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            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>Coronavirus</text>
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                <text>Dominio científico: Coronavirus</text>
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              <text>Outbreak prediction of covid-19 in most susceptible countries</text>
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              <text>H. Maheshwari, D. Yadav, U. Chandra</text>
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              <text>Origin of the coronavirus was the seafood market of Wuhan city, Hubei province in China. The cases of someone suffering from COVID-19 can be traced back to the end of December 2019 in China. This is the most infectious disease and spread worldwide within three months after the first case reported. The World Health Organization renames Coronavirus as COVID-19. COVID-19 is the β-Coronavirus family virus, effect on the lung of human and common symptoms are cough, fever, fatigue, respiratory problem, and cold. The full name of the coronavirus is severe acute respiratory syndrome SARS-CoV. It spread on humans as well as animals and infected more than 183 countries with 2959927 confirm cases and 202733 deaths till 28 April 2020. 84 days data is used to predict confirmed and death cases for the next 10 days by using prophet and daily average based algorithm. Predicted confirmed cases are 2886183 and death cases 190540 till 25 April 2020. This study introduces the spreading pattern of COVID-19 in the top ten infected countries.  After China, European countries are the most infected ones. In this study, data was analyzed on the attributes confirmed, active, recovered and death cases, and next ten days outbreak prediction. Some countries state-wise data confirmed active and death cases also analyzed.</text>
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              <text>2020</text>
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              <text>machine learning, SARS-CoV, covid-19 coronavirus, outbreak prediction, prophet time series</text>
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              <text>DOI: 10.22034/GJESM.2019.06.SI.02</text>
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              <text>Global Journal of Environmental Science and Management</text>
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          <name>Publisher</name>
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              <text>Global Journal of Environmental Science and Management</text>
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          <name>Coverage</name>
          <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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              <text>Environmental sciences</text>
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