Kang Jeon-Young, Aldstadt Jared
CyberGIS Center for Advanced Digital and Spatial Studies; Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, IL, USA.
Department of Geography, University at Buffalo, The State University of New York, Buffalo, NY, USA.
Comput Environ Urban Syst. 2019 May;75:170-183. doi: 10.1016/j.compenvurbsys.2019.02.006. Epub 2019 Feb 21.
Sensitivity analysis (SA) in spatially explicit agent-based models (ABMs) has emerged to address some of the challenges associated with model specification and parameterization. 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 in ABMs are responsible for mismatch between model outcomes and observations. In this paper, we propose the use of multiple scale space-time patterns in variance-based global sensitivity analysis (GSA). A vector-borne disease transmission model was used as the case study. Input factors used in GSA include one related to the environment (introduction rates), two related to interactions between agents and environment (level of herd immunity, mosquito population density), and one that defines agent state transition (mosquito extrinsic incubation period). The results show parameters related to interactions between agents and the 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 GSA 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.
空间显式基于主体的模型（ABM）中的敏感性分析（SA）已经出现，以应对与模型规范和参数化相关的一些挑战。对于空间显式ABM，有人主张通过比较空间或时空模式来评估模型。然而，对于理解ABM中的参数值在多大程度上导致模型结果与观测值之间的不匹配，人们关注较少。在本文中，我们建议在基于方差的全局敏感性分析（GSA）中使用多尺度时空模式。以一个媒介传播疾病传播模型作为案例研究。GSA中使用的输入因素包括一个与环境相关的因素（引入率）、两个与主体和环境之间的相互作用相关的因素（群体免疫水平、蚊虫种群密度）以及一个定义主体状态转变的因素（蚊虫外在潜伏期）。结果表明，与主体和环境之间的相互作用相关的参数对模型再现观测模式的能力有很大影响，尽管这种影响的程度因时空尺度而异。此外，结果突出了空间显式ABM中对参数值的时间依赖性敏感性。本研究中进行的GSA有助于识别在模型中需要仔细参数化的输入因素，以实现能够在多个时空尺度上很好地再现观测模式的ABM。
