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为什么使用模糊认知地图 (FCM) 建模COVID-19大流行?

Peter P Groumpos¹

¹Emeritus Professor Department of Electrical and Computer Engineering, University of Patras,26500 Greece.

IFAC-PapersOnLine

|April 15, 2024

概括

这项研究引入了模糊的认知地图 (FCM) 来建模COVID-19因果关系,超越相关性. 使用真实患者数据进行的FCM模拟为了解大流行病提供了出色的结果.

科学领域:

计算流行病学计算流行病学
复杂系统的建模复杂的系统建模.
公共卫生信息学公共卫生信息学

背景情况:

"COVID-19"疫情造成了全球卫生紧急情况和广泛的社会影响.
现有的COVID-19研究主要依赖于以关联为重点的统计模型,经常忽视因果关系.
需要采用结合因果关系的方法来全面了解大流行病的动态.

研究的目的:

建议和评估用于模拟COVID-19的模糊认知地图 (FCM) 的应用.
通过使用FCM方法调查影响COVID-19流行病的因果因素.
开发一个代表10个关键COVID-19症状的FCM模型.

主要方法:

开发一个模糊的认知地图 (FCM) 模型,包括与COVID-19相关的10个症状概念.
使用拟议的FCM COVID-19模型进行理论模拟研究.
通过模拟与当地医院的真实患者数据验证FCM模型.

主要成果:

该FCM COVID-19模型有效模拟了流行病的动态.
使用真实患者数据的模拟表明与观察到的结果有很好的一致性.
FCM方法提供了对COVID-19症状之间的因果关系的见解.

结论:

模糊的认知地图为分析COVID-19流行病等复杂现象的因果关系提供了有价值的框架.
开发的FCM模型对了解疾病进展和为公共卫生战略提供信息非常有希望.
概述了进一步的研究方向,以改进流行病学研究中的FCM方法.

关键词:

在 COVID-19 疫情中,有关因果关系的因果关系相关性相关性相关性模糊的认知地图.模拟建模的模型.流行病是一种流行病.模拟模拟是指一个模拟模拟.

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