<?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>0124-0064</journal-id>
<journal-title><![CDATA[Revista de Salud Pública]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. salud pública]]></abbrev-journal-title>
<issn>0124-0064</issn>
<publisher>
<publisher-name><![CDATA[Instituto de Salud Publica, Facultad de Medicina - Universidad Nacional de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0124-00642020000200144</article-id>
<article-id pub-id-type="doi">10.15446/rsap.v22n2.86380</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Impacto de diferentes medidas de mitigación en el curso de la pandemia de COVID-19 en Chile: proyección preliminar para el período del 14 de abril al 14 de mayo]]></article-title>
<article-title xml:lang="en"><![CDATA[Impact of mitigation measures on the COVID-19 pandemic in Chile: preliminary data for the period April 14 to May 14]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ochoa-Rosales]]></surname>
<given-names><![CDATA[Carolina]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González-Jaramillo]]></surname>
<given-names><![CDATA[Nathalia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vera-Calzaretta]]></surname>
<given-names><![CDATA[Aldo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Franco]]></surname>
<given-names><![CDATA[Oscar H.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Erasmus Medical Center  ]]></institution>
<addr-line><![CDATA[Rotterdam ]]></addr-line>
<country>Países Bajos</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,University of Bern Institute of Social and Preventive Medicine ]]></institution>
<addr-line><![CDATA[Berna ]]></addr-line>
<country>Switzerland</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad de Concepción Facultad de Medicina Departamento de Salud Pública]]></institution>
<addr-line><![CDATA[Concepción ]]></addr-line>
<country>Chile</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,University of Bern Institute of Social and Preventive Medicine ]]></institution>
<addr-line><![CDATA[Berna ]]></addr-line>
<country>Switzerland</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2020</year>
</pub-date>
<volume>22</volume>
<numero>2</numero>
<fpage>144</fpage>
<lpage>149</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0124-00642020000200144&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0124-00642020000200144&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0124-00642020000200144&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Objetivo Modelar el curso de la pandemia COVID-19 en Chile y proyectar la demanda de recursos hospitalarios y letalidad en escenarios simulados: primero, recurriendo a distintas medidas de mitigación para contener la propagación en un mes -desde el 14 de abril hasta el 14 de mayo del 2020- y, segundo, en el supuesto contagio del 70% de la población, según edad, sin límite de tiempo.  Métodos Utilizamos como base el número de contagios confirmados con SARS-CoV-2 en Chile hasta el 14 de abril del 2020 (8 273 casos, 94 muertes). Para los distintos escenarios, asumimos un número reproductivo básico que va desde R0=2,5 hasta R0=1,5. La proyección de la demanda hospitalaria y letalidad por edad se fundamentaron en reportes italianos y británicos.  Resultados Estimamos que para el 14 de mayo del 2020 habría en Chile 2 019 775 contagiados y 15 068 fallecidos en ausencia de medidas de mitigación (R0=2,5). Al implementar medidas que reduzcan R0 a 1,5 (detección temprana y aislamiento de casos, cuarentena y distanciamiento social de mayores de 70 años), el número de contagios y letalidad disminuirían a 94 235 y 703 respectivamente. Sin embargo, la demanda hospitalaria aún sobrepasaría la capacidad de respuesta. La población de mayor riesgo la componen los mayores de 60 años.  Conclusión Encontramos evidencia a favor de las medidas de mitigación implementadas por el Gobierno chileno. Sin embargo, medidas más estrictas son necesarias para no colapsar el sistema sanitario, que cuenta con menos recursos hospitalarios que los proyectados. Es esencial aumentar la capacidad hospitalaria en términos de equipamiento y entrenamiento del personal de salud.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Objetive To model disease progression, healthcare demand and case fatality rate attributed to COVID-19 pandemic that may occur in Chile in 1-month time, by simulating different scenarios according to diverse mitigation measures hypothetically implemented. Furthermore, we aimed to estimate the same outcomes assuming that 70% of the population will be infected by SARS-CoV-2, with no time limit assumption.  Methods We based on the number of confirmed COVID-19 cases in Chile up to April 14th 2020 (8 273 cases and 94 deaths). For the simulated scenarios we assumed basic reproduction numbers ranging from R0=2.5 to R0=1.5. The estimation of the number of patients that would require intensive care and the age-specific case fatality rate were based on data provided by the Imperial College of London and the Instituto Superiore di Sanità en Italia.  Results If no mitigation measures were applied (R0=2.5), by May 25, Chile would have 2 019 775 cases and 15 068 deaths. If mitigations measures were implemented to decrease R0 to 1.5 (early detection of cases, quarantine, social distancing of elderly), the number of cases and deaths would importantly decrease. Nonetheless, the demand for in-hospital care including intensive care would exceed the available resources. Our age-specific analysis showed that population over 60 years are at higher risk of needing intensive care and death.  Conclusion Our evidence supports the mitigation measures implemented by the Chilean government. Nevertheless, more stringent measures are needed to prevent the health care system's collapse due to shortfall of resources to confront the COVID-19 pandemic.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Pandemias]]></kwd>
<kwd lng="es"><![CDATA[coronavirus]]></kwd>
<kwd lng="es"><![CDATA[infecciones por coronavirus]]></kwd>
<kwd lng="es"><![CDATA[salud pública]]></kwd>
<kwd lng="es"><![CDATA[epidemiología (fuente: DeCS, BIREME)]]></kwd>
<kwd lng="en"><![CDATA[Pandemics]]></kwd>
<kwd lng="en"><![CDATA[coronavirus]]></kwd>
<kwd lng="en"><![CDATA[coronavirus infections]]></kwd>
<kwd lng="en"><![CDATA[public health]]></kwd>
<kwd lng="en"><![CDATA[epidemiology (source: MeSH, NLM)]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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