<?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>2306-9155</journal-id>
<journal-title><![CDATA[Retos de la Dirección]]></journal-title>
<abbrev-journal-title><![CDATA[Rev retos]]></abbrev-journal-title>
<issn>2306-9155</issn>
<publisher>
<publisher-name><![CDATA[Centro de Estudios de Dirección Empresarial Territorial de la Universidad de Camagüey Ignacio Agramonte Loynaz]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2306-91552020000200054</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelación físico-matemática para la toma de decisiones frente a la COVID-19 en Cuba]]></article-title>
<article-title xml:lang="en"><![CDATA[Physical-mathematical Modeling for Decision-Making against COVID-19 in Cuba]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sánchez Vargas]]></surname>
<given-names><![CDATA[Héctor Eduardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramos Sánchez]]></surname>
<given-names><![CDATA[Luis Beltrán]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Galindo Llanes]]></surname>
<given-names><![CDATA[Pablo Ángel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Salgado Rodríguez]]></surname>
<given-names><![CDATA[Amyrsa]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Camagüey Ignacio Agramonte Loynaz Facultad de Ciencias Aplicadas Departamento de Ingeniería Química]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Camagüey Ignacio Agramonte Loynaz Facultad de Ciencias Económicas Centro de Estudios de Dirección Empresarial y Territorial]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<volume>14</volume>
<numero>2</numero>
<fpage>54</fpage>
<lpage>85</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2306-91552020000200054&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2306-91552020000200054&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2306-91552020000200054&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Objetivo:  Aplicar la modelación físico-matemática a la dinámica de la COVID-19 para la toma de decisiones asociadas a la mitigación y erradicación de la pandemia en Cuba.  Métodos:  La modelación se aplicó para caracterizar el pronóstico del pico y el comportamiento reproductivo de la pandemia, mediante herramientas y funciones de MATLAB. El pico se determinó aplicando el modelo SIR, luego de adecuaciones. Este se ajustó con la estrategia de optimización GlobalSearch. Para su solución se empleó la función ode23tb que usa un algoritmo combinado de Runge-Kutta con otro de regla trapezoidal. Para el comportamiento reproductivo se realizó el ajuste de un modelo exponencial empleando la herramienta Curve Fitting.  Principales resultados:  Se identificaron los parámetros del modelo SIR y se logró el pronóstico del pico con dos semanas de anticipación y una precisión satisfactoria. Se pronosticaron los susceptibles, infectados acumulados y recuperados. El número de reproducción básico (R0) calculado permitió determinar que, para erradicar la pandemia por vacunación, la población inmunizada debe ser superior al 72 %. El número de reproducción efectivo (Ref) permitió evaluar la eficacia de las medidas de mitigación. Se reflexionó sobre la conducta a seguir para su erradicación.  Conclusiones:  El modelo SIR demostró capacidad para predecir el pico de la pandemia. El R0 del SARS-CoV-2 permitió corroborar su elevada transmisibilidad. Las medidas de mitigación han sido efectivas y deben mantenerse hasta erradicar la pandemia, incluso para Ref &lt; 1, mientras no se inmunice el 72 % de la población, para lograr la erradicación irreversible.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Objective:  To apply physical-mathematical modeling to the dynamics of COVID-19 to make decisions associated with mitigation and eradication of the pandemics in Cuba.  Methods:  Modeling was applied in order to characterize the forecast peak timing, and the reproductive performance of the pandemic, through MATLAB tools and functions. Peak timing was determined with the application of the SIR model, after adjustments. It was fit using the GlobalSearch optimization strategy. Function ode23tb was used in the solution, including a Runge-Kutta algorithm combined with another trapezoidal rule algorithm. To determine reproductive performance, an exponential model was fit using Curve Fitting.  Main results:  The parameters of the SIR model were identified, and the peak forecast was completed timely and accurately two weeks in advance. The susceptible, accumulated infected and recovered patients were predicted. The calculated basic reproduction number (R0) helped conclude that to eradicate the pandemic by vaccination, the immunized population should be over 72 %. The effective reproduction number (Ref ) helped evaluate the efficacy of mitigation measures. Remarks are made concerning the proper conduct to follow for eradication.  Conclusions:  The SIR model proved its capacity to predict the peak timing of the pandemic. The R0 of SARS-CoV-2 corroborated the high transmissibility of the virus. The mitigation measures have been effective, and should be kept until the pandemic is eradicated, even with Ref &lt; 1, when 72 % of the population is immunized.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[COVID-19]]></kwd>
<kwd lng="es"><![CDATA[SARS-CoV-2]]></kwd>
<kwd lng="es"><![CDATA[toma de decisiones]]></kwd>
<kwd lng="es"><![CDATA[modelación matemática]]></kwd>
<kwd lng="es"><![CDATA[número de reproducción]]></kwd>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[SARS-CoV-2]]></kwd>
<kwd lng="en"><![CDATA[decision-making]]></kwd>
<kwd lng="en"><![CDATA[mathematical modeling]]></kwd>
<kwd lng="en"><![CDATA[reproduction number]]></kwd>
</kwd-group>
</article-meta>
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