<?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>2504-3145</journal-id>
<journal-title><![CDATA[Portuguese Journal of Public Health]]></journal-title>
<abbrev-journal-title><![CDATA[Port J Public Health]]></abbrev-journal-title>
<issn>2504-3145</issn>
<publisher>
<publisher-name><![CDATA[Escola Nacional de Saúde Pública]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2504-31452020000400004</article-id>
<article-id pub-id-type="doi">10.1159/000515535</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[COVID-19 Transmission dynamics: a space-and-time approach]]></article-title>
<article-title xml:lang="pt"><![CDATA[Dinâmicas de transmissão da COVID-19: uma abordagem no espaço e no tempo]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Moniz]]></surname>
<given-names><![CDATA[Marta]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Soares]]></surname>
<given-names><![CDATA[Patrícia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Nunes]]></surname>
<given-names><![CDATA[Carla]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidade NOVA de Lisboa Public Health Research Center National School of Public Health]]></institution>
<addr-line><![CDATA[Lisbon ]]></addr-line>
<country>Portugal</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidade NOVA de Lisboa Comprehensive Health Research Center ]]></institution>
<addr-line><![CDATA[Lisbon ]]></addr-line>
<country>Portugal</country>
</aff>
<pub-date pub-type="pub">
<day>30</day>
<month>12</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>30</day>
<month>12</month>
<year>2020</year>
</pub-date>
<volume>38</volume>
<fpage>4</fpage>
<lpage>10</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_arttext&amp;pid=S2504-31452020000400004&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_abstract&amp;pid=S2504-31452020000400004&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_pdf&amp;pid=S2504-31452020000400004&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Background:  At the end of January 2021, Portugal had over 700,000 confirmed COVID-19 cases. The burden of COVID-19 varies between and within countries due to differences in contextual and individual factors, transmission rates, and clinical and public health interventions.  Objectives:  To identify high-risk areas, between April and October, on a weekly basis and at the municipality level, and to assess the temporal evolution of COVID-19, considering municipalities classified by incidence levels.  Methods:  This is an ecological study following a 3-step approach, i.e., (1) calculation of the relative risk (RR) of the number of new confirmed COVID-19 cases, weekly, per municipality, using a spatial scan analysis; (2) classification of the municipalities according to the European Centre for Disease Control incidence categorization on November 19; and (3) characterization of RR temporal evolution by incidence groups.  Results:  Between April and October, the mean RR was 0.53, with a SD of 1.44, varying between 0 and 46.4. Globally, the north and Lisbon and Tagus Valley (LVT) area were the regions with the highest number of municipalities with a RR above 3.2. In April and beginning of May, most of the municipalities with an RR above 3.2 were from the north, while between May and August most municipalities with an RR above 3.2 were from LVT area. Comparing the incidence in November and retrospectively analyzing the RR showed the huge variation, with municipalities with an RR of 0 at a certain time classified as extremely high in November.  Conclusions:  Our results showed considerable variation in RR over time and space, with no consistent &#8220;better&#8221; or &#8220;worst&#8221; municipality. In addition to the several factors that influence COVID-19 transmission dynamics, there were some outbreaks over time and throughout the country and this may contribute to explaining the observed variation. Over time, on a weekly basis, it is important to identify critical areas allowing tailored and timely interventions in order to control outbreaks in early stages.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo  Introdução:  No final de Janeiro de 2021, Portugal tinha mais de 700 mil casos de COVID-19 notificados. O peso da COVID-19 varia entre países e a nível nacional devido a diferentes fatores individuais e contextuais, taxas de transmissão e diferentes intervenções clínicas e de saúde pública.  Objetivos:  Identificar áreas de maior risco, semanalmente, ao nível dos municípios, entre Abril e Outubro, e a sua variação ao longo do tempo, considerando a classificação, por níveis de incidência, dos municípios.  Métodos:  Estudo ecológico com uma abordagem em 3 fases: (1) Cálculo do risco relativo (RR) do número de novos casos de COVID-19 notificados semanalmente, por município, utilizando metodologia de análise espacial; (2) Classificação dos municípios de acordo com a categorização de incidência do Centro Europeu para o Controlo de Doenças (ECDC) a 19 de Novembro; (3) Caracterização da evolução temporal do RR por grupos de incidência.  Resultados:  Entre Abril e Outubro, a média dos RR foi 0.53 com um desvio padrão de 1.44, variando entre 0 e 46.4. Globalmente, o Norte e a área de Lisboa e Vale do Tejo (LVT) foram as regiões com um maior número de municípios com RR acima de 3.2. Em Abril e no início de Maio, a maioria dos municípios com RR acima de 3.2 eram do Norte, enquanto entre Maio e Agosto, a maioria dos municípios com RR acima de 3.2 eram da área de LVT. Comparando a incidência em Novembro e, analisando retrospetivamente, os RR apresentaram uma grande variação, existindo municípios, num determinado momento, com RR de 0, classificados como extremamente elevados, em Novembro.  Conclusões:  Os riscos relativos apresentaram uma variação considerável ao longo do período de tempo e espaço analisados. Nenhum município apresentou valores consistentemente &#8220;melhores&#8221; ou &#8220;piores.&#8221; Adicionalmente aos vários fatores que influenciam a dinâmica de transmissão da COVID-19, a existência de vários surtos que foram ocorrendo ao longo de todo o território nacional, pode também ajudar a explicar a variação observada. A identificação semanal de áreas críticas é importante, pois possibilita a implementação de intervenções atempadas permitindo um controlo controlar os surtos em fases iniciais.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Epidemiology]]></kwd>
<kwd lng="en"><![CDATA[Relative risk]]></kwd>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[Spatial distribution]]></kwd>
<kwd lng="en"><![CDATA[Transmission dynamics]]></kwd>
<kwd lng="pt"><![CDATA[Epidemiologia]]></kwd>
<kwd lng="pt"><![CDATA[Risco relativo]]></kwd>
<kwd lng="pt"><![CDATA[COVID-19]]></kwd>
<kwd lng="pt"><![CDATA[Distribuição espacial]]></kwd>
<kwd lng="pt"><![CDATA[Dinâmicas de transmissão]]></kwd>
</kwd-group>
</article-meta>
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