摘要
背景:阿尔茨海默病是最常见的精神疾病之一。据推测,超过25%的人口在一生中受到某种精神疾病的影响。EA治疗中国病人从有关经济中汲取资源。因此,必须量化潜在的经济影响。 方法:提出一种基于Agent、系统动力学和数值模拟方法对欧盟及其老年痴呆症患者进行动态建模的方法。司木比较了不同建模工具的分类、特征和结果。 结果:这些方法的结果与欧盟统计局的欧盟人口增长预测结果进行了比较。描述了建立模型的方法。并对三种建模方法进行了比较。讨论了每种建模方法对人口模型的适用性。 结论:在本案例研究中,这三种方法都给出了与欧盟人口预测相对应的结果。此外,我们还能够预测AD患者的数量,并根据在建模方法上,我们还能够监测不同种群的特征。
关键词: 阿尔茨海默病,人口模型,系统动力学,基于Agent的模型,数值模型,人口预测。
Current Alzheimer Research
Title:Numeric, Agent-based or System Dynamics Model? Which Modeling Approach is the Best for Vast Population Simulation?
Volume: 15 Issue: 8
关键词: 阿尔茨海默病,人口模型,系统动力学,基于Agent的模型,数值模型,人口预测。
摘要: Background: Alzheimer’s disease is one of the most common mental illnesses. It is posited that more than 25% of the population is affected by some mental disease during their lifetime. Treatment of each patient draws resources from the economy concerned. Therefore, it is important to quantify the potential economic impact.
Methods: Agent-based, system dynamics and numerical approaches to dynamic modeling of the population of the European Union and its patients with Alzheimer’s disease are presented in this article. Simulations, their characteristics, and the results from different modeling tools are compared.
Results: The results of these approaches are compared with EU population growth predictions from the statistical office of the EU by Eurostat. The methodology of a creation of the models is described and all three modeling approaches are compared. The suitability of each modeling approach for the population modeling is discussed.
Conclusion: In this case study, all three approaches gave us the results corresponding with the EU population prediction. Moreover, we were able to predict the number of patients with AD and, based on the modeling method, we were also able to monitor different characteristics of the population.
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Cite this article as:
Numeric, Agent-based or System Dynamics Model? Which Modeling Approach is the Best for Vast Population Simulation?, Current Alzheimer Research 2018; 15 (8) . https://dx.doi.org/10.2174/1567205015666180202094551
DOI https://dx.doi.org/10.2174/1567205015666180202094551 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
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