In order to join virtual sessions, you must be registered and logged-in(Were you registered for the in-person meeting in Denver? if yes, just log in.) 
Note: All session times are in Mountain Daylight Time.

A CyberGIS approach to spatiotemporally explicit uncertainty and global sensitivity analysis for agent-based modeling of vector-borne disease transmission

Authors: Jeon-Young Kang*, University of Illinois at Urbana-Champaign, Jared Aldstadt, State University of New York at Buffalo, Rebecca Vandewalle, University of Illinois at Urbana-Champaign, Dandong Ying, Google, Shaowen Wang, University of Illinois at Urbana-Champaign
Topics: Cyberinfrastructure, Geographic Information Science and Systems, Spatial Analysis & Modeling
Keywords: CyberGIS, agent-based modeling, sensitivity analysis, dengue virus, spatial simulation
Session Type: Paper
Presentation File: No File Uploaded

Agent-based models (ABMs) serve an effective means for exploring complex interactions between heterogeneous agents and their environment, they may hinder to gain an improved understanding of processes being modeled due to inherent challenges associated with uncertainty in model parameters. In this study, we perform uncertainty analysis and global sensitivity analysis (UA-GSA) to measure the effects of the uncertainty on modeling outputs. The statistics used in UA-GSA, however, are likely to be affected by the modifiable areal unit problem. Therefore, to examine the scale varying effects of modeling inputs, UA-GSA needs to be performed at multiple spatiotemporal scales. Unfortunately, comprehensive UA-GSA comes with considerable computational cost. Our cyberGIS-enabled spatiotemporally explicit UA-GSA approach helps to not only resolve the computational burden, but also to examine dynamic associations between modeling inputs and outputs. A set of computational and modeling experiments shows that input factors have scale-dependent impacts on modeling output variability. In other words, most of the input factors have relatively large impacts in a certain region, but may not influence outcomes in other regions. Furthermore, our spatiotemporally explicit UA-GSA approach sheds light on the effects of input factors on modeling outcomes that are particularly spatially and temporally clustered, such as the occurrence of communicable disease transmission.

To access contact information login