INTRODUCTION
Across the Southern United States, including North Carolina (NC), the HIV epidemic has persisted in large connected sexual networks, particularly among men who have sex with men (MSM).1–5 1 6 3 7
Phylogenetic analysis of HIV sequences is an excellent adjunct to enumerating networks and allows for tracking of local transmission patterns. HIV phylogenies based on sequence similarity and inference of common ancestors can identify putative transmission clusters.8,9 10–12 13 14–16 15 17 18 19,20
We investigated the sexual network constructed from PNS data in Wake County, NC, and compared this with HIV transmission clusters using pol sequences routinely collected statewide. Our objective was to assess the overlap between networks derived through PNS and sequence analysis to identify areas where interventions could be intensified.
METHODS
Study Setting and Design
Wake County is a metropolitan county in central NC that accounts for approximately 10% of statewide annual new HIV diagnoses.21
We conducted a cross-sectional analysis of Wake County residents aged 18 years and older who were newly diagnosed with HIV-1 during 2012–2013 and their social and sexual contacts reported during routine PNS. These data were compared with 15,246 HIV genetic sequences collected among HIV cases in NC 1997-2014. The University of North Carolina Biomedical Institutional Review Board approved the study.
Study Population
Disease intervention specialists (DISs), employed by NC Department of Health and Human Services (DHHS) or Wake County DHHS, attempt to interview all newly diagnosed persons (referred to as index cases) and collect information about their partners for tracing and testing. In NC, high-risk social contacts are elicited at the discretion of each DIS when perceived to increase case finding without overly burdening investigations.22,23
Acute HIV infection was identified through the NC Screening and Tracing Active Transmission (STAT) Program,24
Sexual Network Construction
We constructed the sexual network using name-based partnership data collected during PNS interviews with index cases. All network members were deidentified after network construction to preserve patient confidentiality. A sociosexual network comprises discrete components (at least 2 people directly or indirectly connected) and singletons (isolated persons if no partners are disclosed or located). The network was created using the igraph25 26
HIV-1 Sequences and Transmission Cluster Identification
HIV-1 pol sequences (full-length protease and partial reverse transcriptase) were extracted from genotypes performed by LabCorp, the largest reference laboratory in NC, and sampled between 1997 and mid-2014 from patients accessing clinical care. Demographic variables available included birth date, sex, and sampling site. Geographic location of sampling site was categorized by NC-DHHS HIV Field Service Region [see Figure, Supplemental Digital Content 1, https://links.lww.com/QAI/B150
Index and HIV-positive partners were probabilistically matched to the statewide sequence data set by birth date, sex, and laboratory test dates. We considered nonmatching sequences as background references for cluster construction. All analyses used the earliest sequence per individual. The final data set included 15,246 sequences. A random subset of 100 sequences is available in GenBank, accession numbers KY579388-KY579812.
Sequences were aligned using MUSCLE27 28 29 30 31 32
Putative clusters were confirmed with the Bayesian Markov Chain Monte Carlo (MCMC) approach in BEAST v1.8.2.33 34 33
Statistical Analyses
We compared membership in transmission clusters and sexual network components. Clusters involving ≥2 cases (index or partners) were characterized by demographic features and compared with case location within and across network components. Time of most recent common ancestor and cluster age were estimated based on timing of branching in the phylogeny.
RESULTS
Study Population
In total, 280 persons newly diagnosed with HIV were reported in Wake County from 2012–2013; 83% (n = 232) were male, 65% (n = 183) were black, and 40% (n = 112) were aged 30 years and younger. Many (27%, n = 75) were concurrently diagnosed with AIDS and 4% (n = 11) were diagnosed during AHI. Among 235 index cases with CD4 count data, the median first CD4 count was 338 cells/mm3 [interquartile range (IQR) 130–525 cells/mm3 ]; 31% had CD4 count <200 cells/mm3 . Among 147 cases with viral load results within 3 months of diagnosis, the median was 4.9 log copies per milliliter (IQR 4.3–5.3 log copies/mL) (Table 1 ).
TABLE 1.: Index Cases Diagnosed During 2012–2013 in Wake County, NC, and Their Partners in the Sociosexual Network (N = 663)
Partner Notification Network
Disease intervention specialists interviewed 225/280 index cases (80%), who reported 854 sex partners and 34 social contacts (average 4 contacts per person; number of sex partners ranged 0–50). Approximately half (50%; 446/888) of contacts (414 sexual and 32 social contacts) had enough locating information for DIS to begin investigation. The 446 partnerships investigated (Table 2 ) resulted in 383 unique nonindex case partners (Table 1 ): 36 were index cases themselves, 19 were named by ≥2 index cases, and 3 were index cases who were also named as partners more than once. Although 48/383 (13%) partners were not located during investigation, we included them in the network. Of 383 partners, 39% were HIV-negative, 34% (n = 131) were HIV-positive, and 27% HIV status was unknown. Most HIV-positive nonindex partners (81%; 106/131) were diagnosed before 2012. Thirty-six percent (138/383) of partners resided outside Wake County, including 22 (6%) residing out of state and 6 (2%) with unknown location of residence.
TABLE 2.: Partnerships Reported by Index Cases With Located Members of the Sociosexual Network (N = 446)
The PNS network included 663 persons (Table 1 ), with 280 index cases and 383 partners. Most network members were black (63% vs. 29% white and 5% Hispanic/Latino), MSM or men who have sex with transgender women (MST) (61%), and young (median age 30 years, IQR 24–42). Persons of color were more likely to be HIV-positive (74% Latino and 66% black) compared with white persons (53%). MSM index cases were more likely to have partners who could not be located than men only reporting female partners (37% vs. 29%).
Overall, 176/280 index cases were connected to at least one other person in the network. The remaining 104 singletons represented 37% of index cases; 55 (53%) reported zero partners and 49 provided information for 1–50 partners, though none could be located. The sexual network was sparsely connected, comprising 104 singletons and 137 network components (≥2 persons). Component sizes ranged from 2–65 persons; the 3 largest included 20, 26, and 65 people (Fig. 1A ). Most (62%, n = 85) components only included MSM and MST.
FIGURE 1.: Legend: Sexual network showing phylogenetic cluster membership and sex (A), and selected sexual network components showing cluster members and genetic distance statewide (B). A, sexual and social network compiled from contract tracing depicting HIV status and phylogenetic transmission cluster, Wake County, NC, during 2012–2013. Graph shows sex (node shape), cluster membership with respect to gene sequence availability and cluster membership of other persons represented in this sexual network (node color), and partnerships disclosed by index cases (lines connecting nodes). The graph is split into quadrants by number of persons in each component: (a) singletons (n = 104 persons), (b) dyads (n = 75 components), (c) components Size 3 (n = 22), 4 (n = 10), or 5 (n = 12), and (d) components Size 6 or larger (n = 18 components comprising 243 persons). B, selected phylogenetic transmission clusters (F, I, and J) show sexual network components spanned and additional cluster members statewide who were not part of the Wake County–based sexual network. Graph shows sex (node shape), appearance in sexual network or only transmission cluster (diagonal cross in node shape), transmission cluster status (node color), and connections between nodes. Having a named partner tie (ie, connection in the sociosexual network) is represented by a solid line and being ≤1.5% pairwise genetic distance in the transmission cluster is represented by a dashed line. Component orientation matches (A).
We assessed characteristics of the 446 partnerships (93% sexual and 7% social), which included 559 persons across 137 network components (excluding 104 singletons) (Table 2 ). Most partnerships involved either MSM or MST (81%), were among people of the same race (82%), and included at least one black person (71%). Nearly 25% (n = 106) of partnerships were between an index case and a person with unknown HIV status. Among 340 partnerships where HIV status was documented for both people, 53% involved 2 HIV-infected persons (n = 181). Most (80%) of the 131 HIV-infected partners received their diagnoses before the index cases (median 2.5 years, IQR 1 month-5.5 years).
Transmission Clusters
Over half of HIV-positive cases (56%; 230/411) matched to a pol sequence, including 53% (148/280) index cases and 63% (82/131) HIV-positive partners. Cases who had sequences were similar to those without sequences with respect to sex and age. Among index cases, whites were more likely than persons of color to have sequences (64% vs. 49%, P = 0.04), as well as those diagnosed in 2012 compared with 2013 (63% vs. 44%, P = 0.002).
We identified 116 clusters involving ≥1 person from the network, with a total of 800 persons including 103 index cases (70% those with sequences), 66 partners (80% those with sequences), and 631 background sequences (Fig. 2 ). In the initial maximum-likelihood analysis, 117 clusters were identified but 2 sequences failed to cluster in the confirmatory BEAST analyses. The 116 confirmed clusters had median size of 2 members (range 2–36 persons); only 3 clusters were non-B subtypes (A1, CRF02_AG, CRF06_cpx).
FIGURE 2.: Phylogenetic tree of HIV pol gene sequences showing transmission clusters. Maximum-likelihood tree constructed for display purposes using sequences (n = 800) identified in confirmed phylogenetic transmission clusters among 15,246 HIV-1–positive persons sampled in North Carolina during 1997–2014. Confirmed clusters had posterior probability >0.98 in the Bayesian analysis and include at least 1 index or partner case identified during partner notification of new HIV diagnoses in Wake County, 2012–2013. Index cases (new diagnoses in 2012–2013) are indicated by red circles and partner cases are indicated with blue circles at the tips of the tree. Clusters in gray boxes involve ≥2 cases from the partner notification network. Clusters with letters (A–S) are the Wake clusters that meet these criteria and also include ≥5 persons statewide. Branch support, using the Shimodaira–Hasegawa-like test values, is included for the Wake clusters.
Among 230 index cases (n = 148) and partners (n = 82) with sequences, we evaluated associations with cluster membership. Cluster members were more likely to be male (77% vs. 52% female, P = 0.006), men reporting male contacts (83% vs. 67% heterosexual and 57% no partners reported, P < 0.001), black (80% vs. 69% white and 33% Latino, P = 0.001), and younger (mean age 35 vs. 38 years, P = 0.04), compared with cases with sequences who were not in a cluster. Cluster members had more connections in the network than did cases with sequences who did not cluster (2 vs. 1 mean partners, P = 0.001).
Most clusters included only one index case or partner from the network; 34 (29%) including ≥2 index cases were denoted “Wake” clusters for further analysis (Table 3 shows Wake clusters with ≥5 total cluster members). Wake clusters included 287 persons (56 index cases, 31 partners, and 200 background sequences) (Fig. 2 ); 2 (6%) comprised only 2 partners with no index cases. All Wake clusters were subtype B and most were male-dominated; 7 (21%) included ≥50% women. More than half (59%; n = 20) of Wake clusters only included persons sampled from the same 11-county geographic region (Fig. 2 ). Most (74%; 61/82) clusters with only 1 person from PNS were clusters with ≥50% members sampled in the same region, including 22 clusters with 100% members sampled in the same region.
TABLE 3.: Transmission Clusters That Included 5 or More Persons Statewide and at Least Two Members of the Wake County–Based Sexual Network of Adults Diagnosed With HIV During 2012–2013 and Their Contacts (n = 235)
Wake cluster maximum genetic distance was 1.67% (IQR: 1.04%–2.93%) statewide and 0.95% (IQR: 0.32%–1.28%) when restricted to network members (Table 3 ). Median estimated cluster age before the index case diagnosis was 8.5 years (IQR: 5.1–12.9 years) with median most recent common ancestor estimated to occur in 2005 (range 2000–2007).
Partner Notification Network and Transmission Cluster Overlap
The PNS network included 663 persons: 280 index cases and 383 contacts who formed 104 singletons plus 559 persons in 446 partnerships (Fig. 1A ). Among 230 network members with sequences, including 45 singletons, 169/230 (73%) were in 1 of 116 statewide transmission clusters that included at least 1 network member. The 169 persons spanned 82 network components and 23 singletons; the remaining 61 persons who were not in a cluster spanned 36 network components and 22 singletons. Among the 23 singletons in a cluster (51% singletons with sequences), 8 (35%) did not name any partners and the remainder disclosed at least 1 partner, though none could be located. The median cluster size among singletons was 4 persons (range 2–23).
Among 446 partnerships, 70 (16%) included 2 HIV-positive persons with sequences; of these, 83% (58/70) were sexual connections. All male–female pairs were in the same cluster, whereas only 34% of male–male pairs were in the same transmission cluster (χ2 P < 0.001). Of the 383 contacts, 27 (7%) were only identified as social contacts of an index case; 11 had a sequence, of which 9 were in a statewide cluster with no one else from the PNS network and 2 were in a Wake cluster; one clustered with another PNS social contact (statewide cluster size 2) and the other clustered with the index case who disclosed the contact as a social connection (pairwise genetic distance 1.3%, statewide cluster size 14).
Eighty-seven persons were in 34 Wake clusters (defined as ≥2 persons from PNS network), which included 2–6 network members and spanned 56 PNS network components plus 12 singletons. Overall, 41% (14/34) Wake clusters covered only 1 network component; 1 included 3 network members and the rest included 2. The Wake clusters that covered only 1 component were more likely to include ≥50% women [36% (5/14) vs. 10% (2/20) spanning multiple components].
Among 19 Wake clusters with ≥5 persons statewide (Table 3 ), 6 (32%) covered only 1 component, where all network persons in the cluster were also linked by named partner ties. The remaining 13 spanned multiple components, where the phylogenetic relationships bridged located partnerships: 7 (37%) spanned 2 components, 5 (26%) spanned 3, and 1 (5%) spanned 4 components. For example, the 3 network members in Cluster J spanned 2 components and 1 singleton (Fig. 1A , quadrants Fig. 1A, C, D ), although there were 12 people in the cluster statewide (Fig. 1B ). The maximum genetic distance between any pair of network members in Cluster J was 1.24%, despite each of the 3 network members being in different components (Table 3 , Cluster J). Of 13 clusters with ≥5 members statewide (Table 3 ) that spanned multiple components, 9 (69%) included only men.
There was no significant difference by sampling year, cluster age, or statewide genetic distance between Wake clusters that covered single or spanned multiple components. However, the mean genetic distance among persons in the Wake cluster was significantly smaller when the cluster covered only 1 component (0.66% vs. 1.23%, P = 0.03).
DISCUSSION
This study sought to explore the benefits of combining molecular data with sociosexual network data obtained during routine PNSs from persons newly diagnosed with HIV in a single large county in NC. The study drew on a statewide data set of over 15,000 HIV-1 sequences from persons sampled between 1997 and mid-2014. We overlaid the genetic data and sociosexual network constructed from partner notification records to obtain a more comprehensive picture of the epidemic and identify gaps in PNS, particularly among male-dominated sexual network components.
More than half of local transmission clusters bridged sexual network components that seemed disconnected, demonstrating that molecular data can detect unobserved links in the sexual network. Furthermore, despite not having any partners identified in the network, over half of singletons with sequences were in a statewide cluster. For each set of disconnected network components or singletons in the same transmission cluster, at least one connection is not represented in the PNS network. Some of the disagreement may be explained by differing collection periods because sequence sampling time for the clusters was not limited by period. Many index cases were likely infected for years; so, partners reported at diagnosis may not reflect the network at the time of infection. In addition, some persons in the network were only social contacts; so, their inclusion increased PNS network component size and may have increased the effect of bridging by the transmission clusters if they were in a different cluster than the index case. However, they represented only 2 of 87 network members in a Wake cluster.
Partner notification is limited by missing data due to persons not being diagnosed or located and partnerships not being disclosed or not occurring during the DIS interview period. Stigma and discrimination faced by MSM contribute to interview bias and may reduce willingness to disclose partners to health authorities. Previous HIV sexual network studies in NC found that a high proportion of partners cannot be located3,35 36
Accordingly, local transmission clusters, particularly those that spanned multiple components, were more likely to be male-dominated. This reflects the current epidemic in NC, where the overall rate of new diagnoses remains elevated with ongoing transmission among young men37 38 35
We combined methodologies previously used to describe HIV transmission networks. Although several studies have used sequence data to construct transmission networks,2,39–46 16,20,47 16
Combining PNS and molecular data can lead to an improved representation above what is possible with either alone10,12 48 49–51 2,52,53 52 52 36,45,52
Although there is no accepted genetic distance criteria to define transmission clusters,8 8
Both HIV phylogenetic and PNS data portray networks differently and care must be taken not to misinterpret results. Although the combination of these data provide new insights into network structure, potential ethical and privacy concerns must be considered. HIV genetic clustering does not imply direct person-to-person transmission or direction of transmission48
The HIV sequence analysis recognized ongoing transmission chains among high-risk persons, notably MSM, which was not detected through routine partner notification. Persons who experience the most stigma and those at highest risk, MSM or not, such as those who engage in transactional sex or have anonymous partnerships, are more difficult to reach and may therefore be absent from the PNS network. Molecular approaches provide clues to gaps in PNS and direction for case finding and partner elicitation efforts.54
ACKNOWLEDGMENTS
The authors thank the NC Department of Health and Human Services and the disease intervention specialists serving Wake County.
REFERENCES
1. National Center for HIV/AIDS VH, STD, and TB Preventio. HIV in the Southern United States. Atlanta, GA: Centers for Disease Control and Prevention; 2016:2016.
2. Dennis AM, Hue S, Hurt CB, et al. Phylogenetic insights into regional HIV transmission. AIDS. 2012;26:1813–1822.
3. Hurt CB, Beagle S, Leone PA, et al. Investigating a sexual network of black men who have sex with men: implications for transmission and prevention of HIV infection in the United States. J Acquir Immune Defic Syndr. 2012;61:515–521.
4. McKellar MS, Cope AB, Gay CL, et al. Acute HIV-1 infection in the Southeastern United States: a cohort study. AIDS Res Hum retroviruses. 2013;29:121–128.
5. Oster AM, Wejnert C, Mena LA, et al. Network analysis among HIV-infected young black men who have sex with men demonstrates high connectedness around few venues. Sex Transm Dis. 2013;40:206–212.
6. Centers for Disease Control and Prevention. HIV in the United States: At a Glance. Atlanta, GA; 2017. Available at:
https://stacks.cdc.gov/view/cdc/26041/cdc_26041_DS1.pdf. 7. Hogben M, McNally T, McPheeters M, et al. The effectiveness of HIV partner counseling and referral services in increasing identification of HIV-positive individuals a systematic review. Am J Prev Med. 2007;33:S89–S100.
8. Hassan AS, Pybus OG, Sanders EJ, et al. Defining HIV-1 transmission clusters based on sequence data. AIDS. 2017;31:1211–1222.
9. Aldous JL, Pond SK, Poon A, et al. Characterizing HIV transmission networks across the United States. Clin Infect Dis. 2012;55:1135–1143.
10. Grabowski MK, Redd AD. Molecular tools for studying HIV transmission in sexual networks. Curr Opin HIV AIDS. 2014;9:126–133.
11. Oster AM, Pieniazek D, Zhang X, et al. Demographic but not geographic insularity in HIV transmission among young black MSM. AIDS. 2011;25:2157–2165.
12. Vasylyeva TI, Friedman SR, Paraskevis D, et al. Integrating molecular epidemiology and social network analysis to study infectious diseases: towards a socio-molecular era for public health. Infect Genet Evol. 2016;46:248–255.
13. Delva W, Leventhal GE, Helleringer S. Connecting the dots: network data and models in HIV epidemiology. AIDS. 2016;30:2009–2020.
14. Peters PJ, Gay CL, Beagle S, et al. HIV infection among partners of HIV-infected black men who have sex with men—
North Carolina , 2011–2013. MMWR. 2014;63:90–94.
15. Smith DM, May SJ, Tweeten S, et al. A public health model for the molecular surveillance of HIV transmission in San Diego, California. AIDS. 2009;23:225–232.
16. Wertheim JO, Kosakovsky Pond SL, Forgione LA, et al. Social and genetic networks of HIV-1 transmission in New York City. PLoS Pathog. 2017;13:e1006000.
17. Dennis AM, Pasquale DK, Billock R, et al. Integration of contact tracing and phylogenetics in an investigation of acute HIV infection. Sex Transm Dis. 2017;45:222–228.
18. Lewis F, Hughes GJ, Rambaut A, et al. Episodic sexual transmission of HIV revealed by molecular phylodynamics. Plos Med. 2008;5:e50.
19. Brenner BG, Roger M, Routy JP, et al. High rates of forward transmission events after acute/early HIV-1 infection. J Infect Dis. 2007;195:951–959.
20. Brenner BG, Roger M, Stephens D, et al. Transmission clustering drives the onward spread of the HIV epidemic among men who have sex with men in Quebec. J Infect Dis. 2011;204:1115–1119.
21. NC Department of Health and Human Services.
North Carolina 2012 HIV/STD Surveillance Report. Raleigh, NC: NC Division of Public Health; 2013:2013.
22. Centers for Disease Control and Prevention. Recommendations for partner services programs for HIV infection, syphilis, gonorrhea, and chlamydial infection. MMWR. 2008;57(RR-9).
23. Dailey Garnes NJ, Moore ZS, Cadwell BL, et al. Previously undiagnosed HIV infections identified through cluster investigation,
North Carolina , 2002-2007. AIDS Behav. 2015;19:723–731.
24. Kuruc JD, Cope AB, Sampson LA, et al. Ten years of screening and testing for acute HIV infection in
North Carolina . J Acquir Immune Defic Syndr. 2016;71:111–119.
25. Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal. 2006:1695. Complex Systems.
26. R: A Language and Environment for Statistical Computing [computer program]. Vienna, Austria: R Foundation for Statistical Computing; 2015.
27. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–1797.
28. Hall T. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 98/98/NT. Nucl Acids Symp Ser. 1999;41:95–98.
29. Price MN, Dehal PS, Arkin AP. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol. 2009;26:1641–1650.
30. Tavaré S. Some probabilistic and statistical problems in the analysis of DNA sequences. In: American Mathematical Society: Lectures on Mathematics in the Life Sciences; 1986;17:57–86.
31. Shimodaira H, Hasegawa M. Multiple comparisons of log-likelihoods with applications to phylogenetic inference. Mol Biol Evol. 1999;16:1114–1116.
32. Struck D, Lawyer G, Ternes AM, et al. COMET: adaptive context-based modeling for ultrafast HIV-1 subtype identification. Nucleic Acids Res. 2014;42:e144.
33. Drummond AJ, Rambaut A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol. 2007;7:214.
34. Rambaut A, Suchard MA, Xie D, Drummond AJ. Tracer v1. 6. 2014:2015.
35. Cope AB, Powers KA, Kuruc JD, et al. Ongoing HIV transmission and the HIV care continuum in
North Carolina . PLoS One. 2015;10:e0127950.
36. Chan PA, Hogan JW, Huang A, et al. Phylogenetic investigation of a statewide HIV-1 epidemic reveals ongoing and active transmission networks among men who have sex with men. J Acquir Immune Defic Syndr. 2015;70:428–435.
37. NC HIV/STD Surveillance Unit. 2015
North Carolina HIV/STD Surveillance Report. Raleigh, NC: NC Division of Public Health; 2016:2016.
38. Amirkhanian YA. Social networks, sexual networks and HIV risk in men who have sex with men. Curr HIV/AIDS Rep. 2014;11:81–92.
39. Callegaro A, Svicher V, Alteri C, et al. Epidemiological network analysis in HIV-1 B infected patients diagnosed in Italy between 2000 and 2008. Infect Genet Evol. 2011;11:624–632.
40. Wertheim JO, Leigh Brown AJ, Hepler NL, et al. The global transmission network of HIV-1. J Infect Dis. 2014;209:304–313.
41. Skoura L, Metallidis S, Buckton AJ, et al. Molecular and epidemiological characterization of HIV-1 infection networks involving transmitted drug resistance mutations in Northern Greece. J Antimicrob Chemother. 2011;66:2831–2837.
42. Ross LL, Horton J, Hasan S, et al. HIV-1 transmission patterns in antiretroviral therapy-naive, HIV-infected North Americans based on phylogenetic analysis by population level and ultra-deep DNA sequencing. PLoS One. 2014;9:e89611.
43. Ng KT, Ong LY, Lim SH, et al. Evolutionary history of HIV-1 subtype B and CRF01_AE transmission clusters among men who have sex with men (MSM) in Kuala Lumpur, Malaysia. PLoS One. 2013;8:e67286.
44. Hue S, Clewley JP, Cane PA, et al. HIV-1 pol gene variation is sufficient for reconstruction of transmissions in the era of antiretroviral therapy. AIDS. 2004;18:719–728.
45. Antoniadou ZA, Kousiappa I, Skoura L, et al. Short communication: molecular epidemiology of HIV type 1 infection in northern Greece (2009-2010): evidence of a transmission cluster of HIV type 1 subtype A1 drug-resistant strains among men who have sex with men. AIDS Res Hum Retroviruses. 2014;30:225–232.
46. Buskin SE, Ellis GM, Pepper GG, et al. Transmission cluster of multiclass highly drug-resistant HIV-1 among 9 men who have sex with men in Seattle/King County, WA, 2005–2007. J Acquir Immune Defic Syndr. 2008;49:205–211.
47. Little SJ, Kosakovsky Pond SL, Anderson CM, et al. Using HIV networks to inform real time prevention interventions. PLoS One. 2014;9:e98443.
48. Dennis AM, Herbeck JT, Brown AL, et al. Phylogenetic studies of transmission dynamics in generalized HIV epidemics: an essential tool where the burden is greatest? J Acquir Immune Defic Syndr. 2014;67:181–195.
49. Hall HI, Frazier EL, Rhodes P, et al. FRLBX05-Oral Abstract: Continuum of HIV care: differences in care and treatment by sex and race/ethnicity in the United States. Paper presented at: International AIDS Conference 2012; July 22–27, 2012; Washington, DC.
50. Bradley H, Hall HI, Wolitski RJ, et al. Vital signs: HIV diagnosis, care, and treatment among persons living with HIV—United States, 2011. MMWR 2014;63:1113–1117.
51. Singh S, Bradley H, Hu X, et al. Men living with diagnosed HIV who have sex with men: progress along the continuum of HIV care—United States 2010. MMWR. 2014;63:829–833.
52. Lubelchek RJ, Hoehnen SC, Hotton AL, et al. Transmission clustering among newly diagnosed HIV patients in Chicago, 2008 to 2011: using phylogenetics to expand knowledge of regional HIV transmission patterns. J Acquir Immune Defic Syndr. 2015;68:46–54.
53. Castor D, Low A, Evering T, et al. Transmitted drug resistance and phylogenetic relationships among acute and early HIV-1-infected individuals in New York City. J Acquir Immune Defic Syndr. 2012;61:1–8.
54. Brenner B, Wainberg MA, Roger M. Phylogenetic inferences on HIV-1 transmission: implications for the design of prevention and treatment interventions. AIDS. 2013;27:1045–1057.
