFs are presented in Table 1. Colors were chosen to visually clearly distinguish the different types of migration events and have no specific meaning. Abbreviations: BF, Bayes factor; BF_adj, adjusted Bayes factor; HIV, human immunodeficiency virus; HTS, heterosexual; MSM, men who have sex with men; PWID, people who inject drugs; SH, Shimodaira Hasegawa.

Figure 3.

Predictors of migration rates between locations. The box plots report the contribution of each predictor when included in the model. We also report BF support associated with each predictor considered in the GLM, and the BF_adj when ≥3. Abbreviations: BF, Bayes factor; BF_adj, adjusted Bayes factor.