@article{dd9007b789e6433385c3a51a3b1a534a,
title = "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.",
author = "Kang, {Jeon Young} and Alexander Michels and Andrew Crooks and Jared Aldstadt and Shaowen Wang",
note = "This work is supported in part by the US National Science Foundation (NSF) under grant numbers 1443080, 1743184, and 1824961, an internal data science grant of the University of Illinois at Urbana‐Champaign, and a research grant of Kongju National University in 2021. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. Our computational work used Virtual ROGER, which is a cyberGIS supercomputer supported by the CyberGIS Center for Advanced Digital and Spatial Studies and the School of Earth, Society, and Environment at the University of Illinois at Urbana‐Champaign. The authors would like to thank Rebecca Vandewalle and Dandong Yin at the CyberGIS Center for Advanced Digital and Spatial Studies for helpful discussions at the early stage of this research. This work is supported in part by the US National Science Foundation (NSF) under grant numbers 1443080, 1743184, and 1824961, an internal data science grant of the University of Illinois at Urbana-Champaign, and a research grant of Kongju National University in 2021. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. Our computational work used Virtual ROGER, which is a cyberGIS supercomputer supported by the CyberGIS Center for Advanced Digital and Spatial Studies and the School of Earth, Society, and Environment at the University of Illinois at Urbana-Champaign. The authors would like to thank Rebecca Vandewalle and Dandong Yin at the CyberGIS Center for Advanced Digital and Spatial Studies for helpful discussions at the early stage of this research.",
year = "2022",
month = feb,
doi = "10.1111/tgis.12837",
language = "English (US)",
volume = "26",
pages = "100--128",
journal = "Transactions in GIS",
issn = "1361-1682",
publisher = "Wiley-Blackwell",
number = "1",
}