1Division of Community Health Sciences, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, United States of America.
2Department of Behavioral and Social Sciences, School of Public Health, Brown University, Providence, Rhode Island, United States of America.
3Department of Medicine, Chicago Center for HIV Elimination, University of Chicago, Chicago, Illinois, United States of America.
4Division of Hepatology, Department of Medicine, Loyola University Medical Center, Maywood, Illinois, United States of America.
5Decision and Infrastructure Sciences Division, Argonne National Laboratory, Lemont, Illinois, United States of America.
Progress toward hepatitis C virus (HCV) elimination in the United States is not on track to meet targets set by the World Health Organization, as the opioid crisis continues to drive both injection drug use and increasing HCV incidence. A pragmatic approach to achieving this is using a microelimination approach of focusing on high-risk populations such as people who inject drugs (PWID). Computational models are useful in understanding the complex interplay of individual, social, and structural level factors that might alter HCV incidence, prevalence, transmission, and treatment uptake to achieve HCV microelimination. However, these models need to be informed with realistic sociodemographic, risk behavior and network estimates on PWID. We conducted a meta-analysis of research studies spanning 20 years of research and interventions with PWID in metropolitan Chicago to produce parameters for a synthetic population for realistic computational models (e.g., agent-based models). We then fit an exponential random graph model (ERGM) using the network estimates from the meta-analysis in order to develop the network component of the synthetic population.