<?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>0123-7799</journal-id>
<journal-title><![CDATA[TecnoLógicas]]></journal-title>
<abbrev-journal-title><![CDATA[TecnoL.]]></abbrev-journal-title>
<issn>0123-7799</issn>
<publisher>
<publisher-name><![CDATA[Instituto Tecnológico Metropolitano - ITM]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0123-77992020000300179</article-id>
<article-id pub-id-type="doi">10.22430/22565337.1685</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Herramienta basada en agentes para la valoración del impacto de intervenciones no farmacéuticas contra la COVID-19]]></article-title>
<article-title xml:lang="en"><![CDATA[An agent-based tool for impact assessment of non-pharmaceutical interventions against COVID-19]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pomar]]></surname>
<given-names><![CDATA[Lindsay Álvarez]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Galeano]]></surname>
<given-names><![CDATA[Sergio Rojas]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Distrital Francisco José de Caldas  ]]></institution>
<addr-line><![CDATA[Bogotá Colombia]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Distrital Francisco José de Caldas  ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</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>23</volume>
<numero>49</numero>
<fpage>179</fpage>
<lpage>200</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0123-77992020000300179&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0123-77992020000300179&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0123-77992020000300179&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Las intervenciones no farmacéuticas (NPI) son actualmente el único mecanismo que los gobiernos pueden usar para mitigar el impacto de la epidemia de COVID-19. De manera similar a la propagación real de la enfermedad, la dinámica de los patrones de contención que surgen de la aplicación de los NPI es compleja y depende de las interacciones entre las personas dentro de una región específica, así como de otros factores estocásticos asociados a condiciones demográficas, geográficas, políticas y económicas. Los modelos basados en agentes simulan reglas microscópicas de interacciones simultáneas de múltiples individuos dentro de una población en un intento de reproducir la dinámica compleja del efecto de las medidas de contención. De esta manera, es posible diseñar comportamientos individuales junto con escenarios de NPI, midiendo cómo se ve afectada la dinámica de la simulación y, por lo tanto, brindando información útil para realizar una evaluación rápida del potencial de las intervenciones combinadas, en las diferentes etapas de la epidemia. En este artículo describimos un modelo y una herramienta para experimentar con este tipo de análisis, considerando una serie de NPI ampliamente utilizadas, tales como distanciamiento físico, aislamiento de casos, cuarentena domiciliaria, encierro total, pruebas centinela, uso de tapabocas y una novedosa aplicación &#8220;zonal&#8221;, que permite aplicar estas intervenciones gradualmente a localidades o zonas separadas. La elección de las intervenciones o combinaciones más adecuadas para un territorio particular, dependerá en última instancia de las condiciones socioeconómicas y de salubridad, así como de una validación a gran escala de la viabilidad de los escenarios identificados preliminarmente mediante la herramienta.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Non-Pharmaceutical Interventions (NPI) are currently the only mechanism governments can use to mitigate the impact of the COVID-19 epidemic. Similarly, to the actual spread of the disease, the dynamics of the contention patterns emerging from the application of NPIs are complex and depend on interactions between people within a specific region as well as other stochastic factors associated to demographic, geographic, political and economical conditions. Agent-based models simulate microscopic rules of simultaneous interactions of multiple agents within a population in an attempt to reproduce the complex dynamics of the effect of the contention measures. In this way it is possible to design individual behaviors along with NPI scenarios, measuring how the simulation dynamics is affected and therefore, yielding rapid insights to perform a broad assessment of the potential of composite interventions at different stages of the epidemic. In this paper we describe a model and a tool to experiment with this kind of analysis, considering a number of widely-applied NPIs such as social distancing, case isolation, home quarantine, total lockdown, sentinel testing, mask wearing and a novel &#8220;zonal&#8221; enforcement requiring these interventions to be applied gradually to separated districts (zones). The choice of the most adequate interventions, or mixture of interventions, ultimately will depend on the socio-economic and health conditions of a particular territory and on further large-scale simulation and feasibility estimation of those scenarios yielding a potential mitigation impact, using the insights discovered with the simulation tool.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Contención de la epidemia COVID-19]]></kwd>
<kwd lng="es"><![CDATA[evaluación de intervenciones no farmacéuticas]]></kwd>
<kwd lng="es"><![CDATA[simulación y herramientas orientadas a agentes]]></kwd>
<kwd lng="en"><![CDATA[COVID-19 epidemic contention]]></kwd>
<kwd lng="en"><![CDATA[Non-Pharmaceutical Interventions assessment]]></kwd>
<kwd lng="en"><![CDATA[Agent-based simulation and tools]]></kwd>
</kwd-group>
</article-meta>
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