<?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>1409-2433</journal-id>
<journal-title><![CDATA[Revista de Matemática Teoría y Aplicaciones]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Mat]]></abbrev-journal-title>
<issn>1409-2433</issn>
<publisher>
<publisher-name><![CDATA[Centro de Investigaciones en Matemática Pura y Aplicada (CIMPA) y Escuela de Matemática, San José, Costa Rica.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1409-24332023000100089</article-id>
<article-id pub-id-type="doi">10.15517/rmta.v30i1.50566</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Mathematical models and simulations of different scenarios of COVID-19 in Brazil]]></article-title>
<article-title xml:lang="es"><![CDATA[Modelos matemáticos y simulaciones de diferentes escenarios de COVID-19 en Brasil]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lima]]></surname>
<given-names><![CDATA[Marina]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Silva]]></surname>
<given-names><![CDATA[Anny]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Meyer]]></surname>
<given-names><![CDATA[João Frederico]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,IMECC- UNICAMP  Department of Applied Mathematics]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Brazil</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Federal Institute of Parana, IFPR  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Brazil</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,IMECC- UNICAMP  Department of Applied Mathematics]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Brazil</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2023</year>
</pub-date>
<volume>30</volume>
<numero>1</numero>
<fpage>89</fpage>
<lpage>111</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S1409-24332023000100089&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S1409-24332023000100089&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S1409-24332023000100089&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract In this paper, we develop successive mathematical models for describing the first and second waves of COVID-19 in Brazil, including the original strain, the two variants with the highest circulation (Gamma and Delta variants) and population vaccination. We started with a a simple initial model and using mathematical modelling techniques and the Heaviside step function, we improved it according to the necessities of describing the disease&#8217;s behaviour, both considering the new variants or vaccination. In all cases, we performed computer simulations and compared the obtained curve with real data from the active cases, which reinforced the model&#8217;s efficiency in describing the behaviour of the pandemic and highlighted the importance of population vaccination in describing the dynamics of the infections and reducing cases.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen En este artículo, desarrollamos modelos matemáticos sucesivos para describir la primera y la segunda ola de COVID-19 en Brasil, incluida la cepa original, las dos variantes de mayor circulación (variantes Gamma y Delta) y la vacunación de la población. Partimos de un modelo inicial simple y utilizando técnicas de modelado matemático y la función escalón de Heaviside, lo mejoramos según las necesidades de describir el comportamiento de la enfermedad, tanto considerando las nuevas variantes como la vacunación. En todos los casos, realizamos simulaciones informáticas y comparamos la curva obtenida con datos reales de los casos activos, lo que reforzó la eficiencia del modelo para describir el comportamiento de la pandemia y destacó la importancia de la vacunación de la población para describir la dinámica de los contagios y reducir los casos.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[mathematical modelling]]></kwd>
<kwd lng="en"><![CDATA[SARS-CoV-2 variants]]></kwd>
<kwd lng="en"><![CDATA[mathematical epidemiology]]></kwd>
<kwd lng="en"><![CDATA[nonlinear systems of ODEs.]]></kwd>
<kwd lng="es"><![CDATA[COVID-19]]></kwd>
<kwd lng="es"><![CDATA[modelación matemática]]></kwd>
<kwd lng="es"><![CDATA[variantes de SARS-CoV-2]]></kwd>
<kwd lng="es"><![CDATA[epidemiologia matemática]]></kwd>
<kwd lng="es"><![CDATA[sistemas no lineales de EDO.]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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