An Integrated Modeling Environment to Study the Coevolution of Networks, Individual Behavior, and Epidemics
An Integrated Modeling Environment to Study the Coevolution of Networks, Individual Behavior, and Epidemics
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DOI：
10.1609/aimag.v31i1.2283
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发表时间：
2010-03-01
期刊：
影响因子：
0.9
通讯作者：
Marathe, Madhav
中科院分区：
文献类型：
--
作者：
Barrett, Chris;Bisset, Keith;Marathe, Madhav
我们讨论了一个互动为基础的方法来研究社会技术网络，个人行为，这些网络上的传染过程之间的协同进化。我们用人口中的流行病作为这种现象的一个例子。这些方法包括使用第一原理方法开发合成但现实的国家级网络。与简单的随机图技术不同，这些方法将联合收割机现实世界的数据源与行为和社会理论相结合，以合成详细的社会联系（接近）网络。基于个体的宿主内疾病进展和宿主间传播模型，然后用于模拟传染过程。最后，将个体行为模型与疾病进展模型相结合，以开发一个复杂系统的现实表示，在该复杂系统中，个体行为和社会网络适应传染。这些方法包含在Simdemics中，Simdemics是一个支持流行病规划和响应的通用建模环境。Simdemics是专门为可扩展到拥有3亿代理的网络而设计的; Simdemics中的底层算法和方法都是面向计算的高性能方法。Simdemics的开发需要网络科学、机器学习、高性能计算、数据挖掘和行为建模方面的新进展。Simdemics与其他两个环境Simfrastruct和Didactic相结合，形成一个集成的网络环境。集成的网络环境为最终用户提供了对Simdemics的灵活和无缝的基于互联网的访问。面向服务的体系结构在向最终用户提供所需服务方面起着关键作用。Simdemics与综合网络环境一起，已被用于十多个用户定义的案例研究。进行这些个案研究是为了支持在规划应对流行病（例如H1N1、H5 N1）和人类发起的生物恐怖主义事件方面出现的具体政策问题。这些研究对网络环境的不断发展和改善起了至关重要的作用。
We discuss an interaction-based approach to study the coevolution between sociotechnical networks, individual behaviors, and contagion processes on these networks. We use epidemics in human populations as an example of this phenomenon. The methods consist of developing synthetic yet realistic national-scale networks using a first-principles approach. Unlike simple random graph techniques, these methods combine real-world data sources with behavioral and social theories to synthesize detailed social contact (proximity) networks. Individual-based models of within-host disease progression and interhost transmission are then used to model the contagion process. Finally, models of individual behaviors are composed with disease progression models to develop a realistic representation of the complex system in which individual behaviors and the social network adapt to the contagion. These methods are embodied within Simdemics, a general-purpose modeling environment to support pandemic planning and response. Simdemics is designed specifically to be scalable to networks with 300 million agents; the underlying algorithms and methods in Simdemics are all high-performance computing-oriented methods. New advances in network science, machine learning, high-performance computing, data mining, and behavioral modeling were necessary to develop Simdemics.Simdemics is combined with two other environments, Simfrastructure and Didactic, to form an integrated cyber environment. The integrated cyber environment provides the end user with flexible and seamless Internet-based access to Simdemics. Service-oriented architectures play a critical role in delivering the desired services to the end user. Simdemics, in conjunction with the integrated cyber environment, has been used in more than a dozen user-defined case studies. These case studies were done to support specific policy questions that arose in the context of planning the response to pandemics (for example, H1N1, H5N1) and human-initiated bioterrorism events. These studies played a crucial role in the continual development and improvement of the cyber environment.