An integrated framework of global sensitivity analysis and calibration for spatially explicit agent‐based models

Abstract

Calibration of agent‐based models (ABMs) is a major challenge due to the complex nature of the systems being modeled, the heterogeneous nature of geographical regions, the varying effects of model inputs on the outputs, and computational intensity. Nevertheless, ABMs need to be carefully tuned to achieve the desirable goal of simulating spatiotemporal phenomena of interest, and a well‐calibrated model is expected to achieve an improved understanding of the phenomena. To address some of the above challenges, this article proposes an integrated framework of global sensitivity analysis (GSA) and calibration, called GSA‐CAL. Specifically, variance‐based GSA is applied to identify input parameters with less influence on differences between simulated outputs and observations. By dropping these less influential input parameters in the calibration process, this research reduces the computational intensity of calibration. Since GSA requires many simulation runs, due to ABMs' stochasticity, we leverage the high‐performance computing power provided by the advanced cyberinfrastructure. A spatially explicit ABM of influenza transmission is used as the case study to demonstrate the utility of the framework. Leveraging GSA, we were able to exclude less influential parameters in the model calibration process and demonstrate the importance of revising local settings for an epidemic pattern in an outbreak.

Investigating factors in indoor transmission of respiratory disease through agent‐based modeling

“…ABMs have been utilized to simulate complex and dynamic phenomena through the interactions of heterogeneous agents and their environment (Kang et al, 2022). Crowd simulation research for pedestrian modeling can be classified into two categories: multi‐agent and physics oriented.…”

“…Sensitivity analysis is a tool for reducing model uncertainty by identifying how the variance of inputs contributes to the outcome of ABMs and discrepancies between outcomes and observed data (Kang et al, 2022;Kang & Aldstadt, 2019;Ligmann-Zielinska, 2013;Saltelli et al, 1999). In this study, we employed an ML emulator outputs of simulations to reduce computational intensity of sensitivity analysis (Chen et al, 2021;Ligmann-Zielinska et al, 2020).…”

A decision fusion-based method for global sensitivity analysis of complicated experiments

“…Kang proposed an integrated framework for global sensitivity analysis (GSA) and calibration, called GSA-CAL, which was used to identify input parameters that have a small impact on the difference between the simulation output and the observed results. By dropping these less influential input parameters from the calibration process, this study reduces the computational intensity of the calibration [4]. With the development of experimental techniques, the focus of SA research is not on how to provide analytical efficiency, but on how to distil the knowledge that guides experimental design.…”