Agent-based model of persons who inject drugs in metropolitan Chicago

Transcription

1 Agent-based model of persons who inject drugs in metropolitan Chicago Sasha Gutfraind University of Illinois at Chicago 2014 Community Outreach Intervention Projects

2 Research team University of Illinois at Chicago SG Basmattee Boodram Richard M. Novak Lawrence Ouellet Loyola University Chicago Harel Dahari SG Consortium for Modeling and Analysis of Treatments and Interventions George Mason University Ates Hailegiorgis Los Alamos National Lab Sara Del Valle Susan M. Mniszewski Alan S. Perelson Chicago Department of Public Health Nikhil Prachand Food and Drug Administration Marian Major Stephen Feinstone 2

3 I. Setting Hepatitis C virus (HCV) Infects 180M globally, and 4.1M in the US Transmitted through blood contamination Persons who inject drugs (PWID) 1.4M in the US 40-90% are HCV+ Outcomes of HCV Fibrosis, Hepatocellular Carcinoma Liver transplant (~$580k/person) Treatment (~$130k/person) 3

4 The situation in Chicago Estimated 32,000 PWID in metropolitan Chicago 44% are HCV positive 6% are HIV positive 98% use Heroin Heroin injection toolkit 4

5 Aim: comprehensive HCV strategy I. Map and Forecast the population of II. persons who inject drugs (PWID) Forecast the prevalence and incidence of Hepatitis C epidemic in PWID III. Evaluate Interventions e.g. syringe exchange, antivirals, vaccines, behavioral 5

6 Outline I. Method 1. Data-driven synthetic population 2. Discrete-event agent-based model APK II. Findings 1. Significant aging of the PWID population 2. Declining HCV prevalence* 3. Network dynamics drive new infections 6

7 II. Approach: Agent-based Model Agent-based Model (ABM[1]) Software that simulates epidemics[2] Tracks all individuals ( agents ) in the population Behavior rules, instead of big equations Strengths Forecast complex social systems Test hypotheses, evaluate individual-level interventions Weaknesses Simplified reality; needs primary data 1. Bonabeau PNAS Stroud, Del Valle et al. JASSS

8 Example: Agent-based model of influenza Germann et al. 280m virtual people 8

9 Agent-based Pathogen Kinetics (APK) model 1. Population and geography Synthetic population of PWID in Chicago Use large surveys 2. Social drug network Networks form spontaneously near home or place of purchase Networks enable drug/syringe sharing and transmit HCV 3. Simulation methods Repast Sim 2.1 toolkit (Java) APK s graphical interface Imputation of HCV a multi-linear regression model Latin hypercube sampling for sensitivity analysis (Python + R) HPC parallelization on UT s Stampede supercomputer 9

10 APK vs. Existing models APK Hahn [1] Hellard [2] Hutchinso n[3] Krahn[4] Marti n[5] Martin[6] Martin [7] Martin [8] Mather [9] Rolls [10] Vickerman [11] Vickerman [12] Zeiler [13] EpiRisk (U01 proposal) Model type ABM ABM ODE ABM Markov chain ODE ODE ODE ODE ABM ABM ODE ODE ODE ABM HCV states Network d - - d s d (d=dynamic) Geography Data-driven population Vaccine Treatment Future - AV,O - AV,L AV AV AV AV,L L AV - AV,O AV,O AV Longitudinal Validation Additional features Graph ics - HCV Econ. genotypes - analysis - HCV genotypes - Econ. analysis Viral-host dynamics [1] J. A. Hahn, D. Wylie, J. Dill, M. S. Sanchez, J. O. Lloyd-Smith, K. Page-Shafer, and W. M. Getz, Potential impact of vaccination on the hepatitis C virus epidemic in injection drug users, Epidemics, vol. 1, pp , [2] M. E. Hellard, R. Jenkinson, P. Higgs, M. A. Stoové, R. Sacks-Davis, J. Gold, M. Hickman, P. Vickerman, and N. K. Martin, Modelling antiviral treatment to prevent hepatitis C infection among people who inject drugs in Victoria, Australia, Med. J. Aust., vol. 196, no. 10, pp , Jun [3] S. Hutchinson, S. Bird, A. Taylor, and D. Goldberg, Modelling the spread of hepatitis C virus infection among injecting drug users in Glasgow: Implications for prevention, Int. J. Drug Policy, vol. 17, pp , [4] M. D. Krahn, A. John-Baptiste, Q. Yi, A. Doria, R. S. Remis, P. Ritvo, and S. Friedman, Potential cost-effectiveness of a preventive hepatitis C vaccine in high risk and average risk populations in Canada, Vaccine, vol. 23, pp , [5] N. K. Martin, P. Vickerman, J. Grebely, M. Hellard, S. J. Hutchinson, V. D. Lima, G. R. Foster, J. F. Dillon, D. J. Goldberg, G. J. Dore, and M. Hickman, Hepatitis C virus treatment for prevention among people who inject drugs: Modeling treatment scale-up in the age of direct-acting antivirals: Martin et al., Hepatology, vol. 58, no. 5, pp , Nov [6] N. K. Martin, P. Vickerman, G. R. Foster, S. J. Hutchinson, D. J. Goldberg, and M. Hickman, Can antiviral therapy for hepatitis C reduce the prevalence of HCV among injecting drug user populations? A modeling analysis of its prevention utility, J. Hepatol., vol. 54, no. 6, pp , Jun [7] N. K. Martin, P. Vickerman, and M. Hickman, Mathematical modelling of hepatitis C treatment for injecting drug users, J. Theor. Biol., vol. 274, no. 1, pp , Apr [8] N. K. Martin, P. Vickerman, A. Miners, G. R. Foster, S. J. Hutchinson, D. J. Goldberg, and M. Hickman, Cost-effectiveness of hepatitis C virus antiviral treatment for injection drug user populations, Hepatology, vol. 55, no. 1, pp , Jan [9] D. Mather and N. Crofts, A computer model of the spread of hepatitis C virus among injecting drug users, Eur. J. Epidemiol., vol. 15, pp. 5 10, [10] D. A. Rolls, R. Sacks-Davis, R. Jenkinson, E. McBryde, P. Pattison, G. Robins, and M. Hellard, Hepatitis C Transmission and Treatment in Contact Networks of People Who Inject Drugs, Plos One, vol. 8, no. 11, p. e78286, Nov [11] P. Vickerman, M. Hickman, and A. Judd, Modelling the impact on Hepatitis C transmission of reducing syringe sharing: London case study, Int. J. Epidemiol., vol. 36, p. 396, [12] P. Vickerman, N. Martin, and M. Hickman, Can Hepatitis C virus treatment be used as a prevention strategy? Additional model projections for Australia and elsewhere, Drug Alcohol Depend., vol. 113, no. 2 3, pp , [13] I. Zeiler, T. Langlands, J. M. Murray, and A. Ritter, Optimal targeting of Hepatitis C virus treatment among injecting drug users to those not enrolled in methadone maintenance programs, Drug Alcohol Depend., vol. 110, no. 3, pp , Aug

11 Making virtual drug injectors (agents) Conventional approaches Use univariate stats (prevalence, age) to generate agents Con: loss of correlations in age, health, location Data-driven approach (here) Survey responses Agents Information-rich simulation (thanks to UIC-COIP data) Captures correlations Con: not uniform accuracy 11

12 Chicago drug use surveys in APK 12

13 Turnover in the PWID population Stochastic attrition (2.3% / year) due to Incarceration Mortality Stochastic cessation of injecting drug use Mean career: 30.3 years Introduction of newly-initiating PWID Mostly HCV-negative Tend to be younger & ethnically different Overall population size is constant 13

14 The Chicago Synthetic Population PWID PWID newly North South Suburban Under Over Harm Non-HR January initiating Side Side Reduct (HR) Total Female (%) Male (%) NH Black (%) Hispanic (%) NH White (%) Age (years) Length of Career (years) Daily injections Fraction receptive sharing In Degree* Out Degree* Harm Reduct. (HR) (%) % 68.3% 65.5% 34.5% non-hr (%) % 31.7% 34.5% 65.5% HCV RNA+ (%) % 21.7% 41.6% 19.7% HCV Antibody + but not infected (%) % 8.2% 17.0% 7.9%

15 PWID Network Network = all ties of drug & equipment sharing Network is formed by encounters: (1) home neighborhood (2) drug market (3) elsewhere Rate( d ij ) p i p j c c d c d h 2 ij 2 ij c d c d if if d ij otherwise d max samedrug market Equation 1. The rate of encounters between persons in zones i and j. Network is dynamically rewired Relationship time T T ~ Exp(1.7y) 15

16 Stages of Hepatitis C in APK 16

17 APK Main display

18 Limitations 1. Small sample effects, esp. in suburbs (some corrections were applied) 2. Simplified model of HCV kinetics (steady viral titers) 3. Transmission through needle sharing only (small probability of other transmission) 4. Stationary behaviors 5. Simplified network No gender roles No individual turnover rates 18

19 III. Findings - Aging Contributors to aging: Low cessation of injecting drug use Low attrition of existing PWID Newly-initiating PWID are young 19

20 Findings: Declining prevalence HR=Harm reduction 20

21 Findings: Declining Prevalence 21

22 Dilbert on harm reduction

23 Incidence Forecast Static network (theoretical) *HR=Harm reduction programs Dynamics is responsible for 2/3 of infections Overall incidence ( all ): 0.51 vs

24 Timing of new infections 30% of cases in 1 st year of drug use 20% of cases in 2 nd year of drug use Interventions need to start early

25 Top risk groups: NH Whites, nonhr HR=Harm reduction programs NH=Non-Hispanic 25

26 Validation of APK 26

27 Conclusions 1. APK an agent-based geographic and network model of injection drug use 2. Forecasting of population and prevalence 1. Significant aging of the PWID population 2. Declining HCV prevalence* 3. Network dynamics drive new infections Questions? Sasha Gutfraind 27

28 Optional slides The Encore 28

29 III. Findings - composition HR=Harm reduction programs By 2020, ~80% are older than 30 years old 29

30 ANOVA of HCV for NHBS09 (n=335) HCV: [1=HCV antibody present, 0=otherwise] Duration: years since initiating injection drug use Sharing: fraction of injections involving reception of needles 30