A CyberGIS Approach to Spatiotemporally Explicit Uncertainty and Global Sensitivity Analysis for Agent-Based Modeling of Vector-Borne Disease Transmission

Abstract

Although agent-based models (ABMs) provide an effective means for investigating complex interactions between heterogeneous agents and their environment, they might hinder an improved understanding of phenomena being modeled due to inherent challenges associated with uncertainty in model parameters. This study uses uncertainty analysis and global sensitivity analysis (UA-GSA) to examine the effects of such uncertainty on model 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 model inputs, UA-GSA needs to be performed at multiple spatiotemporal scales. Unfortunately, performing comprehensive UA-GSA comes with considerable computational cost. In this article, our cyberGIS-enabled spatiotemporally explicit UA-GSA approach helps to not only resolve the computational burden but also measure dynamic associations between model 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 might 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.

代理人基模型（ABM）是研究异构主体与其环境间复杂相互作用的有效方法, 但由于这个模型本质上存着参数不确定的问题, 可能会阻碍人们更好地理解正在建模的现象。本研究通过不确定性分析和全局灵敏分析（UA-GSA）研究这种不确定性对模型输出的影响。但可修改面积单位的问题可能会影响 UA-GSA 中使用的统计数据, 因此为了了解模型输入数据的尺度变化效果, 就需要在多个时空尺度上进行 UA-GSA 分析。但问题是, 进行全面 UA-GSA 分析会产生巨大的计算成本。在本文中, 我们使用 cyberGIS 予以实现的时空显式 UA-GSA 方法, 不仅有助于解决计算方面的负担, 还可以测量模型输入和输出之间的动态关联。一组计算和建模实验显示出, 输入因素会对建模输出变量产生与尺度关联影响。换言之, 大多数输入因素会在某个具体区域产生相对较大的影响, 但可能不会影响其他区域的结果。此外, 我们的时空显式 UA-GSA 方法还体现了输入因素对建模结果的影响, 这种影响尤其体现出空间和时间上是集群性, 例如发生了传染病传播。

Aunque los modelos basados en agente (ABM) son un medio efectivo para investigar interacciones complejas entre agentes heterogéneos y su entorno, podrían, sin embargo, entorpecer la comprensión mejorada del fenómeno que se modela debido a los inherentes retos que van asociados con la incertidumbre de los parámetros del modelo. Este estudio usa análisis de incertidumbre y análisis de sensibilidad global (UA-GSA) para examinar los efectos de tal incertidumbre en los resultados de aplicación del modelo. Sin embargo, las estadísticas usadas en UA-GSA es posible que estén afectadas por el problema de la unidad areal modificable. Por tanto, para examinar los efectos de variación de escala en los insumos del modelo, los UA-GSA deben realizarse a múltiples escalas espaciotemporales. Infortunadamente, realizar UA-GSA de amplia cobertura viene acompañado de un costo computacional considerable. En este artículo, nuestro enfoque de UA-GSA espaciotemporalmente explícito de base cíberSIG ayuda no solo a resolver el peso computacional sino también a medir las asociaciones dinámicas entre los insumos y productos del modelo. Un conjunto de experimentos computacionales y modelantes muestra que los factores de insumo tienen impactos dependientes de la escala en la modelación de la variabilidad del producto. En otras palabras, la mayoría de los factores de insumo tienen impactos relativamente grandes en una determinada región, pero podrían no influir los resultados en otras regiones. Además, nuestro enfoque de UA-GSA espaciotemporalmente explícito arroja luz sobre los efectos de los factores de insumo en modelar resultados que están en particular apiñados espacial y temporalmente, tal como ocurre en la transmisión de enfermedades contagiosas.

Key Words:

• agent-based modeling
• cyberGIS
• global sensitivity analysis
• spatiotemporal scale
• uncertainty analysis

关键词:

• 代理人基模型
• cyberGIS
• 全局敏感性分析
• 时空尺度
• 不确定性分析

Palabras clave:

• análisis de incertidumbre
• análisis de sensibilidad global
• cíberSIG
• escala espaciotemporal
• modelado basado en agente

Additional information

Funding

This article and associated materials are based in part upon work supported by the National Institutes of Health (R01 GM083224 and P01 AI034533) and the National Science Foundation under grant numbers 1443080 and 1743184. 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 funding agencies. 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.

Notes on contributors

Jeon-Young Kang

JEON-YOUNG KANG is a Postdoctoral Research Associate at the CyberGIS Center for Advanced Digital and Spatial Studies at the University of Illinois at Urbana–Champaign, Urbana, IL 61801. E-mail: [email protected]. His research interests focus on GIScience, agent-based modeling, cyberinfrastructure, and health geography.

Jared Aldstadt

JARED ALDSTADT is an Associate Professor of Geography at the State University of New York at Buffalo, Buffalo, NY 14261. E-mail: [email protected]. He is a medical geographer and spatial modeler specializing in the ecology of infectious disease, including mosquito-borne pathogens.

Rebecca Vandewalle

REBECCA VANDEWALLE is a PhD Student in the Department of Geography and Geographic Information Science at the University of Illinois at Urbana–Champaign, Urbana, IL 61801. E-mail: [email protected]. Her research interests are GIScience, high-performance computing, cyberinfrastructure, and natural hazards.

Dandong Yin

DANDONG YIN is a PhD Candidate in the Department of Geography and Geographic Information Science at the University of Illinois at Urbana–Champaign, Urbana, IL 61801. E-mail: [email protected]. His research interests are GIScience, high-performance computing, cyberinfrastructure, and natural hazards.

Shaowen Wang

SHAOWEN WANG is a Professor and Head of the Department of Geography and Geographic Information Science at the University of Illinois at Urbana–Champaign, Urbana, IL 61801. E-mail: [email protected]. His research interests focus on GIScience and geographic information systems, advanced cyberinfrastructure and cyberGIS, complex environmental and geospatial problems, computational and data sciences, high-performance and distributed computing, and spatial analysis and modeling.