MASHIV

1. MASHIV Multi-Agent Simulation of HIV in MSM CommunitiesA Study of Concurrency Robert Puckett, UH Manoa, November 20, 2014

2. Outline • Core Concepts • HIV • Concurrency • MSM • Agents • MASHIV System Design • HIV Model • Agents • Sexual Negotiation • Modes of Operation • Q & A

3. Research Questions • Can a multi-agent simulation of individual level HIV transmission illuminate the impact that concurrent sexual relationships have on the HIV epidemic in MSM communities? • What is the impact of PrEP amid concurrency on the resulting HIV epidemic? • Can we overcome stochastic variability to provide consistent analysis and recommendations?

4. Really Quick Overview Core concepts

5. HIV Stages • Primary/Acute HIV Infection (PHI) • Virulence: High • Estimated 43% of infections due to PHI period [Wawer] • Occurs 2-4 weeks after exposure • Duration: 1.5 – 12 months [Blaser, 2013] • Only 2/3 experience symptoms • Fever, fatigue, pharyngitis, weight loss, night sweats, lymphadenopathy, myalgia, headache, nausea • Symptoms commonly result in misdiagnosis • Correctly diagnosed as PHI in 1000 of 60 million cases • HIV undetectable by antibody tests for several weeks into PHI [Coplan]

6. HIV Stages • Asymptomatic Period • Virulence: Low, but present • Duration: 8+ years, untreated • Acquired Immuno-deficiency Syndrome (AIDS) • Immune system badly damaged (CD4 count < 200) • Susceptible to • Opportunistic infections • Certain cancers • Life expectancy • 1-3 years depending on presence of opportunistic infections

7. Viral Plasma Load by Stage

8. Concurrency Serial Monogamy time 1 2 3 4 5 Concurrent partnerships time 1 2 3 4 5 • ”Overlapping sexual partnerships where sexual intercourse with one partner occurs between two acts of intercourse with another partner.” [UNAIDS, 2009] • Key Components • Duration of relationships • Contact frequency • Number of partners • Virulence of partners

9. monogamy concurrency Concurrency & PHI • The concern • Concurrent relationships will create a highly connected/reachable sexual network • PHI stage is highly virulent, unlikely to be detected • Result • Waves of PHI stage HIV infection sweep through sexual network

10. The MSM Community • MSM – Behavior specific term • Includes gay, bisexual, male sex workers, transgendered • Any other men who engage in same-gendered sex • Statistics • 51 % of new HIV cases in the US were MSM • 14-19% of urban MSM are HIV+ [Goodreau 2007] • HIV prevalence in MSM increasing in most developed countries since 90s [Grulich 2008]

11. MSM & HIV Risk • Risk Factors • Unprotected Anal Receptive Sex (UAR) • Disproportionately affected by STIs [Goodreau] • Other behavioral factors • “Bareback “ sex seeking, sero-sorting, high-risk venues • Higher proportion of concurrent relationships? • Concurrent relationships • General US Population: 11% of men • San Francisco urban MSM cohort: 78% of men • Note: vastly different sample populations • Less sexual role segregation • Heterosexual sexual role defined by gender • MSM sexual role defined by preference (insertive/receptive) • Role versatility allows HIV to spread more easily

12. An Overview Multi-agent systems

13. What are Agents? • A programming construct meant to represent a real-life entity or role • Key characteristics • Autonomous • Goal oriented • Self-organizing • Exist in / React to environment • Local knowledge • Decentralized • Compete, coordinate, cooperate • In an individual-level multi-agent simulation of HIV • 1 agent == 1 person

14. Multi-Agent System • The Agents • Cognitive model • Personal history of interactions with the world/agents • The Environment • Observable by the agents • Does not directly control the agents • The Rules • Actions the agents may choose • Reactions to the environment/agents • Goal-oriented behavior • Limitations on agent behavior

15. Why use Agents for HIV? Intuitive pairing of agents and people Simple rules can result in complex behavior Allows for observing the dynamics of individual-level decision making on the HIV epidemic Potentially useful for guiding real-world studies and interventions

16. The Multi-Agent Simulation for HIV Transmission MASHIV

17. MASHIV • Goal • Use JAVA to develop a multi-agent simulation of HIV for the MSM community • Determine the role of concurrency in HIV epidemics of MSM • Track and Analyze • HIV Prevalence/Incidence trends • Proportion of infections resulting from PHI • Concurrency measure of population over time

18. MASHIV Operation • User defines parameter set • Global vs. Population parameter assignment • Population definition • Runtime • Initialization • Process User Parameters • Generate Relationship Schemas • Generate Agents • Main Loop • Update Agents • Update HIV Disease Progression • Updating Existing Relationships • Sex, HIV Transmission, Relationship Ending • Date & Add Relationships • Statistics Collection

19. Parameters

20. Person Agent Represents an MSM person Forms/Ends Relationships Evaluates potential partners Reacts to HIV infection

21. Relationship Instance Agents can have different schemas and expectations for relationship.

22. Relationship Schema Duration vs. Probabilistic Mode

23. Relationships • Wanting to Date • Modes • Duration-based: Time since last date • Probabilistic: Probability of formation • Factors • Existing steady relationship • Number of relationships • Dating Pools • 10 random dating agents • Sexual role compatible • Both seeking same relationship type (Casual, Steady) • Steady: no repeats; Casual: repeats allowed • Dating Evaluation…

24. Dating Evaluation

25. The Multi-Agent Simulation for HIV Transmission MASHIV hiv model

26. HIV Model • Initialization • Distributed to population based upon user input • HIVInstance is created • Transmission Risk • Viral load of stage determines virulence • Safe sex practice determines risk • Progression toward mortality • CD4 compartment model

27. HIV Instance • Handles HIV progression & mortality • Logs key information • Source of Infection • Stage of infection source

28. HIV Transmission • Condom Use • Transmission Risk • S – Stage risk multiplier • Art() - ART risk reduction

29. HIV Transmission • Reception Risk • P() : Sexual role risk • PrEP() – PrEP risk reduction • Circ() – Circumcision risk reduction

30. HIV Infection • Fast-moving vs. Regular Speed • Virus Stages • PHI – 90 days • Asymptomatic & AIDS • CD4 Model

31. HIV Progression • CD4 Compartment Model • Adapted from Spectrum/EPP specification [UNAIDS] • Used to progress agents from infection to death • Compartments correlate to CD4 counts of individuals • Annual progression rates define progression between compartments and compartment mortality • Reduces progression rate to account for ART usage

32. Lambdas

33. Mus

34. Alphas

35. Transmission Probabilities Tool for viewing transmission probabilities for model

36. A Multi-Agent Simulation of HIV MASHIV Interface

37. Main Window & Menus

38. Parameter Set

39. Parameter Set

40. Interactive Dash

41. Interactive Dash - Running

42. Interactive Dash

43. Interactive Dash – Adding Set

44. Aggregate Set Editor

45. Aggregate Set - Running

46. Aggregate Set - Running

47. Progression in MASHIV Analysis tool for observing Stage & Group Progression in Model

48. Mortality In MASHIV Tool for observing mortality without ART

49. Mortality In MASHIV • Tool for analyzing mortality with ART • Graphs represent ART started in first CD4 group assigned • Assumes only mortality based ART failure • Lack of background mortality

50. Questions and Answers Q&A