Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps
Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps
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DOI：
10.1016/j.chaos.2020.109917
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发表时间：
2020-09-01
影响因子：
7.8
通讯作者：
Castillo, Oscar
中科院分区：
文献类型：
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作者：
Melin, Patricia;Cesar Monica, Julio;Castillo, Oscar
我们在本文中描述了通过使用特定类型的无监督神经网络的冠状病毒大流行的空间演化的分析，该网络称为自组织图。基于自组织地图的聚类能力，我们能够将根据冠状病毒病例相似的空间分组，这样，这样就可以分析哪些国家的行为相似，因此可以通过使用类似的策略来处理哪些国家，从而受益病毒的传播。分析已使用了过去几个月以来全球冠状病毒病例的公开数据集。已经得出了有趣的结论，这可能有助于决定处理这种病毒的最佳策略。以前关于冠状病毒数据的之前的大多数论文都查看了时间方面的问题，这也很重要，但这主要与数字信息的预测有关。但是，我们认为空间方面也很重要，因此，在这种观点中，本文的主要贡献是使用无监督的自组织地图将类似国家组合在一起，以与冠状病毒大流行作斗争，从而提出了这些策略可以建立类似的国家。 （c）2020 Elsevier Ltd.保留所有权利。
We describe in this paper an analysis of the spatial evolution of coronavirus pandemic around the world by using a particular type of unsupervised neural network, which is called self-organizing maps. Based on the clustering abilities of self-organizing maps we are able to spatially group together countries that are similar according to their coronavirus cases, in this way being able to analyze which countries are behaving similarly and thus can benefit by using similar strategies in dealing with the spread of the virus. Publicly available datasets of coronavirus cases around the globe from the last months have been used in the analysis. Interesting conclusions have been obtained, that could be helpful in deciding the best strategies in dealing with this virus. Most of the previous papers dealing with data of the Coronavirus have viewed the problem on temporal aspect, which is also important, but this is mainly concerned with the forecast of the numeric information. However, we believe that the spatial aspect is also important, so in this view the main contribution of this paper is the use of unsupervised self-organizing maps for grouping together similar countries in their fight against the Coronavirus pandemic, and thus proposing that strategies for similar countries could be established accordingly. (C) 2020 Elsevier Ltd. All rights reserved.