Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation.
Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation.
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DOI：
10.1109/jbhi.2021.3052134
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发表时间：
2021-03
影响因子：
7.7
通讯作者：
Serra GLO
中科院分区：
文献类型：
--
作者：
Gomes DCDS;Serra GLO
提出了一种用于流行病学数据分析的智能机器学习计算模型。所采用的方法的创新包括：基于自适应相似距离机制的区间2型模糊聚类算法，用于定义与流行病学数据的行为和不确定性相关的特定操作区域；以及区间2型模糊版本的观察者/卡尔曼滤波识别(OKID)算法，用于根据实验流行病学数据的递归频谱分解计算的不可观测分量进行自适应跟踪和实时预测。实验结果和对比分析表明，所提出的方法对于自适应跟踪和实时预测巴西新型冠状病毒2019(新冠肺炎)爆发的动态传播行为是有效和适用的。
A computational model with intelligent machine learning for analysis of epidemiological data, is proposed. The innovations of adopted methodology consist of an interval type-2 fuzzy clustering algorithm based on adaptive similarity distance mechanism for defining specific operation regions associated to the behavior and uncertainty inherited to epidemiological data, and an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm for adaptive tracking and real time forecasting according to unobservable components computed by recursive spectral decomposition of experimental epidemiological data. Experimental results and comparative analysis illustrate the efficiency and applicability of proposed methodology for adaptive tracking and real time forecasting the dynamic propagation behavior of novel coronavirus 2019 (COVID-19) outbreak in Brazil.