Department of Governance and Technology for Sustainability (CSTM), Faculty of Behavioral, Management, and Social Sciences (BMS), University of Twente, Enschede, The Netherlands.
Department of Computer Science, College of Science, University of Duhok (UoD), Duhok, Kurdistan Region, Iraq.
Int J Health Geogr. 2018 Mar 20;17(1):8. doi: 10.1186/s12942-018-0128-x.
Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among agents and their environment by means of agent-based models. Yet, current disease models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend agent-based disease models by capturing behavioral aspects of decision-making in a risky context using machine learning techniques. We illustrate it with a case of cholera in Kumasi, Ghana, accounting for spatial and social risk factors that affect intelligent behavior and corresponding disease incidents. The results of computational experiments comparing intelligent with zero-intelligent representations of agents in a spatial disease agent-based model are discussed.
We present a spatial disease agent-based model (ABM) with agents' behavior grounded in Protection Motivation Theory. Spatial and temporal patterns of disease diffusion among zero-intelligent agents are compared to those produced by a population of intelligent agents. Two Bayesian Networks (BNs) designed and coded using R and are further integrated with the NetLogo-based Cholera ABM. The first is a one-tier BN1 (only risk perception), the second is a two-tier BN2 (risk and coping behavior).
We run three experiments (zero-intelligent agents, BN1 intelligence and BN2 intelligence) and report the results per experiment in terms of several macro metrics of interest: an epidemic curve, a risk perception curve, and a distribution of different types of coping strategies over time.
Our results emphasize the importance of integrating behavioral aspects of decision making under risk into spatial disease ABMs using machine learning algorithms. This is especially relevant when studying cumulative impacts of behavioral changes and possible intervention strategies.
全球数百万人经常接触致命的传染病。例如，寨卡病毒爆发的突发新闻登上了国际主流媒体的头条。对疾病风险的感知促使人们改变行为，以更安全、更具保护性的生活方式。通过基于主体的模型，计算科学在探索由许多个人决策以及主体与其环境之间的相互作用所产生的疾病传播模式方面发挥了重要作用。然而，当前的疾病模型很少考虑模拟风险感知及其对适应性保护行为的影响。社会科学提供了对个体风险感知及其相应保护行为的深入了解，而机器学习则提供了捕获这些学习过程的算法和方法。本文提出了一种创新方法，通过使用机器学习技术捕获风险决策中的行为方面，从而扩展基于主体的疾病模型。我们使用加纳库马西的霍乱案例来说明这一点，该案例考虑了影响智能行为和相应疾病事件的空间和社会风险因素。讨论了在空间疾病基于主体的模型中比较智能代理和零智能代理的计算实验结果。
我们提出了一个基于主体的空间疾病模型（ABM），其中代理的行为基于保护动机理论。比较了零智能代理之间的疾病扩散的时空模式与智能代理产生的时空模式。使用 R 设计和编写了两个贝叶斯网络（BN），并进一步与基于 NetLogo 的霍乱 ABM 集成。第一个是单层 BN1（仅风险感知），第二个是双层 BN2（风险和应对行为）。
我们运行了三个实验（零智能代理、BN1 智能代理和 BN2 智能代理），并根据几个感兴趣的宏观指标报告每个实验的结果：流行曲线、风险感知曲线以及不同类型的应对策略随时间的分布。
我们的结果强调了使用机器学习算法将风险决策的行为方面纳入空间疾病 ABM 的重要性。当研究行为变化的累积影响和可能的干预策略时，这一点尤其重要。
