1. HIV/AIDS AND SEXUAL NETWORS Dimitri Fazito (CEDEPLAR/UFMG) International Workshop on Demography of Lusophane African Countries 22nd - 24th of May, 2007

2. The global AIDS epidemic in 2006 An estimated 39.5 million people are living with HIV/AIDS. The vast majority are aged 15-49 years. 4.3 million people were newly infected with the virus in 2006. 2.9 million people died of AIDS. There are 11,000 new infections and nearly 8,000 deaths daily. 2.3 million children (under 15 years) are living with HIV. Nearly one-third of the world’s HIV-infected people – or 13 million – lives in countries classified by the World Bank as heavily burdened by debt. Of the 41 poorest and most indebted countries, 34 are in sub-Saharan Africa.

3. Vulnerable Groups Children: Globally, 2.3 million children are living with HIV; Women: 2.5 times more vulnerable to HIV infection than men. UNAIDS estimates that 60% of all people living with HIV in sub- Saharan Africa were women; Young People: More than one-third of all people living with HIV/AIDS are under the age of 25, accounting for 2 million infections each year. In sub-Saharan Africa, more than half of all new infections are among young people, with girls being particularly affected; Sex Workers: High rates of HIV infection have been found among sex workers. Higher proportion in Asia, especially among women; Injecting Drug Users: UNAIDS estimates that injecting drug use accounts for one-third of new infections outside sub-Saharan Africa, especially in Europe, North and Latin America and Asia; Prisioners: The prevalence of HIV infection in prisons is higher than that in the general population. In South Africa it is estimated that 41% of prisioners are HIV positive.

4. Estimated HIV/AIDS, 2003

5. Table 1: Maplecroft’s HIV/AIDS Index (HAI) Worldwide (2006) Country HAIRankCategoryAdults (%) Adults (#) Women (#) Children (#) Deaths (#) Orphans (#) Mozambique0.132147extreme16.1%1,600,000960,000140,000 510,000 Angola1.491137extreme3.7%280,000170,00035,00030,000160,000 South Africa1.733133extreme18.8%5,300,0003,100,000240,000320,0001,200,000 Guinea- Bissau 2.613120high3.8%29,00017,0003,2002,70011,000 India2.696117high0.9%5,600,0001,600,000No Data Brazil4.18591high0.5%610,000220,000No Data14,000No Data USA5.16769medium0.6%1,200,000300,000No Data16,000No Data Portugal5.54751medium0.4%32,0001,300No Data<1000No Data South Korea7.71420low0.1%13,0007,400No Data<500No Data Finland10.0001low0.1%1,900<1000No Data<100No Data HIV/AIDS Index (HAI): level of prevalence in adults (%) + total number of infected adults (year) + country’s capacity of disease contention Countries Studied: 148 Category Risk: extreme (0–2.5), high (2.5–5.0), medium (5.0–7.5) and low (7.5–10) Source: Maplecroft & UNAIDS, 2007

6. Why Networks Matter Sexual behaviors are socially sanctioned in groups (eg. dyads, personal networks, cliques and cores) within the context of social norms (cultural values and social interactions); Culture Social position Role expectation Gender identities Community values Symbolic representations Individual Sexuality

7. Why Networks Matter How social structure influences sexual behavior? Network Analysis Collective Patters / Structure of Sexual relations Individual Attributes Normative prescriptions Dyadic Relationships Sexual Behavior Network Properties

8. Local network involvement The strength and qualities of particular network ties (“direct embeddedness”) Degree, tie strength, condom use, etc One’s position in the overall network (“structural embeddedness”) Centrality, local-network density, transitivity, membership. Global network structure The global structure of the network affects how goods can travel throughout the population. Distance distribution Connectivity structure Among the most challenging tasks for modeling networks is building a robust link from the first to the second. Why Networks Matter

9. Why Networks matter Disease transmission occurs through diffusion networks ( “one-by-one” personal contacts); Sexual risk is a function of relational and structural composition of networks (dyads and cliques); Network ties established within structuring environments do not occur at random – the network “clustering” effect;

10. A simplified multi-layered framework Social units (y) individuals... Ties among social units (x) person-to-person... Settings (s) geographical sociocultural... For example: Interactions between tie variables depend on node attributes social selection effects Interactions between ties depend on proximity through settings context effects

11. The Network “Clustering” Effect When different processes can lead to similar macro signatures: For example: “clustering” typically observed in social nets Sociality – highly active persons create clusters (eg. Leaders, drug-dealers, brokers) Homophily – assortative mixing by attribute creates clusters (eg. Ethinic cliques, religious communities) Triad closure – triangles create clusters (eg. Work and schoolmates)

12. Friend of a friend, or birds of a feather? 1.Homophily:: People tend to chose friends who are like them, in grade, race, etc. (“birds of a feather”), triad closure is a by- product 2.Transitivity:: People who have friends in common tend to become friends (“friend of a friend”), closure is the key process

13. Why do Networks Matter? Local vision

14. Why do Networks Matter? Global vision

15. Networks are structurally cohesive if they remain connected even when nodes are removed Node Connectivity 01 23 Disease Transmission and the Network Density

16. Variation in the Timing and Intensity of HIV Epidemic The rate of sexual partner acquisition The impact of “core groups” activities The presence of different sexually transmitted diseases (infection amplification) Higher mobility (migration) The rate of concurrent (simultaneous) sexual partnerships and duration The rate of partnership stability

17. Definition of Concurrency Concurrent partnerships Same contact rate (5/yr), but the timing and sequence of partnerships is different From M. Morris (2006) 1 2 3 4 5 Serial monogamy 1 2 3 4 5 time

18. 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 2 11 3 2 3 monogamyconcurrency monogamy

19. Connectivity in sparse networks High degree hubsLow degree linking Both have mean degree = 1.9

20. Connectivity in sparse networks and Concurrency “Low degree” “High degree” -Some individuals are highly connected (core transmitters) -Perceived as “high risk” -Potentially more likely to motivate prevention behavior -Most individuals are less connected -Perceived as “lower risk” -Potentially less likely to motivate prevention behavior

21. Structural degree and cohesion gives rise automatically to a clear notion of embeddedness, since cohesive sets nest inside of each other supporting concurrency partnership and contagion 17 18 19 20 2 22 23 8 11 10 14 12 9 15 16 13 4 1 75 6 3 2 Structural Properties: Concurrency and Speed of HIV/AIDS Transmission

22. Degree Networks, Cohesion, Concurrency and transmission core In largest component: In largest bicomponent: 2% 0 41% 5% 64% 15% 10% 1% Mean: 1.74 Mean: 1.80 Mean: 1.86 Largest components Mean: 1.68 Number of Partners Bicomponents in red Source: Martina Morris, Univ. of Washingtion, used with permission from a presentation given at a meeting on concurrent sexual parnerships and sexually transmitted infections at Princeton University, 6 May 2006.

23. Worldwide, almost all studies show increased risks with increased sexual partners Partner reduction has been associated with declines in HIV at the population level in both concentrated and generalized epidemic settings Multiple sexual partnerships

24. Morris et al. (2006) MenWomen Concurrencies reported UgandaUSThaila nd Ugand a US 071.784.874.096.492.5 119.49.710.63.45.1 20.52.310.90.01.3 38.33.34.60.31.1 Total any concurrency 28.315.226.03.67.5 Uganda vs. US and Thailand

25. 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. Empirical Findings: Rate of Concurrency and Duration

26. Concluding Remarks: the importance of networks Large populations exhibit network structure –Social, sexual, infrastructure, transportation Large epidemics need to be understood as many small epidemics linked by networks (clustering and overlapping effects) Incorporating “multi-scale” structure of the world in epidemic models can explain multi-modality and resurgence of HIV/AIDS “Rare events” (e.g. one person getting on a plane) can have big consequences. Such events can be modeled by Network Models (eg. Small World, Random Graphs, Free Scale Networks) Population structure itself can be used as control measure (e.g. intermediate connections)