<?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>2304-0106</journal-id>
<journal-title><![CDATA[Anales de la Academia de Ciencias de Cuba]]></journal-title>
<abbrev-journal-title><![CDATA[Anales de la ACC]]></abbrev-journal-title>
<issn>2304-0106</issn>
<publisher>
<publisher-name><![CDATA[Academia de Ciencias de Cuba]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2304-01062021000100009</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelo inspirado en un proceso hidrológico para el pronóstico de escenarios de contagio de la COVID-19]]></article-title>
<article-title xml:lang="en"><![CDATA[Model inspired by a hydrological process for the forecast of spread scenarios of COVID-19]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez González]]></surname>
<given-names><![CDATA[Yoel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García Cortés]]></surname>
<given-names><![CDATA[Daniel Ayax]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de La Habana Instituto Superior de Tecnologías y Ciencias Aplicadas, InSTEC Departamento de Medio Ambiente]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2021</year>
</pub-date>
<volume>11</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2304-01062021000100009&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2304-01062021000100009&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2304-01062021000100009&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción:  Los modelos matemáticos son abstracciones de la realidad que permiten describir características importantes de los fenómenos en estudio. Entre estos los modelos epidemiológicos son considerados una de las herramientas más poderosas para analizar y comprender la propagación y el control de epidemias. Objetivos: desarrollar un modelo para el pronóstico de escenarios de contagio de la COVID-19.  Métodos:  Se combinaron el modelo propuesto de población inspirado en el tránsito hidrológico en ríos, un modelo autorregresivo de medias móviles (ARIMA) y un modelo de transición para evaluar tres escenarios posibles de contagio y su evolución temporal durante el desarrollo de la pandemia de la COVID-19.  Resultados:  Fueron estudiados 27 países con reporte de contagio hasta el 16 de abril de 2020 y fueron agrupados por cuartiles según un parámetro que relaciona personas recuperadas y fallecidas. Esta información permitió establecer criterios sobre los parámetros involucrados en el modelo propuesto para el caso de Cuba. Los escenarios de contagio de la epidemia favorable, poco favorable y crítico fueron generados a partir de modelos ARIMA hasta el 29 de abril de 2020 y de transición, extendido a 120 días. Los resultados de la implementación del modelo fueron comparados con los datos reales de la evolución de la pandemia en Cuba hasta el 22 de mayo, obteniéndose una correspondencia satisfactoria entre los registros reales y los escenarios de casos activos. Este modelo podría utilizarse de la misma forma para otros países de América que tienen también un retardo con respecto a los países seleccionados para este estudio.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Introduction:  The mathematical models are abstractions of the reality that allow for the description of important characteristics of the phenomena studied. Among these, the epidemic models are considered one of the most powerful tools to analyze and understand epidemic propagation and control. Objective: A model for the forecast of spread scenarios of the COVID-19 has been developed.  Methods:  A proposed population model inspired by analogy by hydrologic routing in rivers, an autoregressive integrated moving average model (ARIMA) and a transition model were combined in order to evaluate three possible spread scenarios and their temporary evolution during the development of COVID-19 pandemic.  Results:  In this contribution, 27 countries having coronavirus daily reports until April 16th, 2020 were studied. They were grouped by quartiles according to rates of recovered and deceased people. This information permitted the establishment of criteria for the parameters involved in the proposed model for Cuba as a case study. The favorable, not very favorable and critical epidemic spread scenarios were generated from ARIMA up to April 29th, 2020 and the transition model extended to 120 days. The results of the model implementation were compared with the real data of the pandemic evolution in Cuba up to May 22nd, 2020, resulting in a satisfactory correspondence between the real daily reports and the scenarios of active cases. This model could be used in the same way for other American countries that also have a delay with regard to the countries selected for this study.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[COVID-19]]></kwd>
<kwd lng="es"><![CDATA[modelos]]></kwd>
<kwd lng="es"><![CDATA[pronóstico]]></kwd>
<kwd lng="es"><![CDATA[escenarios de contagio]]></kwd>
<kwd lng="en"><![CDATA[COVID-19, models]]></kwd>
<kwd lng="en"><![CDATA[forecast]]></kwd>
<kwd lng="en"><![CDATA[spread scenarios]]></kwd>
</kwd-group>
</article-meta>
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