<?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>1679-4974</journal-id>
<journal-title><![CDATA[Epidemiologia e Serviços de Saúde]]></journal-title>
<abbrev-journal-title><![CDATA[Epidemiol. Serv. Saúde]]></abbrev-journal-title>
<issn>1679-4974</issn>
<publisher>
<publisher-name><![CDATA[Secretaria de Vigilância em Saúde e Ambiente - Ministério da Saúde do Brasil]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1679-49742020000500024</article-id>
<article-id pub-id-type="doi">10.1590/s1679-49742020000500022</article-id>
<title-group>
<article-title xml:lang="pt"><![CDATA[Projeção de internações em terapia intensiva pela COVID-19 no Distrito Federal, Brasil: uma análise do impacto das medidas de distanciamento social]]></article-title>
<article-title xml:lang="es"><![CDATA[Proyección de hospitalizaciones en cuidados intensivos por COVID-19 en el Distrito Federal, Brasil: un análisis del impacto de las medidas de distanciamiento social]]></article-title>
<article-title xml:lang="en"><![CDATA[Projection of COVID-19 intensive care hospitalizations in the Federal District, Brazil: an analysis of the impact of social distancing measures]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zimmermann]]></surname>
<given-names><![CDATA[Ivan]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sanchez]]></surname>
<given-names><![CDATA[Mauro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Brant]]></surname>
<given-names><![CDATA[Jonas]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Alves]]></surname>
<given-names><![CDATA[Domingos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidade de Brasília Faculdade de Ciências da Saúde ]]></institution>
<addr-line><![CDATA[Brasília DF]]></addr-line>
<country>Brasil</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidade de São Paulo Faculdade de Medicina de Ribeirão Preto ]]></institution>
<addr-line><![CDATA[Ribeirão Preto SP]]></addr-line>
<country>Brasil</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>29</volume>
<numero>5</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.iec.gov.br/scielo.php?script=sci_arttext&amp;pid=S1679-49742020000500024&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.iec.gov.br/scielo.php?script=sci_abstract&amp;pid=S1679-49742020000500024&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.iec.gov.br/scielo.php?script=sci_pdf&amp;pid=S1679-49742020000500024&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo  Objetivo: Construir cenários e analisar o impacto das políticas de distanciamento social na propagação da COVID-19 e a necessidade de leitos de unidades de terapia intensiva (UTI).  Métodos: Sobre modelo compartimental de transição dinâmica e simulações de Monte Carlo, construíram-se três cenários de propagação conforme o nível de adesão às medidas de distanciamento social no Distrito Federal, Brasil. Os valores dos parâmetros do modelo fundamentaram-se em fontes oficiais, bases com indexação bibliográfica e repositórios públicos de dados.  Resultados: O cenário favorável, com manutenção constante de 58% de adesão ao distanciamento social, estimou pico de 189 (intervalo interquartil [IIQ]: 57 a 394) internações-UTI em 7/3/2021. A ausência do distanciamento implicaria grave cenário, com pico de 6.214 (IIQ: 4.618 a 8.415) internações-UTI já na data provável de 14/7/2020.  Conclusão: as projeções indicam alto impacto das medidas de distanciamento social e reforçam a aplicabilidade de indicadores públicos no monitoramento da COVID-19.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Objetivo: Construir escenarios y analizar el impacto de las políticas de distanciamiento social en la propagación de Covid-19 y la necesidad de camas en unidades de cuidados intensivos (UCI).  Métodos: Con un modelo compartimental de transición dinámica y simulaciones de Monte Carlo, los escenarios de propagación se construyeron de acuerdo al nivel de adhesión de las medidas de distanciamiento social en el Distrito Federal, Brasil. Los parámetros se basaron en fuentes oficiales, bases de datos indexadas y repositorios de datos.  Resultados: La adhesión al nivel de distanciamiento social con manutención constante de 58% fue el único escenario favorable, con un pico de 189 (intervalo intercuartil IIC: 57 a 394) admisiones en la UCI el 7/3/2021. La ausencia de distanciamiento implicaría en grave escenario, con un pico de 6.214 (IIC: 4.618 a 8.415) admisiones en UCI ya en la fecha probable de 14/7/2020.  Conclusión: Las proyecciones muestran el alto impacto de las medidas de distanciamiento social y la aplicabilidad de indicadores públicos en el monitoreo.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Objective: To build scenarios and analyze the impact of social distancing policies on the spread of COVID-19 and the need for intensive care unit beds.  Methods: Three dissemination scenarios were built according to level of adherence to social distancing measures in the context of Brazil's Federal District, based on a dynamic transition compartmental model and Monte Carlo simulations. The model's parameter values were based on official sources, indexed bibliographic databases and public data repositories.  Results: The favorable scenario, with constant 58% adherence to social distancing, estimated a peak of 189 (interquartile range [IQR]: 57 &#8211; 394) ICU hospitalizations on March 3rd2021. Absence of social distancing would result in an unfavorable scenario with a peak of 6,214 (IQR: 4,618 &#8211; 8,415) ICU hospitalizations probably as soon as July 14th2020.  Conclusion: The projections indicate the high impact of social distancing measures and emphasize the applicability of public indicators for COVID-19 monitoring.]]></p></abstract>
<kwd-group>
<kwd lng="pt"><![CDATA[Infecções por Coronavirus]]></kwd>
<kwd lng="pt"><![CDATA[Unidades de Terapia Intensiva]]></kwd>
<kwd lng="pt"><![CDATA[Ocupação de Leitos]]></kwd>
<kwd lng="pt"><![CDATA[Avaliação em Saúde]]></kwd>
<kwd lng="pt"><![CDATA[Política Pública]]></kwd>
<kwd lng="es"><![CDATA[Infecciones por Coronavirus]]></kwd>
<kwd lng="es"><![CDATA[Unidades de Cuidados Intensivos]]></kwd>
<kwd lng="es"><![CDATA[Ocupación de Camas]]></kwd>
<kwd lng="es"><![CDATA[Evaluación en Salud]]></kwd>
<kwd lng="es"><![CDATA[Política Pública]]></kwd>
<kwd lng="en"><![CDATA[Coronavirus Infections]]></kwd>
<kwd lng="en"><![CDATA[Intensive Care Units]]></kwd>
<kwd lng="en"><![CDATA[Bed Occupancy]]></kwd>
<kwd lng="en"><![CDATA[Health Evaluation]]></kwd>
<kwd lng="en"><![CDATA[Public Policy]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<label>1</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mohammadi]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Meskini]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Nascimento Pinto]]></surname>
<given-names><![CDATA[AL]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[2019 Novel coronavirus (COVID-19) overview]]></article-title>
<source><![CDATA[Z Gesundh Wiss [Internet]]]></source>
<year>2020</year>
<page-range>1-9</page-range></nlm-citation>
</ref>
<ref id="B2">
<label>2</label><nlm-citation citation-type="book">
<collab>Ministério da Saúde (BR)</collab>
<source><![CDATA[Painel coronavírus [Internet]]]></source>
<year>2020</year>
<publisher-loc><![CDATA[Brasília ]]></publisher-loc>
<publisher-name><![CDATA[Ministério da Saúde]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B3">
<label>3</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rothan]]></surname>
<given-names><![CDATA[HA]]></given-names>
</name>
<name>
<surname><![CDATA[Byrareddy]]></surname>
<given-names><![CDATA[SN]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak]]></article-title>
<source><![CDATA[J Autoimmun [Internet]]]></source>
<year>2020</year>
<volume>109</volume>
<page-range>102433</page-range></nlm-citation>
</ref>
<ref id="B4">
<label>4</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Prem]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Russell]]></surname>
<given-names><![CDATA[TW]]></given-names>
</name>
<name>
<surname><![CDATA[Kucharski]]></surname>
<given-names><![CDATA[AJ]]></given-names>
</name>
<name>
<surname><![CDATA[Eggo]]></surname>
<given-names><![CDATA[RM]]></given-names>
</name>
<name>
<surname><![CDATA[Davies]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study]]></article-title>
<source><![CDATA[Lancet Public Health [Internet]]]></source>
<year>2020</year>
<volume>5</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>e261-70</page-range></nlm-citation>
</ref>
<ref id="B5">
<label>5</label><nlm-citation citation-type="book">
<collab>Governo do Distrito Federal</collab>
<article-title xml:lang=""><![CDATA[Decreto n° 40.509, de 11 de março de 2020]]></article-title>
<source><![CDATA[Dispõe sobre as medidas para enfrentamento da emergência de saúde pública de importância internacional decorrente do novo coronavírus, e dá outras providências [Internet]]]></source>
<year>2020</year>
<publisher-loc><![CDATA[Brasília (DF) ]]></publisher-loc>
<publisher-name><![CDATA[Diário Oficial do Distrito Federal]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B6">
<label>6</label><nlm-citation citation-type="journal">
<collab>Rede CoVida</collab>
<article-title xml:lang=""><![CDATA[Os impactos das medidas de distanciamento social e redução de fluxo intermunicipal na Bahia]]></article-title>
<source><![CDATA[Bol CoVida [Internet]]]></source>
<year>2020</year>
<volume>1</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>19</page-range></nlm-citation>
</ref>
<ref id="B7">
<label>7</label><nlm-citation citation-type="">
<collab>COVID-19 Brasil</collab>
<source><![CDATA[Monitoramento e análises da situação do Coronavírus no Brasil [Internet]]]></source>
<year>2020</year>
<publisher-loc><![CDATA[São Paulo ]]></publisher-loc>
</nlm-citation>
</ref>
<ref id="B8">
<label>8</label><nlm-citation citation-type="journal">
<collab>Organização Pan-Americana de Saúde - OPAS</collab>
<article-title xml:lang=""><![CDATA[Ministério da Saúde (BR). Universidade de Brasília. Butantã]]></article-title>
<source><![CDATA[Pressão hospitalar por COVID-19 [Internet]]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B9">
<label>9</label><nlm-citation citation-type="book">
<collab>Presidência da República (BR)</collab>
<source><![CDATA[Casa Civil. Avaliação de políticas públicas: guia prático de análise ex post [Internet]]]></source>
<year>2018</year>
<publisher-loc><![CDATA[Brasília ]]></publisher-loc>
<publisher-name><![CDATA[Presidência da República]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B10">
<label>10</label><nlm-citation citation-type="book">
<collab>Instituto Brasileiro de Geografia e Estatística - IBGE</collab>
<article-title xml:lang=""><![CDATA[Diretoria de Pesquisas. Coordenação de População e Indicadores Sociais. Gerência de Estudos e Análises da Dinâmica Demográfica]]></article-title>
<source><![CDATA[Projeção da população do Brasil e Unidades da Federação por sexo e idade para o período 2000-2030 [Internet]]]></source>
<year>2020</year>
<publisher-loc><![CDATA[Rio de Janeiro ]]></publisher-loc>
<publisher-name><![CDATA[Instituto Brasileiro de Geografia e Estatística]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B11">
<label>11</label><nlm-citation citation-type="book">
<collab>Inloco</collab>
<source><![CDATA[Mapa brasileiro da COVID-19 [Internet]]]></source>
<year>2020</year>
<publisher-loc><![CDATA[São Paulo ]]></publisher-loc>
<publisher-name><![CDATA[Inloco]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B12">
<label>12</label><nlm-citation citation-type="book">
<collab>Ministério da Saúde (BR)</collab>
<article-title xml:lang=""><![CDATA[Secretaria de Ciência Tecnologia e Insumos Estratégicos]]></article-title>
<source><![CDATA[Departamento de Ciência e Tecnologia. Diretrizes metodológicas: diretriz de avaliação econômica [Internet]]]></source>
<year>2014</year>
<publisher-loc><![CDATA[Brasília ]]></publisher-loc>
<publisher-name><![CDATA[Ministério da Saúde]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B13">
<label>13</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Vynnycky]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[White]]></surname>
<given-names><![CDATA[RG]]></given-names>
</name>
<name>
<surname><![CDATA[Fine]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
</person-group>
<source><![CDATA[An introduction to infectious disease modelling]]></source>
<year>2010</year>
<publisher-loc><![CDATA[Oxford ]]></publisher-loc>
<publisher-name><![CDATA[Oxford University Press]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B14">
<label>14</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rocha Filho]]></surname>
<given-names><![CDATA[TM]]></given-names>
</name>
<name>
<surname><![CDATA[Santos]]></surname>
<given-names><![CDATA[FSG]]></given-names>
</name>
<name>
<surname><![CDATA[Gomes]]></surname>
<given-names><![CDATA[VB]]></given-names>
</name>
<name>
<surname><![CDATA[Rocha]]></surname>
<given-names><![CDATA[TAH]]></given-names>
</name>
<name>
<surname><![CDATA[Croda]]></surname>
<given-names><![CDATA[JHR]]></given-names>
</name>
<name>
<surname><![CDATA[Ramalho]]></surname>
<given-names><![CDATA[WM]]></given-names>
</name>
</person-group>
<source><![CDATA[Expected impact of COVID-19 outbreak in a major metropolitan area in Brazil. medRxiv Prepr [Internet]]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B15">
<label>15</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hill]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<source><![CDATA[Modeling COVID-19 spread vs healthcare capacity [Internet]]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B16">
<label>16</label><nlm-citation citation-type="">
<collab>Penn University</collab>
<source><![CDATA[CHIME model: discrete-time SIR modeling of infections/recovery [Internet]]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B17">
<label>17</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Eikenberry]]></surname>
<given-names><![CDATA[SE]]></given-names>
</name>
<name>
<surname><![CDATA[Mancuso]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Iboi]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Phan]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
<name>
<surname><![CDATA[Eikenberry]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
<name>
<surname><![CDATA[Kuang]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[To mask or not to mask: modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic]]></article-title>
<source><![CDATA[Infect Dis Model [Internet]]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B18">
<label>18</label><nlm-citation citation-type="book">
<collab>World Health Organization - WHO</collab>
<source><![CDATA[&#8220;Immunity passports&#8221; in the context of COVID-19 [Internet]]]></source>
<year>2020</year>
<publisher-loc><![CDATA[Genebra ]]></publisher-loc>
<publisher-name><![CDATA[World Health Organization]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B19">
<label>19</label><nlm-citation citation-type="book">
<collab>Governo do Distrito Federal</collab>
<article-title xml:lang=""><![CDATA[Secretaria de Saúde]]></article-title>
<source><![CDATA[Boletins Informativos DIVEP/CIEVES (COE). Boletins informativos sobre coronavirus (COVID-19)]]></source>
<year>2020</year>
<publisher-loc><![CDATA[Brasília ]]></publisher-loc>
<publisher-name><![CDATA[GDF]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B20">
<label>20</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Briggs]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Claxton]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
<name>
<surname><![CDATA[Sculpher]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<source><![CDATA[Decision modelling for health economic evaluation]]></source>
<year>2011</year>
<publisher-loc><![CDATA[Oxford ]]></publisher-loc>
<publisher-name><![CDATA[Oxford University Press]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B21">
<label>21</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zimmermann]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Modelo de projeção da demanda por leitos de UTI por COVID-19]]></article-title>
<source><![CDATA[Mendeley Data [Internet]]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B22">
<label>22</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mellan]]></surname>
<given-names><![CDATA[TA]]></given-names>
</name>
<name>
<surname><![CDATA[Hoeltgebaum]]></surname>
<given-names><![CDATA[HH]]></given-names>
</name>
<name>
<surname><![CDATA[Mishra]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Whittaker]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Schnekenberg]]></surname>
<given-names><![CDATA[RP]]></given-names>
</name>
<name>
<surname><![CDATA[Gandy]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Report 21: estimating COVID-19 cases and reproduction number in Brazil]]></article-title>
<source><![CDATA[Imperial College London [Internet]]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B23">
<label>23</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tariq]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Lee]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Roosa]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
<name>
<surname><![CDATA[Blumberg]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Yan]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
</person-group>
<source><![CDATA[Real-time monitoring the transmission potential of COVID-19 in Singapore, March 2020. medRxiv Prepr [Internet]]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B24">
<label>24</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chowell]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<name>
<surname><![CDATA[Sattenspiel]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Bansal]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Viboud]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Mathematical models to characterize early epidemic growth: a review]]></article-title>
<source><![CDATA[Phys Life Rev [Internet]]]></source>
<year>2016</year>
<volume>18</volume>
<page-range>66-97</page-range></nlm-citation>
</ref>
<ref id="B25">
<label>25</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Fang]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Cai]]></surname>
<given-names><![CDATA[Z]]></given-names>
</name>
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Gao]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Min]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Comorbid chronic diseases and acute organ injuries are strongly correlated with disease severity and mortality among COVID-19 patients: a systemic review and meta-analysis]]></article-title>
<source><![CDATA[Research (Wash D C) [Internet]]]></source>
<year>2020</year>
<page-range>2402961</page-range></nlm-citation>
</ref>
<ref id="B26">
<label>26</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sanche]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Lin]]></surname>
<given-names><![CDATA[YT]]></given-names>
</name>
<name>
<surname><![CDATA[Xu]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Romero-Severson]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Hengartner]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Ke]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
</person-group>
<source><![CDATA[The novel Coronavirus, 2019-nCoV, is highly contagious and more infectious than initially estimated. medRxiv Prepr [Internet]]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B27">
<label>27</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Linton]]></surname>
<given-names><![CDATA[NM]]></given-names>
</name>
<name>
<surname><![CDATA[Kobayashi]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Hayashi]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
<name>
<surname><![CDATA[Akhmetzhanov]]></surname>
<given-names><![CDATA[AR]]></given-names>
</name>
<name>
<surname><![CDATA[Jung]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Incubation period and other epidemiological characteristics of 2019 novel coronavirus infections with right truncation: a statistical analysis of publicly available case data]]></article-title>
<source><![CDATA[J Clin Med [Internet]]]></source>
<year>2020</year>
<volume>9</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>538</page-range></nlm-citation>
</ref>
<ref id="B28">
<label>28</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Verity]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Okell]]></surname>
<given-names><![CDATA[LC]]></given-names>
</name>
<name>
<surname><![CDATA[Dorigatti]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[Winskill]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
<name>
<surname><![CDATA[Whittaker]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Imai]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Estimates of the severity of coronavirus disease 2019: a model-based analysis]]></article-title>
<source><![CDATA[Lancet Infect Dis [Internet]]]></source>
<year>2020</year>
<volume>20</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>669-77</page-range></nlm-citation>
</ref>
<ref id="B29">
<label>29</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ferguson]]></surname>
<given-names><![CDATA[Neil M]]></given-names>
</name>
<name>
<surname><![CDATA[Laydon]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Nedjati-Gilani]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<name>
<surname><![CDATA[Imai]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Ainslie]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
<name>
<surname><![CDATA[Baguelin]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand]]></article-title>
<source><![CDATA[Imperial College London [Internet]]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B30">
<label>30</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Grasselli]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<name>
<surname><![CDATA[Zangrillo]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Zanella]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Antonelli]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Cabrini]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Castelli]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy Region, Italy]]></article-title>
<source><![CDATA[JAMA [Internet]]]></source>
<year>2020</year>
<volume>323</volume>
<numero>16</numero>
<issue>16</issue>
<page-range>1574-81</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
