Abstract

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Progress toward hepatitis C virus (HCV) elimination in the United States is not on track to meet targets, as injection drug use continues to drive increasing HCV incidence. Computational models are useful in understanding the complex interplay of individual, social, and structural level factors that might alter HCV incidence, prevalence, transmission, and disease progression. However, these models need to be informed with real socio-behavioral data. We conducted a meta-analysis of research studies spanning 20 years of research and interventions with people who inject drugs in metropolitan Chicago to produce parameters for a synthetic population for a computational model. We then fit an exponential random graph model (ERGM) using the network estimates from the meta-analysis in order to develop the dynamic component of a realistic agent-based model.

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