MASHIV Multi-Agent Simulation of HIV in MSM Communities A Study of Concurrency Robert Puckett, UH Manoa, November 20, 2014

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

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? 3

CORE CONCEPTS Really Quick Overview

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] 5

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 6

Viral Plasma Load by Stage 7

Concurrency ”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 Serial Monogamy Concurrent partnerships time

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 concurrencymonogamy 9

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] 10

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 11

MULTI-AGENT SYSTEMS An Overview

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 13

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 14

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 15

MASHIV The Multi-Agent Simulation for HIV Transmission

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 17

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 18

Parameters 19

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

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

Relationship Schema Duration vs. Probabilistic Mode 22

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… 23

Dating Evaluation 24

MASHIV HIV MODEL The Multi-Agent Simulation for HIV Transmission

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 26

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

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

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

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

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 31

Lambdas 32

Mus 33

Alphas 34

Transmission Probabilities Tool for viewing transmission probabilities for model 35

MASHIV INTERFACE A Multi-Agent Simulation of HIV

Main Window & Menus 37

Parameter Set 38

Parameter Set 39

Interactive Dash 40

Interactive Dash - Running 41

Interactive Dash 42

Interactive Dash – Adding Set 43

Aggregate Set Editor 44

Aggregate Set - Running 45

Aggregate Set - Running 46

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

Mortality In MASHIV Tool for observing mortality without ART 48

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 49

Q&A Questions and Answers