A Discrete Simulation Optimization Approach Towards Calibration of an Agent-based Simulation Model of Hepatitis C Virus Transmission

TL;DR

This paper applies a stochastic ruler discrete simulation optimization method to calibrate an agent-based model of hepatitis C transmission, improving accuracy and efficiency in matching real-world prevalence data.

Contribution

It introduces a novel calibration approach using stochastic ruler optimization and exploits monotonic relationships to enhance calibration efficiency.

Findings

Successful calibration of the ABM to real-world HCV and IDU prevalences.

Demonstrated improved calibration efficiency using monotonicity-based methods.

Validated the effectiveness of the stochastic ruler approach in complex epidemiological models.

Abstract

This study demonstrates the implementation of the stochastic ruler discrete simulation optimization method for calibrating an agent-based model (ABM) developed to simulate hepatitis C virus (HCV) transmission. The ABM simulates HCV transmission between agents interacting in multiple environments relevant for HCV transmission in the Indian context. Key outcomes of the ABM are HCV and injecting drug user (IDU) prevalences among the simulated cohort. Certain input parameters of the ABM need to be calibrated so that simulation outcomes attain values as close as possible to real-world HCV and IDU prevalences. We conceptualize the calibration process as a discrete simulation optimization problem by discretizing the calibration parameter ranges, defining an appropriate objective function, and then applying the stochastic ruler random search method to solve this problem. We also present a method that exploits the monotonic relationship between the simulation outcomes and calibration parameters to yield improved calibration solutions with lesser computational effort.