Modelling COVID 19 in the Basque Country from introduction to control measure response.
Identifieur interne : 001248 ( Main/Corpus ); précédent : 001247; suivant : 001249Modelling COVID 19 in the Basque Country from introduction to control measure response.
Auteurs : Maíra Aguiar ; Eduardo Millán Ortuondo ; Joseba Bidaurrazaga Van-Dierdonck ; Javier Mar ; Nico StollenwerkSource :
- Scientific reports [ 2045-2322 ] ; 2020.
English descriptors
- KwdEn :
- Betacoronavirus (isolation & purification), COVID-19 (MeSH), Coronavirus Infections (epidemiology), Coronavirus Infections (pathology), Coronavirus Infections (prevention & control), Coronavirus Infections (virology), Humans (MeSH), Models, Theoretical (MeSH), Pandemics (prevention & control), Pneumonia, Viral (epidemiology), Pneumonia, Viral (pathology), Pneumonia, Viral (prevention & control), Pneumonia, Viral (virology), Quarantine (MeSH), SARS-CoV-2 (MeSH), Spain (epidemiology).
- MESH :
- geographic , epidemiology : Spain.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- isolation & purification : Betacoronavirus.
- pathology : Coronavirus Infections, Pneumonia, Viral.
- prevention & control : Coronavirus Infections, Pandemics, Pneumonia, Viral.
- virology : Coronavirus Infections, Pneumonia, Viral.
- COVID-19, Humans, Models, Theoretical, Quarantine, SARS-CoV-2.
Abstract
In March 2020, a multidisciplinary task force (so-called Basque Modelling Task Force, BMTF) was created to assist the Basque health managers and Government during the COVID-19 responses. BMTF is a modelling team, working on different approaches, including stochastic processes, statistical methods and artificial intelligence. Here we describe the efforts and challenges to develop a flexible modeling framework able to describe the dynamics observed for the tested positive cases, including the modelling development steps. The results obtained by a new stochastic SHARUCD model framework are presented. Our models differentiate mild and asymptomatic from severe infections prone to be hospitalized and were able to predict the course of the epidemic, providing important projections on the national health system's necessities during the increased population demand on hospital admissions. Short and longer-term predictions were tested with good results adjusted to the available epidemiological data. We have shown that the partial lockdown measures were effective and enough to slow down disease transmission in the Basque Country. The growth rate [Formula: see text] was calculated from the model and from the data and the implications for the reproduction ratio r are shown. The analysis of the growth rates from the data led to improved model versions describing after the exponential phase also the new information obtained during the phase of response to the control measures. This framework is now being used to monitor disease transmission while the country lockdown was gradually lifted, with insights to specific programs for a general policy of "social distancing" and home quarantining.
DOI: 10.1038/s41598-020-74386-1
PubMed: 33057119
PubMed Central: PMC7560887
Links to Exploration step
pubmed:33057119Le document en format XML
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Ortuondo</name><affiliation><nlm:affiliation>Osakidetza Basque Health Service, General Sub-directorate for Healthcare, Vitoria-Gasteiz, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Bidaurrazaga Van Dierdonck, Joseba" sort="Bidaurrazaga Van Dierdonck, Joseba" uniqKey="Bidaurrazaga Van Dierdonck J" first="Joseba" last="Bidaurrazaga Van-Dierdonck">Joseba Bidaurrazaga Van-Dierdonck</name><affiliation><nlm:affiliation>Public Health, Basque Health Department, Rekalde Zumarkalea 39A, 48008, Bilbao, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Mar, Javier" sort="Mar, Javier" uniqKey="Mar J" first="Javier" last="Mar">Javier Mar</name><affiliation><nlm:affiliation>Research Unit, Osakidetza Basque Health Service, Debagoiena Integrated Healthcare Organisation, Arrasate-Mondragón, Guipúzcoa, Spain.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Biodonostia Health Research Institute, Donostia-San Sebastián, Guipúzcoa, 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sort="Ortuondo, Eduardo Millan" uniqKey="Ortuondo E" first="Eduardo Millán" last="Ortuondo">Eduardo Millán Ortuondo</name><affiliation><nlm:affiliation>Osakidetza Basque Health Service, General Sub-directorate for Healthcare, Vitoria-Gasteiz, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Bidaurrazaga Van Dierdonck, Joseba" sort="Bidaurrazaga Van Dierdonck, Joseba" uniqKey="Bidaurrazaga Van Dierdonck J" first="Joseba" last="Bidaurrazaga Van-Dierdonck">Joseba Bidaurrazaga Van-Dierdonck</name><affiliation><nlm:affiliation>Public Health, Basque Health Department, Rekalde Zumarkalea 39A, 48008, Bilbao, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Mar, Javier" sort="Mar, Javier" uniqKey="Mar J" first="Javier" last="Mar">Javier Mar</name><affiliation><nlm:affiliation>Research Unit, Osakidetza Basque Health Service, Debagoiena Integrated Healthcare Organisation, Arrasate-Mondragón, Guipúzcoa, Spain.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Biodonostia Health Research Institute, Donostia-San Sebastián, Guipúzcoa, Spain.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Economic Evaluation Unit, Kronikgune Institute for Health Services Research, Barakaldo, Spain.</nlm:affiliation></affiliation></author><author><name sortKey="Stollenwerk, Nico" sort="Stollenwerk, Nico" uniqKey="Stollenwerk N" first="Nico" last="Stollenwerk">Nico Stollenwerk</name><affiliation><nlm:affiliation>Dipartimento di Matematica, Università degli Studi di Trento, Via Sommarive, 14, 38123, Povo, Trento, Italy.</nlm:affiliation></affiliation><affiliation><nlm:affiliation>Center for Mathematics, Fundamental Applications and Operations Research, Lisbon University, Lisbon, Portugal.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Scientific reports</title><idno type="eISSN">2045-2322</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Betacoronavirus (isolation & purification)</term><term>COVID-19 (MeSH)</term><term>Coronavirus Infections (epidemiology)</term><term>Coronavirus Infections (pathology)</term><term>Coronavirus Infections (prevention & control)</term><term>Coronavirus Infections (virology)</term><term>Humans (MeSH)</term><term>Models, Theoretical (MeSH)</term><term>Pandemics (prevention & control)</term><term>Pneumonia, Viral (epidemiology)</term><term>Pneumonia, Viral (pathology)</term><term>Pneumonia, Viral (prevention & control)</term><term>Pneumonia, Viral (virology)</term><term>Quarantine (MeSH)</term><term>SARS-CoV-2 (MeSH)</term><term>Spain (epidemiology)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Spain</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="isolation & purification" xml:lang="en"><term>Betacoronavirus</term></keywords><keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Coronavirus Infections</term><term>Pandemics</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" xml:lang="en"><term>COVID-19</term><term>Humans</term><term>Models, Theoretical</term><term>Quarantine</term><term>SARS-CoV-2</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In March 2020, a multidisciplinary task force (so-called Basque Modelling Task Force, BMTF) was created to assist the Basque health managers and Government during the COVID-19 responses. BMTF is a modelling team, working on different approaches, including stochastic processes, statistical methods and artificial intelligence. Here we describe the efforts and challenges to develop a flexible modeling framework able to describe the dynamics observed for the tested positive cases, including the modelling development steps. The results obtained by a new stochastic SHARUCD model framework are presented. Our models differentiate mild and asymptomatic from severe infections prone to be hospitalized and were able to predict the course of the epidemic, providing important projections on the national health system's necessities during the increased population demand on hospital admissions. Short and longer-term predictions were tested with good results adjusted to the available epidemiological data. We have shown that the partial lockdown measures were effective and enough to slow down disease transmission in the Basque Country. The growth rate [Formula: see text] was calculated from the model and from the data and the implications for the reproduction ratio r are shown. The analysis of the growth rates from the data led to improved model versions describing after the exponential phase also the new information obtained during the phase of response to the control measures. This framework is now being used to monitor disease transmission while the country lockdown was gradually lifted, with insights to specific programs for a general policy of "social distancing" and home quarantining.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">33057119</PMID><DateCompleted><Year>2020</Year><Month>10</Month><Day>28</Day></DateCompleted><DateRevised><Year>2020</Year><Month>12</Month><Day>18</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">2045-2322</ISSN><JournalIssue CitedMedium="Internet"><Volume>10</Volume><Issue>1</Issue><PubDate><Year>2020</Year><Month>10</Month><Day>14</Day></PubDate></JournalIssue><Title>Scientific reports</Title><ISOAbbreviation>Sci Rep</ISOAbbreviation></Journal><ArticleTitle>Modelling COVID 19 in the Basque Country from introduction to control measure response.</ArticleTitle><Pagination><MedlinePgn>17306</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1038/s41598-020-74386-1</ELocationID><Abstract><AbstractText>In March 2020, a multidisciplinary task force (so-called Basque Modelling Task Force, BMTF) was created to assist the Basque health managers and Government during the COVID-19 responses. BMTF is a modelling team, working on different approaches, including stochastic processes, statistical methods and artificial intelligence. Here we describe the efforts and challenges to develop a flexible modeling framework able to describe the dynamics observed for the tested positive cases, including the modelling development steps. The results obtained by a new stochastic SHARUCD model framework are presented. Our models differentiate mild and asymptomatic from severe infections prone to be hospitalized and were able to predict the course of the epidemic, providing important projections on the national health system's necessities during the increased population demand on hospital admissions. Short and longer-term predictions were tested with good results adjusted to the available epidemiological data. We have shown that the partial lockdown measures were effective and enough to slow down disease transmission in the Basque Country. The growth rate [Formula: see text] was calculated from the model and from the data and the implications for the reproduction ratio r are shown. The analysis of the growth rates from the data led to improved model versions describing after the exponential phase also the new information obtained during the phase of response to the control measures. This framework is now being used to monitor disease transmission while the country lockdown was gradually lifted, with insights to specific programs for a general policy of "social distancing" and home quarantining.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Aguiar</LastName><ForeName>Maíra</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Dipartimento di Matematica, Università degli Studi di Trento, Via Sommarive, 14, 38123, Povo, Trento, Italy. m.aguiar@unitn.it.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Basque Center for Applied Mathematics (BCAM), Alameda Mazarredo, 14, 48009, Bilbao, Spain. m.aguiar@unitn.it.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Ikerbasque, Basque Foundation for Science, Bilbao, Spain. m.aguiar@unitn.it.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ortuondo</LastName><ForeName>Eduardo Millán</ForeName><Initials>EM</Initials><AffiliationInfo><Affiliation>Osakidetza Basque Health Service, General Sub-directorate for Healthcare, Vitoria-Gasteiz, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bidaurrazaga Van-Dierdonck</LastName><ForeName>Joseba</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Public Health, Basque Health Department, Rekalde Zumarkalea 39A, 48008, Bilbao, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mar</LastName><ForeName>Javier</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Research Unit, Osakidetza Basque Health Service, Debagoiena Integrated Healthcare Organisation, Arrasate-Mondragón, Guipúzcoa, Spain.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Biodonostia Health Research Institute, Donostia-San Sebastián, Guipúzcoa, Spain.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Economic Evaluation Unit, Kronikgune Institute for Health Services Research, Barakaldo, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Stollenwerk</LastName><ForeName>Nico</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>Dipartimento di Matematica, Università degli Studi di Trento, Via Sommarive, 14, 38123, Povo, Trento, Italy.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Mathematics, Fundamental Applications and Operations Research, Lisbon University, Lisbon, Portugal.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>792494</GrantID><Agency>H2020 Marie Skłodowska-Curie Actions</Agency><Country>International</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate 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