<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1870-6622</journal-id>
<journal-title><![CDATA[EconoQuantum]]></journal-title>
<abbrev-journal-title><![CDATA[EconoQuantum]]></abbrev-journal-title>
<issn>1870-6622</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Guadalajara]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1870-66222021000100021</article-id>
<article-id pub-id-type="doi">10.18381/eq.v18i1.7222</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[COVID-19 and Economics Forecasting on Advanced and Emerging Countries]]></article-title>
<article-title xml:lang="es"><![CDATA[COVID-19 y pronósticos sobre crecimiento económico para economías avanzadas y emergentes]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramírez García]]></surname>
<given-names><![CDATA[Abraham]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Jiménez Preciado]]></surname>
<given-names><![CDATA[Ana Lorena]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Politécnico Nacional Escuela Superior de Economía ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Politécnico Nacional Escuela Superior de Economía ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<volume>18</volume>
<numero>1</numero>
<fpage>21</fpage>
<lpage>43</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1870-66222021000100021&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1870-66222021000100021&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1870-66222021000100021&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract:  Objective:  To estimate the size and the dynamics of the coronavirus (COVID-19) pandemic in Advanced, Emerging, and Developing Economies, and to determine its implications for economic growth.  Methodology:  A susceptible Infected Recovered (SIR) model is implemented, we calculate the size of the pandemic through numerical integration and phase diagrams for COVID-19 trajectory; finally, we use ensemble models (random forest) to forecast economic growth.  Results:  We confirm that there are differences in pandemic spread and size among countries; likewise, the trajectories show a long-term spiral cycle. Economic recovery is expected to be slow and gradual for most of the economies.  Limitations:  All countries differ in COVID-19 test applications, which could lead to inaccurate total confirmed cases and an imprecise estimate of the pandemic&#8217;s spread and size. In addition, there is a lack of leading indicators in some countries, generating a higher MSE of some machine learning models.  Originality:  To implement economic-epidemiological models to analyze the evolution and virus&#8217; spreading throughout time.  Conclusions:  It is found the pandemic&#8217;s final size to be between 74-77%. Likewise, it is demonstrated that COVID-19 is endemic, with a constant prevalence of 9 years on average. The spread of the pandemic has caused countries to self-induce in an unprecedented recession with a slow recovery.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen:  Objetivo:  Estimar el tamaño y la dinámica de la pandemia del coronavirus (COVID-19) de economías avanzadas y economías emergentes y en desarrollo, así como sus implicaciones en el crecimiento económico.  Metodología:  Se implementa el modelo Susceptible Infectado Recuperado (SIR), se calcula el tamaño de la pandemia mediante integración numérica y se utilizan diagramas de fase para conocer la trayectoria del COVID-19; finalmente, se hacen pronósticos de crecimiento con modelos de ensamble (bosques aleatorios).  Resultados:  Se confirman las diferencias de tamaño y contagio entre los países; asimismo, las trayectorias exhiben ciclos en forma de espiral. Se espera que la recuperación económica sea lenta pero gradual en las naciones.  Limitaciones:  Todos los países difieren en número de pruebas aplicadas para detectar el COVID-19, esto puede llevar a un número impreciso de casos totales y una estimación imprecisa de la propagación y el tamaño de la pandemia. Además, hay una falta de indicadores adelantados en algunos países, lo que genera un MSE más alto de algunos de los modelos de machine learning.  Originalidad:  Se hace uso de modelos económicos-epidemiológicos para analizar la evolución y expansión del virus a través del tiempo.  Conclusiones:  Se encontró que el tamaño final de la pandemia se encuentra entre el 74% y el 77%. Asimismo, se demuestra que el COVID-19 es endémico, con una prevalencia constante de 9 años en promedio.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[phase diagrams]]></kwd>
<kwd lng="en"><![CDATA[SIR model]]></kwd>
<kwd lng="en"><![CDATA[ensemble models]]></kwd>
<kwd lng="en"><![CDATA[forecasting]]></kwd>
<kwd lng="es"><![CDATA[COVID-19]]></kwd>
<kwd lng="es"><![CDATA[diagramas de fase]]></kwd>
<kwd lng="es"><![CDATA[modelo SIR]]></kwd>
<kwd lng="es"><![CDATA[modelos de ensamble]]></kwd>
<kwd lng="es"><![CDATA[pronóstico]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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