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Multi-objective model exploration of hepatitis C elimination in an agent-based model of people who inject drugs

Eric Tatara, Alexander Gutfraind, Nicholson T. Collier, Scott J. Cotler, Marian Major, Basmattee Boodram, Harel Dahari, Jonathan Ozik

Winter Simulation Conference

 accepted June 2019

[Website, Full Paper, Pubmed]


Hepatitis C (HCV) is a leading cause of chronic liver disease and mortality worldwide and persons who inject drugs (PWID) are at the highest risk for acquiring and transmitting HCV infection. We developed an agent-based model (ABM) to identify and optimize direct-acting antiviral (DAA) therapy scale-up and treatment strategies for achieving the World Health Organization (WHO) goals of HCV elimination by the year 2030. While DAA is highly efficacious, it is also expensive, and therefore intervention strategies should balance the goals of elimination and the cost of the intervention. Here we present and compare two methods for finding PWID treatment enrollment strategies by conducting a standard model parameter sweep and compare the results to an evolutionary multi-objective optimization algorithm. The evolutionary approach provides a pareto-optimal set of solutions that minimizes treatment costs and incidence rates.

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