SPEW: Synthetic Populations and Ecosystems of the World
SPEW: Synthetic Populations and Ecosystems of the World
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DOI：
10.1080/10618600.2018.1442342
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发表时间：
2018-01-01
影响因子：
2.4
通讯作者：
Eddy, William F.
中科院分区：
文献类型：
--
作者：
Gallagher, Shannon;Richardson, Lee F.;Eddy, William F.
基于智能体的模型(ABM)模拟受限环境中自治智能体之间随时间的相互作用，通常用于模拟传染病的传播。ABMS使用有关代理人及其环境的信息作为输入，统称为“合成生态系统！”以前用于生成合成生态系统的方法有一些局限性：它们不是开源的，不能适应新的或更新的输入数据源，或者不允许使用替代方法来采样试剂的特征和位置。我们介绍了一个生成合成生态系统的一般框架，称为“世界合成种群和生态系统”(SPEW)。SPEW允许研究人员从各种采样方法中选择代理特征和位置，并以开源R包的形式实现。我们分析了SPEW的准确性和计算效率，给出了针对代理特征和位置的不同采样方法，并提供了一套统计和图形工具来筛选我们生成的生态系统。SPEW已经在70多个国家和地区的大约100,000个地理区域产生了50多亿个人类代理，可以在网上获得。
Agent-based models (ABMs) simulate interactions between autonomous agents in constrained environments over time and are often used for modeling the spread of infectious diseases. ABMs use information about agents and their environments as input, together referred to as a "synthetic ecosystem!' Previous approaches for generating synthetic ecosystems have some limitations: they are not open-source, cannot be adapted to new or updated input data sources, or do not allow for alternative methods for sampling agent characteristics and locations. We introduce a general framework for generating synthetic ecosystems, called "Synthetic Populations and Ecosystems of the World" (SPEW). SPEW lets researchers choose from a variety of sampling methods for agent characteristics and locations and is implemented as an open-source R package. We analyze the accuracy and computational efficiency of SPEW, given different sampling methods for agent characteristics and locations, and provide a suite of statistical and graphical tools to screen our generated ecosystems. SPEW has generated over five billion human agents across approximately 100,000 geographic regions in over 70 countries available online.