Sexual Networks: Challenges (and Opportunities)

Transcription

1 Sexual Networks: Challenges (and Opportunities) Susan Little, M.D. Professor of Medicine University of California San Diego 1

2 Network Theory What network theory teaches us is that connections, even within the most complex systems, are not random (that is, they are not unpredictable). Instead, networks behave in ways that we can theorize, model, and predict. The PLoS Medicine Editors. It's the Network, Stupid: Why Everything in Medicine Is Connected. PLoS Med

3 Estimating Transmission Probability Evaluate the degree to which use of network-level data will provide an estimate of the probability of exposure to an infected partner. Identify which network parameters are the most useful predictors of HIV infection risk. 3 Christley R.M., et al. Am J Epidemiol Fichtenberg CM, et al. Sex Transm Infect. 2009

4 Sexual Networks: Outline Challenges Defining network structure Network position and risk of HIV/STI Defining the contribution of acute HIV 4

5 Network Structure: Background Sexual network structure has a central role in transmission dynamics of STIs Liljeros F, et al. Microbes Infect 2003; Newman MEJ. SIAM Rev 2003; Doherty IA, et al. J Infect Dis 2005; Keeling & Eames. J R Soc Interface 2005 The structure and dynamics of the sexual network may be more associated with HIV transmission than standard biological and behavioral factors Kottiri B, et al. JAIDS

6 HIV: Background HIV-1 is a measurably evolving pathogen, functioning near the evolutionary speed limit [Zeldovich et al, PNAS 2007] In treatment-naive patients, HIV-1 rapidly diverges from the founder strain within host (estimated as % per year in env C2-V5 [Lemey et al, PLoS Comp Biol 2007]). In recent paper by Pacold et al. on dual infections, we use 2% for pol and 5% for env as normal intra-patient variation [Pacold et al, AIDS Research and Human Retroviruses, In Press]. The bulk sequence of HIV-1 pol is nearly unique to each patient. Sequences sampled from different patients that are unusually close genetically could signify a potential transmission event. 6

7 Terminology 7

8 Type of Network Organization Two major types of networks have been extensively studied: 8 Many real networks, independent of their age, function, and scope, converge to similar architectures. Utilize epidemiologic and phylogenetic data to characterize the network

9 Scale-free networks Scale free or power law networks are defined by the form of the degree distribution: α - characteristic exponent New nodes prefer to attach to the more connected nodes in the network, a process of preferential attachment. Thus, highly connected nodes acquire more links leading to the natural emergence of hubs as the largest nodes. 9

10 Basic Network Statistics 10 The structure and function of complex networks. M.E.J. Newman

11 Example: San Diego HIV network On a log-log plot, probabilities and degrees are linearly related: The HIV network looks quite linear (correlation of 96%) with the characteristic exponent of -3 consistent with a scale free network. 11

12 Sexual Networks: Outline Challenges Defining network structure Network position and risk of HIV/STI Defining the contribution of acute HIV 12

13 Network Centrality Measures The more central individuals are at greatest risk of becoming infected during an outbreak. Degree centrality appeared to correlate well with risk of transmission risk. Concurrency (proportion of relationships between individuals that are coexistent) has been associated with transmission risk within sexual networks. 13 Christley R.M., et al. Am J Epidemiol. 2005

14 San Diego Primary Infection Cohort 831 protease and partial reverse transcriptase sequences N=562 unique patients (1-21 samples/patient) Samples spanning 06/1996-present Subtype distribution (baseline sequences only) 545 (97%) subtype B 3 C, 1 AE, 1G 12 URFs 14

15 Pairwise Genetic Distance Intra-subtype median (distance = ~5%) * Potential Linkage Inter-subtype (distance 12-15%) (distance 1%) 15 *Smith et al. AIDS, 2009

16 Network Structure <1% <1.5% Node Statistics Node 16

17 Cluster statistics Of 562 patients, 46.3% belonged to a 1%-delineated phylogenetic cluster 17 Cluster Size Number of clusters Sum Proportion of sequences in clusters, %

18 Phylogenetic Confirmation 94% 18

19 Modeling Scale-Free Networks A scale free network with exponent -3 can be generated by the preferential-attachment, or a friend of your friend is my friend model Predictions can be made as to the size of the underlying network that are consistent with the degree distribution of what we have sampled, i.e. we can try to estimate the size of the HIV-1 infection network in San Diego. 19

20 Simulations: random sampling Observed network: 562 nodes, 378 clusters, 756 edges. Random sampling (100 replicates): Size Median Edges Clusters

21 Simulations: random sampling Observed network: 562 nodes, 378 clusters, 756 edges. Random sampling (100 replicates): Size Median Edges Clusters ~2000 seems like a severe underestimate based on epidemiological & sequence data

22 Simulations: biased sampling In our cohort, sampling is biased to recruit partners. Assume that this bias is 45%, i.e. a partner of an already sampled individual is 45% more likely to be sampled than an epidemiologically unrelated individual Observed network: 562 nodes, 378 clusters, 756 edges. Random sampling (100 replicates): Median Size Edges Clusters

23 Simulations: biased sampling In our cohort, sampling is biased to recruit partners. Assume that this bias is 45%, i.e. a partner of an already sampled individual is 45% more likely to be sampled than an epidemiologically unrelated individual Observed network: 562 nodes, 378 clusters, 756 edges. Random sampling (100 replicates): Median ~10000 is a more reasonable estimate Size Edges Clusters

24 SD Network degree/vl Degree Statistics Degree Log10 VL Node Limited to 399 sequences with associated viral loads Degree =1 One sided Wilcoxon p=0.8 Degree =0 24

25 HIV Transmission and Degree Transmission often takes place in groups that have a disproportionate number of persons with many contacts (high degree ). Alternatively, transmission may be dominated by persons with relatively few contacts (low degree ), but high concurrency. Confounders: bridging (MSMW, IDU to non-idu through HET sex etc.), migration, food insecurity, under education, etc. 25

26 HIV Infection and Concurrency Behavior Ever had 1 sex partner HIV Status HIV HIV + Total Yes 63 (59%) 43 (41%) 106 (100%) sex partner No 122 (73%) 46 (27%) 168 (100%) Total 185 (67%) 89 (33%) 274 (100%) * P = Ever had Yes 42 (42%) 58 (58%) 100 (100%) ** group sex ( No 170 (72%) 66 (28%) 236 (100%) 1 partner) Total 212 (63%) 124 (37%) 336 (100%) ** P < (same highly significant result for group sex last 3 months) * 26

27 Challenge: Missingness Egocentric (A): Index subject A, names sex partners and describes them - characterizing A s personal network. Structure of the entire network cannot be determined Sociometric (B) : Iterative process whereby Index A names contacts, who are then traced, recruited, and interviewed. Sociograms generated of the network, with inevitable bias. Index A may not know/or provide names Can t find/recruit partners Only trace infected partners 27 Doherty I, et al. JID 2005

28 Reconstructing a transmission network Source outside sampling area Unsampled intermediary Incomplete sampling Recipient outside sampling area 28

29 Explosive Social Sexual Networking Opportunities Gay, bi, and curious men looking for a date can find him on Grindr, a new social networking/dating app. Grindr is mobile and works based on your location. Grindr uses the GPS technology to determine your exact location and instantly shows photos of the guys around you. Over 600,000 men globally are part of the Grindr Network, with 2,000 new guys joining every day. 29

30 Sociogram: Syphilis-Internet By interviewing index cases, notifying sexual partners, and raising community awareness (42% of named partners were identified and evaluated), 5 related cases were identified 30 Klausner, et al. JAMA 2000

31 Sexual Networks: Outline Challenges Defining network structure Network position and risk of HIV/STI Defining the contribution of acute HIV 31

32 Defining the Contribution of Acute Infection Acute infection is estimated to be times more infectious than during established infection. Wawer M, et al. JID 2005; Hollingsworth T, et al. JID 2008 Period of increased infectiousness may last up to 3 months after seroconversion. HIV status may affect the structure of the network: HIV diagnosis may decrease risk behavior and partnering patterns may change through serosorting. Marks et al. J AIDS 2005; Parsons et al. AIDS

33 Molecular Phylodynamics 2,126 anonymized HIV PR/RT sequences from London Relaxed-clock analysis of major clusters revealed an episodic pattern of transmission, most within 3-4 years ( ), and 25% within 6 months of infection. HIV pol sequences from Quebec cohorts evaluated: PHI patients (n=593 unique subtype B), Chronically infected (CI), ART-naive (n=135), CI, ART-experienced (n=660) Half (293/593) of all PHIs grouped into 75 different transmission clusters - the other half represented unique sequences Early infection may represent a major driver of onward transmissions 33 Brenner, et al. JID 2007, Lewis, et al. PLoS Med. 2008

34 Conclusion: HIV Network Data & Prevention: Limited network data would allow determination of HIV migration patterns (immigration/ emigration) within communities and appropriate response strategies. Access to subsequent network analysis could provide a very powerful tool for developing and testing public health prevention interventions. 34

35 Acknowledgments 35 Sergei Kosakovsky Pond Jason Young Wayne Delport Joel Wertheim Lin Liu Richard Garfein Sheldon Morris Davey Smith David Posada Konrad Scheffler Art Poon Victor DeGruttola Doug Richman Andy Leigh Brown Simon Frost Grant Support: By National Institutes of Health grants AI43638, AI074621; UCSD Centers for AIDS Research grant AI36214; and California HIV/ AIDS Research Program grant RN07-SD-702