LockdownV1, Main, Exploration, bibRecord, 001F21

Early Phase of the COVID-19 Outbreak in Hungary and Post-Lockdown Scenarios.

Identifieur interne : 001F21 ( Main/Exploration ); précédent : 001F20; suivant : 001F22

Early Phase of the COVID-19 Outbreak in Hungary and Post-Lockdown Scenarios.

Auteurs : Gergely Röst [Hongrie] ; Ferenc A. Bartha [Hongrie] ; Norbert Bogya [Hongrie] ; Péter Boldog [Hongrie] ; Attila Dénes [Hongrie] ; Tamás Ferenci [Hongrie] ; Krisztina J. Horváth [Hongrie] ; Attila Juhász [Hongrie] ; Csilla Nagy [Hongrie] ; Tamás Tekeli [Hongrie] ; Zsolt Vizi [Hongrie] ; Beatrix Oroszi [Hongrie]

Source :

Viruses [ 1999-4915 ] ; 2020.

RBID : pubmed:32629880

Descripteurs français

KwdFr :
- Adolescent (MeSH), Adulte (MeSH), Adulte d'âge moyen (MeSH), Betacoronavirus (MeSH), Enfant (MeSH), Enfant d'âge préscolaire (MeSH), Facteurs de risque (MeSH), Facteurs sexuels (MeSH), Facteurs âges (MeSH), Femelle (MeSH), Hongrie (épidémiologie), Humains (MeSH), Infections à coronavirus (mortalité), Infections à coronavirus (prévention et contrôle), Infections à coronavirus (transmission), Infections à coronavirus (épidémiologie), Jeune adulte (MeSH), Modèles statistiques (MeSH), Mâle (MeSH), Nourrisson (MeSH), Nouveau-né (MeSH), Pandémies (prévention et contrôle), Pneumopathie virale (mortalité), Pneumopathie virale (prévention et contrôle), Pneumopathie virale (transmission), Pneumopathie virale (épidémiologie), Quarantaine (MeSH), Sujet âgé (MeSH), Sujet âgé de 80 ans ou plus (MeSH), Épidémies de maladies (prévention et contrôle).
MESH :
- mortalité : Infections à coronavirus, Pneumopathie virale.
- prévention et contrôle : Infections à coronavirus, Pandémies, Pneumopathie virale, Épidémies de maladies.
- épidémiologie : Hongrie, Infections à coronavirus, Pneumopathie virale.
- Adolescent, Adulte, Adulte d'âge moyen, Betacoronavirus, Enfant, Enfant d'âge préscolaire, Facteurs de risque, Facteurs sexuels, Facteurs âges, Femelle, Humains, Jeune adulte, Modèles statistiques, Mâle, Nourrisson, Nouveau-né, Quarantaine, Sujet âgé, Sujet âgé de 80 ans ou plus.
Wicri :
- geographic : Hongrie.

English descriptors

KwdEn :
- Adolescent (MeSH), Adult (MeSH), Age Factors (MeSH), Aged (MeSH), Aged, 80 and over (MeSH), Betacoronavirus (MeSH), COVID-19 (MeSH), Child (MeSH), Child, Preschool (MeSH), Coronavirus Infections (epidemiology), Coronavirus Infections (mortality), Coronavirus Infections (prevention & control), Coronavirus Infections (transmission), Disease Outbreaks (prevention & control), Female (MeSH), Humans (MeSH), Hungary (epidemiology), Infant (MeSH), Infant, Newborn (MeSH), Male (MeSH), Middle Aged (MeSH), Models, Statistical (MeSH), Pandemics (prevention & control), Pneumonia, Viral (epidemiology), Pneumonia, Viral (mortality), Pneumonia, Viral (prevention & control), Pneumonia, Viral (transmission), Quarantine (MeSH), Risk Factors (MeSH), SARS-CoV-2 (MeSH), Sex Factors (MeSH), Young Adult (MeSH).
MESH :
- geographic , epidemiology : Hungary.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- mortality : Coronavirus Infections, Pneumonia, Viral.
- prevention & control : Coronavirus Infections, Disease Outbreaks, Pandemics, Pneumonia, Viral.
- transmission : Coronavirus Infections, Pneumonia, Viral.
- Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Betacoronavirus, COVID-19, Child, Child, Preschool, Female, Humans, Infant, Infant, Newborn, Male, Middle Aged, Models, Statistical, Quarantine, Risk Factors, SARS-CoV-2, Sex Factors, Young Adult.

Abstract

COVID-19 epidemic has been suppressed in Hungary due to timely non-pharmaceutical interventions, prompting a considerable reduction in the number of contacts and transmission of the virus. This strategy was effective in preventing epidemic growth and reducing the incidence of COVID-19 to low levels. In this report, we present the first epidemiological and statistical analysis of the early phase of the COVID-19 outbreak in Hungary. Then, we establish an age-structured compartmental model to explore alternative post-lockdown scenarios. We incorporate various factors, such as age-specific measures, seasonal effects, and spatial heterogeneity to project the possible peak size and disease burden of a COVID-19 epidemic wave after the current measures are relaxed.

DOI: 10.3390/v12070708
PubMed: 32629880
PubMed Central: PMC7412537

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Main, to step Corpus: 002128
to stream Main, to step Curation: 002128

Le document en format XML

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<author><name sortKey="Horvath, Krisztina J" sort="Horvath, Krisztina J" uniqKey="Horvath K" first="Krisztina J" last="Horváth">Krisztina J. Horváth</name>
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<author><name sortKey="Nagy, Csilla" sort="Nagy, Csilla" uniqKey="Nagy C" first="Csilla" last="Nagy">Csilla Nagy</name>
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<author><name sortKey="Horvath, Krisztina J" sort="Horvath, Krisztina J" uniqKey="Horvath K" first="Krisztina J" last="Horváth">Krisztina J. Horváth</name>
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<country xml:lang="fr">Hongrie</country>
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<author><name sortKey="Juhasz, Attila" sort="Juhasz, Attila" uniqKey="Juhasz A" first="Attila" last="Juhász">Attila Juhász</name>
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<affiliation wicri:level="1"><nlm:affiliation>Department of Public Health, Government Office of Capital City Budapest, 1034 Budapest, Hungary.</nlm:affiliation>
<country xml:lang="fr">Hongrie</country>
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<author><name sortKey="Nagy, Csilla" sort="Nagy, Csilla" uniqKey="Nagy C" first="Csilla" last="Nagy">Csilla Nagy</name>
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<country xml:lang="fr">Hongrie</country>
<wicri:regionArea>Bolyai Institute, University of Szeged, 6720 Szeged</wicri:regionArea>
<wicri:noRegion>6720 Szeged</wicri:noRegion>
</affiliation>
<affiliation wicri:level="1"><nlm:affiliation>Department of Public Health, Government Office of Capital City Budapest, 1034 Budapest, Hungary.</nlm:affiliation>
<country xml:lang="fr">Hongrie</country>
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<author><name sortKey="Tekeli, Tamas" sort="Tekeli, Tamas" uniqKey="Tekeli T" first="Tamás" last="Tekeli">Tamás Tekeli</name>
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<author><name sortKey="Vizi, Zsolt" sort="Vizi, Zsolt" uniqKey="Vizi Z" first="Zsolt" last="Vizi">Zsolt Vizi</name>
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<author><name sortKey="Oroszi, Beatrix" sort="Oroszi, Beatrix" uniqKey="Oroszi B" first="Beatrix" last="Oroszi">Beatrix Oroszi</name>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent (MeSH)</term>
<term>Adult (MeSH)</term>
<term>Age Factors (MeSH)</term>
<term>Aged (MeSH)</term>
<term>Aged, 80 and over (MeSH)</term>
<term>Betacoronavirus (MeSH)</term>
<term>COVID-19 (MeSH)</term>
<term>Child (MeSH)</term>
<term>Child, Preschool (MeSH)</term>
<term>Coronavirus Infections (epidemiology)</term>
<term>Coronavirus Infections (mortality)</term>
<term>Coronavirus Infections (prevention & control)</term>
<term>Coronavirus Infections (transmission)</term>
<term>Disease Outbreaks (prevention & control)</term>
<term>Female (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Hungary (epidemiology)</term>
<term>Infant (MeSH)</term>
<term>Infant, Newborn (MeSH)</term>
<term>Male (MeSH)</term>
<term>Middle Aged (MeSH)</term>
<term>Models, Statistical (MeSH)</term>
<term>Pandemics (prevention & control)</term>
<term>Pneumonia, Viral (epidemiology)</term>
<term>Pneumonia, Viral (mortality)</term>
<term>Pneumonia, Viral (prevention & control)</term>
<term>Pneumonia, Viral (transmission)</term>
<term>Quarantine (MeSH)</term>
<term>Risk Factors (MeSH)</term>
<term>SARS-CoV-2 (MeSH)</term>
<term>Sex Factors (MeSH)</term>
<term>Young Adult (MeSH)</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Adolescent (MeSH)</term>
<term>Adulte (MeSH)</term>
<term>Adulte d'âge moyen (MeSH)</term>
<term>Betacoronavirus (MeSH)</term>
<term>Enfant (MeSH)</term>
<term>Enfant d'âge préscolaire (MeSH)</term>
<term>Facteurs de risque (MeSH)</term>
<term>Facteurs sexuels (MeSH)</term>
<term>Facteurs âges (MeSH)</term>
<term>Femelle (MeSH)</term>
<term>Hongrie (épidémiologie)</term>
<term>Humains (MeSH)</term>
<term>Infections à coronavirus (mortalité)</term>
<term>Infections à coronavirus (prévention et contrôle)</term>
<term>Infections à coronavirus (transmission)</term>
<term>Infections à coronavirus (épidémiologie)</term>
<term>Jeune adulte (MeSH)</term>
<term>Modèles statistiques (MeSH)</term>
<term>Mâle (MeSH)</term>
<term>Nourrisson (MeSH)</term>
<term>Nouveau-né (MeSH)</term>
<term>Pandémies (prévention et contrôle)</term>
<term>Pneumopathie virale (mortalité)</term>
<term>Pneumopathie virale (prévention et contrôle)</term>
<term>Pneumopathie virale (transmission)</term>
<term>Pneumopathie virale (épidémiologie)</term>
<term>Quarantaine (MeSH)</term>
<term>Sujet âgé (MeSH)</term>
<term>Sujet âgé de 80 ans ou plus (MeSH)</term>
<term>Épidémies de maladies (prévention et contrôle)</term>
</keywords>
<keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Hungary</term>
</keywords>
<keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="mortality" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="mortalité" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Pneumopathie virale</term>
</keywords>
<keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Coronavirus Infections</term>
<term>Disease Outbreaks</term>
<term>Pandemics</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="prévention et contrôle" xml:lang="fr"><term>Infections à coronavirus</term>
<term>Pandémies</term>
<term>Pneumopathie virale</term>
<term>Épidémies de maladies</term>
</keywords>
<keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
<keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Hongrie</term>
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<front><div type="abstract" xml:lang="en">COVID-19 epidemic has been suppressed in Hungary due to timely non-pharmaceutical interventions, prompting a considerable reduction in the number of contacts and transmission of the virus. This strategy was effective in preventing epidemic growth and reducing the incidence of COVID-19 to low levels. In this report, we present the first epidemiological and statistical analysis of the early phase of the COVID-19 outbreak in Hungary. Then, we establish an age-structured compartmental model to explore alternative post-lockdown scenarios. We incorporate various factors, such as age-specific measures, seasonal effects, and spatial heterogeneity to project the possible peak size and disease burden of a COVID-19 epidemic wave after the current measures are relaxed.</div>
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<DateCompleted><Year>2020</Year>
<Month>07</Month>
<Day>20</Day>
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<Month>12</Month>
<Day>18</Day>
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<Issue>7</Issue>
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<Title>Viruses</Title>
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<ArticleTitle>Early Phase of the COVID-19 Outbreak in Hungary and Post-Lockdown Scenarios.</ArticleTitle>
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<Abstract><AbstractText>COVID-19 epidemic has been suppressed in Hungary due to timely non-pharmaceutical interventions, prompting a considerable reduction in the number of contacts and transmission of the virus. This strategy was effective in preventing epidemic growth and reducing the incidence of COVID-19 to low levels. In this report, we present the first epidemiological and statistical analysis of the early phase of the COVID-19 outbreak in Hungary. Then, we establish an age-structured compartmental model to explore alternative post-lockdown scenarios. We incorporate various factors, such as age-specific measures, seasonal effects, and spatial heterogeneity to project the possible peak size and disease burden of a COVID-19 epidemic wave after the current measures are relaxed.</AbstractText>
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<ForeName>Gergely</ForeName>
<Initials>G</Initials>
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<AffiliationInfo><Affiliation>Bolyai Institute, University of Szeged, 6720 Szeged, Hungary.</Affiliation>
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<AffiliationInfo><Affiliation>Department of Public Health, Government Office of Capital City Budapest, 1034 Budapest, Hungary.</Affiliation>
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<AffiliationInfo><Affiliation>Department of Public Health, Government Office of Capital City Budapest, 1034 Budapest, Hungary.</Affiliation>
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<ForeName>Tamás</ForeName>
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<ForeName>Zsolt</ForeName>
<Initials>Z</Initials>
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</AffiliationInfo>
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<Initials>B</Initials>
<AffiliationInfo><Affiliation>Bolyai Institute, University of Szeged, 6720 Szeged, Hungary.</Affiliation>
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<Language>eng</Language>
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<Month>06</Month>
<Day>30</Day>
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Early Phase of the COVID-19 Outbreak in Hungary and Post-Lockdown Scenarios.

Early Phase of the COVID-19 Outbreak in Hungary and Post-Lockdown Scenarios.

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