Davey Smith, MD, MAS, of the UC San Diego AntiViral Research Center presents, "How Should We Target Prevention Interventions?"

1. The UC San Diego AntiViral Research Center sponsors weekly
presentations by infectious disease clinicians, physicians and
researchers. The goal of these presentations is to provide the most
current research, clinical practices and trends in HIV, HBV, HCV, TB
and other infectious diseases of global significance.
The slides from the AIDS Clinical Rounds presentation that you are
about to view are intended for the educational purposes of our
audience. They may not be used for other purposes without the
presenter’s express permission.
AIDS CLINICAL ROUNDS

2. Network Informed Prevention
Study?
Davey Smith, MD
Associate Professor of Medicine
UCSD

3. Who knows who these guys?

4. John Snow’s Map

5. Cholera Outbreak: space and time
http://en.wikipedia.org/wiki/John_Snow_%28physician%29

6. Snow Veterinary Research 2011 42:56 doi:10.1186/1297-9716-42-56

7. How to define a network
 Contact versus Transmission
◦ Behavior
◦ Social
◦ Venue
 Molecular (genetic relatedness)
◦ Origins
◦ Timing
◦ Transmission linkages
◦ Transmission networks
Retroviruses. Cold Spring Harbor Laboratory Press; c1997. Van Heuverswyn F. et al
Nature. 2006 Nov 9;444(7116):164. Kober et al. Science 2000.

8. Why Father’s Day is Important

9. Do Genetic Backgrounds Matter?
(Or how to blame everything on your parents)
Smithsonian Natural History Museum 2011 www.hiv.lanl.gov/content/sequence/HIV/RE
VIEWS/nomenclature/Nomen.html

10. Kosakovsky Pond and Smith Clin Infect Dis. 2009 May 1;48(9):1306-9.
Viral Background
Host Background (e.g. HLA)
CTL
How can background genetics influence HIV disease?

11. Defining Transmission Network and
Clusters in San Diego
 Even in this geographically localized and aggressively sampled
cohort, genetic distances <1.5% were very rare (0.25 %).
Pairwise Nucleotide Distance
Count
0.00 0.05 0.10 0.15
0100030005000
Distances <1.5%
0.000 0.005 0.010 0.015
020406080
Potential transmission
Network
N=921 unique seqs
Non-subtype B

12. Switching Gears
Networks are everywhere
How many can you name is10 seconds?

13. Network structure
http://cleverdeveloper.com/blog/?p=111

14. SO CAN MOLECULAR
EPIDEMIOLOGYTELL US
ANYTHING ABOUT
NETWORKS?

15. HIV is transmitted along a complex contact network
Concept Contact Network Transmission network
Node Individual HIV+ individual
Edge A contact that could lead to HIV transmission,
e.g. sexual, shared needle
Transmission event
Degree = edges
connected to a node
Number of contacts associated with a node Number of transmissions
associated with a node
HIV+ HIV-
Contact w/o tranmission
Transmission
Degree = 7
Degree = 1
Degree = 3
Transmission network is a
subset of the contact network

16. HIV Molecular Epidemiology Has
Been Used Before
 Montreal and London: Primary infection drives local
epidemics. (Brenner JID 2007, Fraser PLoS Med 2008, Leigh Brown JID 2011)
 San Diego: Partner tracing identifies hubs. (Smith AIDS
2009)
 Tijuana/San Diego: border offers some barrier (Mehta
ARHR 2010)
 Europe: some countries serve as hubs (Retrovirology 2009)

17.  United States: Clustering associated with
◦ Not using ARVs
◦ CD4 >350 cells/ml
◦ Viral load 10,000-100,000 copies/ml
◦ Women cluster with other women
◦ African Americans clustered with other African Americans
(Aldous CID 2012)

18. Mol Epi-Network techniques are powerful
 Can readily infer patterns of world-wide HIV-1
transmission
 Found 4342 clusters using ~85,000 sequences from 141
countries.
 A large IDU and MSM cluster spanned 18 countries
(primarily from the former USSR).
Wertheim JID 2013

19. Defining Transmission Network and
Clusters in San Diego
 Even in this geographically localized and aggressively sampled
cohort, genetic distances <1.5% were very rare (0.25 %).
Pairwise Nucleotide Distance
Count
0.00 0.05 0.10 0.15
0100030005000
Distances <1.5%
0.000 0.005 0.010 0.015
020406080
Potential transmission
Network
N=921 unique seqs
Non-subtype B

20. What kind of HIV network is
San Diego?
A random intervention would not work in a scale
free network, but a targeted intervention would!
Scale-free network
Same size but one has
Hubs
Random network

21. San Diego Network is Scale Free
and Deeply Sampled
Count
50100150
2000 2002 2004 2006 2008 2010
2050100200500
Year
Nodes
Edges

22. So, can we use molecular epi and
networks to do anything meaningful?
 Hypothesis:We can reduce R0<1 in the
San Diego HIV epidemic by identifying
and intervening on the “hubs” of the
network.

23. Properties of SDPIC network
 54% of individuals connected to someone
else
 A small proportion of individuals have many
connections (high degree)
 HIV transmission networks largely follow the
model of preferential attachment – high
degree nodes attract more new connections.
 Perhaps the network degree of the individual
can be informative for intervention?

24. It pays to target highly connected
nodes
Degree = 7
Degree = 1
Targeting a low degree
node has a local effect
Targeting a high degree
node has a global effect

25. Transmission Network Score
 DefineTNS as extent of the observed out-degree of the node and probability
estimated from network degree distribution for the year of sampling.
 A highly connected node has a high likelihood to have future connections-TNS
Cluster

26. Transmission Network Score
 For each participant, we determined the degree (i.e.
‘connectivity’) of their node within the network on the date
that corresponded to the first HIV pol sequence.
 This was then used to predict how many new edges (i.e.
contacts) the participant would acquire within the first year
following their baseline sequence.

27. Early versus delayed treatment

28. LET USVISUALIZETHE
PROBLEM
Brought to you by Nadir Weibel
and Sanjay Mehta

29. Applications
 Help us to understand the origins and
dispersal of viral epidemics
 Reconstructing the history of
epidemics
Understanding the detailed epidemiology of an
epidemic in real time may help us design, prioritize
and direct prevention efforts

30. Obtaining a granular understanding of how the HIV
epidemic occurred in a particular region will help us with
future predictions on the how the epidemic will progress
Example
We find that HIV transmissions are occurring at a slow steady rate in
IVDU, but occurring in bursts of related infections in MSM
How?
Suggests that we use this information to focus
resources on contact tracing of acutely infected
MSM

31. Obtaining a real-time picture of the HIV epidemic in a
region complete with socio-demographic correlates of
risk will help us choose and target our interventions
Example
 Several new HIV infections are identified among heterosexual
migrant farm-workers that are not IVDU
 Infections are linked together, and to several infections
identified in another migrant farm-worker camp
How?
Suggests that we target our interventions
toward sex workers servicing these camps

32.  Clever Information Visualization helps in
making hidden connections apparent
New Methods?
Finding the cause of a cholera
outbreak
John Snow’s map of London of
1854 allowed to trace back an
unexplainable outbreak to a
malfunctioning water pump on
Broad Street
… just by superimposing cholera
cases (in red) on the street map.

33. Visualizing HIV Tracking: Challenges (1)
 (Too?) many variables
◦ Demographic information (e.g. age, gender, race, ethnicity,
marital status, sexual orientation, education level, birth
country and city)
◦ Clinical data (e.g. HIV stage at diagnosis, current CD4 levels,
HAART use)
◦ HIV sequence and phylogenetic clustering
◦ Sexually transmitted infections and sexual risk (e.g.
transaction sex, commercial sex, number of partners)
◦ Illicit drug use (e.g. needle sharing, use of heroin, meth,
alcohol)
◦ Risk venues (e.g. bathhouses, adult bookstores, bars,
internet sites used to meet partners for sex or drug use)
◦ Geography (e.g. residence, border crossing).

34. Visualizing HIV Tracking: Challenges (2)
 Continuous data over time
◦ How do we visualize trends?
◦ How can we identify significant snapshots?
 Unknown relationships between variables
◦ How do variable influence each other?
◦ Different views on data highlight different relationships
 Different kind of networks
◦ Phylogenetic
◦ Geographic
◦ Social

35. Approach
 Stanford’s Data-Driven Documents
(D3)
D3: Data-Driven Documents
Michael Bostock, Vadim Ogievetsky, Jeffrey Heer
IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis),
2011
http://d3js.org/
http://vis.stanford.edu/papers/d3

36. D3 Example / Inspiration

37. Preliminary work (1)
 HIV Datasets
◦ >1000 data entries collected
◦ 3 different sites:
 Primary Infection Cohort
 UCSD Owen Clinic
 San Diego County Epidemiology Department
 Phylogenetic cluster analysis
◦ Distance between single sequences
◦ 111 clusters found

38. Preliminary Work (2)
 Visualization
◦ Clusters grouped in circles
◦ Descriptive statistics for the cluster
 Number of subjects per cluster (size of the circle)
 average age (color of the circle)
 male/female percentage
 Ethnicity
 Sexual Orientation

39. Demo

40. Next Steps
 Multi-layered descriptions of
phylogenetic clusters
 Network connectivity within
the cluster and across clusters
 Time-based dynamic update of
clusters
 Identification of specific
important changes

41. In a (not too) far future?
 Dynamic Real-time
Geographic visualizations
 Automatic detection of
anomalies
◦ Machine learning algorithms
 Ultra-ScaleVisualizations
◦ Calit2 Wall Displays
(VROOM)

42. Public Health Approach
• Prevention measures cannot be a one-size fits all.
• Use network analytical methods to characterize
HIV transmission dynamics within a population
• Use network and phylogenetic methods to identify
cores, bridges and hubs
• Focus outreach and prevention programs on
identified cores, bridges and hubs
• Since network dynamics can change (venues,
susceptible populations, STIs, drug use, etc.)
network surveillance should also be dynamic.
• Epidemiologic surveillance
• HIV genetic surveillance (genotypes)
41

43. Legal:Take the ‘C’ out of CSI
 The identification of individuals who have
transmitted HIV can have significant legal
implications.
 Therefore for these data to be used for public
health purposes there must be:
◦ Decriminalization of unintended HIV transmission during
consensual exposure.
◦ Legal recognition that phylogenetic linkage using pol
sequences does not prove beyond a reasonable doubt
that transmission between partners occurred, such as
with a third party intermediary
◦ The legal precedent for admitting phylogenetic evidence
has set the bar extremely high [Scaduto PNAS, 2010], and
requires very complex analyses of multiple source and
recipient sequence isolates.
42

44. Analyze HIV pol
sequences in real
time in the
background of all
sequences
Generate
TNS
TNS
high
Generate Risk Profile and
Intervention Plan
DatAvailable for Risk Profile
• Demographics
• Risk behavior (MSM, IDU)
• Venues (bars, bathhouses, shooting galleries, internet sites)
• Estimated duration of infection (acute, early, chronic)
• Clinical (CD4 and viral load)
• No personally identifying information
Ring Intervention
Target identified venues
HIV Testing, Counseling, STI testing,
Needle Exchange, etc.
Intervention Team
• Nurse
• Social worker
• Test Counselor
Intervention success can be
measured by observing phylogenetic
lineages dying out (Wertheim et al.
2011)
TNS low=
follow

45. Take Home Message

46. Coverage- Acknowledgements
Sanjay Mehta
Nadir Weibel
Sergei Kosakovsky Pond
Susan Little
Kimberly Bouwer
Richard Garfein
Igor Grant
Tom Patterson
Doug Richman
Stephanie Strathdee
Jason Young
Joel Wertheim