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      Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps

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          Abstract

          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.

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          Most cited references16

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          World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19)

          An unprecedented outbreak of pneumonia of unknown aetiology in Wuhan City, Hubei province in China emerged in December 2019. A novel coronavirus was identified as the causative agent and was subsequently termed COVID-19 by the World Health Organization (WHO). Considered a relative of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), COVID-19 is caused by a betacoronavirus named SARS-CoV-2 that affects the lower respiratory tract and manifests as pneumonia in humans. Despite rigorous global containment and quarantine efforts, the incidence of COVID-19 continues to rise, with 90,870 laboratory-confirmed cases and over 3,000 deaths worldwide. In response to this global outbreak, we summarise the current state of knowledge surrounding COVID-19.
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            COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses

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              Analysis and forecast of COVID-19 spreading in China, Italy and France

              Highlights • Epidemic spreading • COVID19 • SIR model • Recursive relations and non linear fitting
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                Author and article information

                Contributors
                Journal
                Chaos Solitons Fractals
                Chaos Solitons Fractals
                Chaos, Solitons, and Fractals
                Elsevier Ltd.
                0960-0779
                0960-0779
                21 May 2020
                21 May 2020
                Affiliations
                [0001]Tijuana Institute of Technology, Tijuana, Mexico
                Author notes
                [* ]Corresponding author pmelin@ 123456tectijuana.mx
                Article
                S0960-0779(20)30317-9 109917
                10.1016/j.chaos.2020.109917
                7241408
                d4d65e52-eb9d-453d-b334-436ba44c78bb
                © 2020 Elsevier Ltd. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                Categories
                Article

                coronavirus,spatial similarity,self-organizing maps,neural networks

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