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Integrating rich survey datasets in computational simulations of hepatitis C virus infection among injecting drug users in Chicago area

Basmattee Boodram, University of Illinois at Chicago 
Harel Dahari, Department of Medicine, Loyola University Chicago 
Sara Del Valle, Los Alamos National Laboratory 
Stephen Feinstone, The George Washington University 
*Alexander Gutfraind, University of Illinois at Chicago 
Marian Major, CBER, Food and Drug Administration 
Susan M Mniszewski, Los Alamos National Laboratory 
Richard Novak, University of Illinois at Chicago 
Lawrence J. Ouellet, University of Illinois at Chicago 
Alan S. Perelson, Los Alamos National Laboratory 
Nikhil Prachand, Chicago Department of Public 

Keywords: Injecting drug user, IDU, agent-based model, prevalence, hepatitis C virus, HCV, syringe exchange, epidemiology, Agent-based Pathogen Kinetics model, APK

Hepatitis C virus (HCV) infection is transmitted through contact with blood, and frequently occurs when injection drug users (IDUs) share contaminated needles. Computational modeling is an effective tool to simulate HCV transmission among IDUs and, in turn, inform the design of intervention strategies and vaccine trials. We have developed a prototype model – the Agent-based Pathogen Kinetics model (APK) – that models the complex interplay of behavior, social networks and geography. APK simulates HCV spread among the metropolitan Chicago IDU population. APK extrapolates from individual-level profiles built from raw data collected by several Chicago-based IDU research studies. We demonstrate how this data-driven approach reproduces complex correlations in the population, including the spatially heterogeneous distribution of infected individuals. Future applications of APK include the simulation of vaccine trials and other intervention and prevention strategies.