Using Multiple Scale Space-Time Patterns in Sensitivity Analysis for Spatially Explicit Agent-Based Models

Authors: Jeon-Young Kang*, CyberGIS Center for Advanced Digital and Spatial Studies, University of Illinois at Urbana-Champaign, Jared Aldstadt, SUNY-Buffalo
Topics: Geographic Information Science and Systems, Geography and Urban Health, Medical and Health Geography
Keywords: agent-based model, sensitivity analysis, spacetime pattern, dengue
Session Type: Paper
Day: 4/4/2019
Start / End Time: 3:05 PM / 4:45 PM
Room: Capitol Room, Omni, East
Presentation File: No File Uploaded


Sensitivity analysis (SA) is used to address the challenges associated with model specification and parameterization in spatially explicit agent-based models (ABMs). For spatially explicit ABMs, the comparison of spatial (or spatio-temporal) patterns has been advocated to evaluate models. Nevertheless, less attention has been paid to understanding the extent to which parameter values embedded in ABMs are responsible for mismatch between model outcomes and observations. In this study, we propose the use of multiple scale space-time patterns in SA. A vector-borne disease transmission model was used as the case study. Input parameters used in SA include one related to the environment (introduction rate), two related to interactions between agents and environment (level of herd immunity and mosquito population density), and one that defines agent state transition (mosquito extrinsic incubation period). The results show factors related to interactions between agents and environment have great impact on the ability of a model to reproduce observed patterns, although the magnitudes of such impacts vary by space-time scales. Additionally, the results highlight the time-dependent sensitivity to parameter values in spatially explicit ABMs. The SA performed in this study helps in identifying the input factors that need to be carefully parameterized in the model to implement ABMs that well reproduce observed patterns at multiple space-time scales.

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