<?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>1561-3003</journal-id>
<journal-title><![CDATA[Revista Cubana de Higiene y Epidemiología]]></journal-title>
<abbrev-journal-title><![CDATA[Rev Cubana Hig Epidemiol]]></abbrev-journal-title>
<issn>1561-3003</issn>
<publisher>
<publisher-name><![CDATA[Centro Nacional de Información de Ciencias MédicasEditorial Ciencias Médicas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1561-30032020000100005</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelos estadísticos para las predicciones de la COVID-19 en Cuba]]></article-title>
<article-title xml:lang="en"><![CDATA[Statistical models to predictions of the COVID-19 in Cuba]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Prades Escobar]]></surname>
<given-names><![CDATA[Eliecer]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Marín Sánchez]]></surname>
<given-names><![CDATA[Dayana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Centro de Toxicología y Biomedicina  ]]></institution>
<addr-line><![CDATA[ Santiago de Cuba]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2020</year>
</pub-date>
<volume>57</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1561-30032020000100005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1561-30032020000100005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1561-30032020000100005&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción:  Los estudios basados en modelos estadísticos juegan un papel importante para las predicciones sobre la COVID-19.  Objetivo:  Realizar un análisis de modelación estadística combinando 6 modelos de pronósticos para predecir la aparición de casos positivos diarios, activos y fallecidos por COVID-19 en Cuba.  Métodos: Se utilizaron los datos reportados diariamente del 11 de marzo al 25 de mayo publicados en el sitio web CUBADEBATE. A los modelos propuestos se les calculó el desempeño mediante los estadísticos: MAE, RMSE, MAPE y ME así como el análisis de residuales.  Resultados: Los modelos A y B dan una tendencia constante de 8 y 9 casos positivos respectivamente para el día 22 de julio. El modelo C indica una ligera disminución de los casos con 4 ese mismo día y el modelo D una tendencia al aumento con 19 casos. . El modelo E refleja un mínimo de 126 casos el día 3 de junio y luego un aumento de los casos hasta alcanzar el 22 de julio un valor de 374 casos activos hospitalizados. En el modelo F se apreció una tendencia a mantenerse constante el número de fallecidos por encima de 80 casos en la primera quincena de julio.  Conclusiones:  Los 6 modelos estudiados cumplen con las pruebas estadísticas, de desempeño y residuales. Sus datos proporcionan un pronóstico para la COVID-2019, representando una herramienta válida.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Introduction:  Studies based on statistical models play an important role for predictions about COVID-19.  Objective:  To carry out a statistical modeling analysis combining 6 forecast models to predict the appearance of daily positive cases, active and deceased by COVID-19 in Cuba.  Method:  Data reported daily from March 11 to May 25 from the CUBADEBATE website were used, which were processed and analyzed. The performance of the models was calculated: Mean absolute error (MAE), root of the mean square error (RMSE), percent of mean absolute error (MAPE) and the mean error (ME) as well as the residual analysis.  Results: Models A and B gave a constant trend between 8 and 9 cases of until July 22. Model C indicated a decrease in cases with 4 that same day and model D indicated a raise to 19 cases. Model E indicated a minimum of 126 cases on June E and then a raise to 374 hospitalized cases. Deceases cases had a constant tendency in deceases numbers above of 80 cases in first 15 days of July.  Conclusions:  The 6 models studied meet the statistical , performance and residual tests. Their data provides a forecast for COVID2019, representing a valid tool.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[COVID-2019]]></kwd>
<kwd lng="es"><![CDATA[RMSE]]></kwd>
<kwd lng="es"><![CDATA[residuales]]></kwd>
<kwd lng="es"><![CDATA[MAPE]]></kwd>
<kwd lng="en"><![CDATA[COVID-2019]]></kwd>
<kwd lng="en"><![CDATA[prediction]]></kwd>
<kwd lng="en"><![CDATA[RMSE]]></kwd>
<kwd lng="en"><![CDATA[residuals]]></kwd>
<kwd lng="en"><![CDATA[MAPE]]></kwd>
</kwd-group>
</article-meta>
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