Colorado Springs “Project 90”, John Potterat, PI Visual by Jim Moody, Network Modeling Project Partnership networks and HIV: Global consequences of local decisions Network Modeling Project: Steve Goodreau, Mark Handcock, Martina Morris (UW), Phillipa Pattison, Garry Robins (Melbourne), Dave Hunter (PSU), Jim Moody (OSU), Rich Rothenberg (Emory), Tom Snijders (Groningen) Grad students: Krista Gile, Deven Hamilton, Dave Schruth

Adults and Children Estimated to Be Living with HIV/AIDS 94% of all persons living with HIV/AIDS are in developing countries … extreme variation in regional prevalence (Dec 2003)

Momentum for the future: 4-6M new HIV infections per year worldwide 15 – 39% 5 – 15% 1 – 5% 0.5 – 1.0% 0.1 – 0.5% 0.0 – 0.1% not available Source:UNAIDS/WHO July % % + 60% + 160% + 30% + 40% + 20% HIV prevalence in adults, end 2001

Why this massive global variation in HIV prevalence? HIV is spread by intimate contact with partners (unlike TB, SARS, or malaria) So – is the number of partners we have the key determinant of epidemic intensity? Many reasons to think so -- Contact rate is central to traditional epidemiological models

Individual level model for the probability of infection l ( p ) =  +  c + … Contact rate Your probability of infection l (p) is a function of your contact rate:  is the percent change in the odds of infection with each additional partner.  > 0 more partners increase your odds of infection.

Population level model for epidemic transmission “Reproduction rate” for infected cases, Like a reproduction rate in a population R 0 =  c D P(transmit)/contact # contacts/yr # yrs infected R 0 is the expected number of new cases caused by an infected person at the start of an epidemic. R 0 > 1 means an epidemic can take off, this is known as the “epidemic threshold”

Contact rates link individual and population levels: In studies of STD clinic patients in the 1970s, repeat cases were found to contribute disproportionately to the total caseload. 3-7% of persons accounted for 30% of caseload, due to reinfection “ [ R 0 ] in the non-core is less than 1. … Thus all cases are caused directly or indirectly by the core.” The core causes STDs to persist in a population. “Core Group” theory

Nature 411, (2001) 21 June The web of human sexual contacts [E]pidemics arise and propagate much faster in scale-free networks … [T]he measures adopted to contain or stop the propagation of diseases need to be radically different. (Liljeros et al., Nature, 2001) "The Bush administration's policy to give drugs to mothers with children is completely irrelevant to stopping AIDS in Africa," Mr. Barabasi said. "It's much better to go and target the hubs.” (NYT, January 2003) Contact rates are also central to recent “Scale free” models

So it would seem that contact rates are the key to explaining variations in prevalence. Or are they?

HIV is different No cure, so no reinfection –Reinfection was central to the definition of a “core group” Long duration of infection (remember  cD) –So high c less important for reaching epidemic threshold of R 0 How can we tell if the contact rate is or is not the key behavioral parameter? Let’s look at some data

Lifetime Number of Partners Country Prevalence Uganda 18% HIV (Rakai Sexnet study) United States 1% HIV (NHSLS study) Thailand 2% HIV (BRAIDS study)

Thailand Non commercial Commercial And Thailand? …

So contact rates are clearly not the whole story... Many other aspects of partnership networks. mixing by personal characteristics, geographic clustering & bridging, etc. Will look here at timing and sequence of partnerships. affects both the cross-sectional structure and the temporal evolution of the network and the epidemic focus on the role of “concurrent partnerships”

Definition of Concurrency Concurrent partnerships Same contact rate (5/yr), but the timing and sequence of partnerships is different Serial monogamy time

Why concurrency matters 1.Less protection afforded by sequence 2. virus-eye view: Less time lost locked in partnership 3. Larger “connected component” in the network monogamyconcurrency monogamy

2000 persons in network the mean # infected over 100 simulations at that level of concurrency Concurrency measured as the mean # of partners for each dyad (mean degree of the line graph) Early simulation results suggested big effects (1995-7) Simulate a partnership network over time, let a disease spread through it Number of partnerships is the same in every case, just vary concurrency Serial monogamy 7/10 partners have a concurrent partnership

Rakai SEXNET study (1993-4) Rakai district in Southern Uganda Random sample, 90 communities Age range 15-49, men and women Face-to-face interviews, 90 min Response rate: 92% N = 1640, 1428 sexually active “Local network” questionnaire … last partner, 77 questions incl. dates of first and last sex, … partner before that Empirical evidence: UGANDA

Concurrency in Uganda 28% of men report a concurrency last year 45% of men with 2+ lifetime partners 13% of men with 3+ lifetime partners report that all 3 were concurrent 4% of women year old Rs … is common in the last year Last 3 partners in last year Configuration Concurrencies Reported MenWomen Total with any concurrency or

Overlap duration for the two partnerships Median overlap for active concurrencies is 3 years for men 1 year for women ~40% of concurrent partnerships are active at the time of interview For completed partnerships, overlaps are 10 and 6 months respectively. … Most concurrent partners are long term partners “Short term” = 1 month or less CompositionMenWomen 2 long term Long & short, or 2 short

… Thailand also has high levels of concurrency MenWomen Concurrencies reported UgandaUSThailandUgandaUS Total any concurrency Uganda vs. US and Thailand

Thailand has higher levels of short-term partners paired with long-term Median Overlap in months (% of concurrencies) So the duration of overlap is much shorter CompositionUgandaUSThailand 2 long term long, 1 short ObservationUgandaUSThailand Completed 10 (59%) 4 (95%) 1 day (95%) Active 36 (41%) 10 (5%) 24 (5%) Focus on men The pairing patterns are different

Do these differences matter? Use a simulation study to gauge the impact Same number of long term partnerships in both populations Total number of partnerships higher in Thailand due to additional CSP Uganda: match observed concurrency distribution for LTP Thailand: add observed commercial sex ties 80% of men visit CS, 50% visit in a year, average 3/yr LTP-CSP concurrencies only Constant probability of transmission once infected Pegged to generate median 18% in Ugandan scenario After 10 yrs, how different are the epidemics? No vital dynamics Uganda vs. Thailand

How do differences in infectivity and condom use affect these epidemics? The network differences alone explain about half the prevalence differential (8%) Asymetric infectivity could explain another third (5%) The rest may be due to condom use with commercial sex partners (3%) Approach: Run these simulations 1000 times, on much larger networks (n=1000) -- vary infectivity and condom use

Thailand’s population has many more partners, but the network connections are extremely short duration. Despite much higher contact rates, transmission dynamics are dampened, and prevalence will remain low Uganda’s population has fewer partners, but the network is more continuously connected over time. This long term concurrency amplifies transmission dynamics, allowing prevalence to rise much higher. Bottom line

Concurrency creates a transmission core In largest component: In largest bicomponent: 2% 0 41% 5% 64% 15% 10% 1% Mean: 1.74 Mean: 1.80 Mean: 1.87 Largest components Bicomponents In red Mean: 1.68 Number of Partners

Implications for prevention research 1.Prevention message should be modified “ One partner at a time” 2.Epidemiological surveillance data needs to change Need to learn how to sample networks “Local network” data the least expensive/intrusive to collect 3.Data analysis needs to change Networks contain dependent data Methods similar to spatial models Local network modeling may be sufficient

Implications for basic science Top down approach to modeling: small worlds, scale free Model the outcome: if it looks clustered, put in a “clustering parameter” Bottom up approach: local rules to global structures Model the mechanism: what are the rules people use to select partners? Attributes of partners (commercial sex workers, others) Timing and sequence of partnerships This time, the bottom up approach clearly won

Other benefits of bottom up modeling If a simple local model does well at explaining the global structure : Data are easy to collect: contact tracing not necessary Parameters are interpretable: They represent behavioral regularities that can be used to design intervention strategies Network epidemiology becomes a feasible tool for HIV prevention research

Thank You