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Modelling COVID 19 in the Basque Country from introduction to control measure response
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 an...
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Published in: | Scientific reports 2020-10, Vol.10 (1), p.17306-17306, Article 17306 |
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description | 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
λ
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_str_mv | 10.1038/s41598-020-74386-1 |
format | article |
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λ
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.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-020-74386-1</identifier><identifier>PMID: 33057119</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>631/114 ; 631/553 ; 692/699 ; Betacoronavirus - isolation & purification ; Coronavirus Infections - epidemiology ; Coronavirus Infections - pathology ; Coronavirus Infections - prevention & control ; Coronavirus Infections - virology ; COVID-19 ; Humanities and Social Sciences ; Humans ; Models, Theoretical ; multidisciplinary ; Pandemics - prevention & control ; Pneumonia, Viral - epidemiology ; Pneumonia, Viral - pathology ; Pneumonia, Viral - prevention & control ; Pneumonia, Viral - virology ; Quarantine ; SARS-CoV-2 ; Science ; Science (multidisciplinary) ; Spain - epidemiology</subject><ispartof>Scientific reports, 2020-10, Vol.10 (1), p.17306-17306, Article 17306</ispartof><rights>The Author(s) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c509t-dec06f804a6300f6bf16f718ec03c4f2a1acf25476e7710b2d3c9afc003acfcb3</citedby><cites>FETCH-LOGICAL-c509t-dec06f804a6300f6bf16f718ec03c4f2a1acf25476e7710b2d3c9afc003acfcb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7560887/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7560887/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,36990,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33057119$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Aguiar, Maíra</creatorcontrib><creatorcontrib>Ortuondo, Eduardo Millán</creatorcontrib><creatorcontrib>Bidaurrazaga Van-Dierdonck, Joseba</creatorcontrib><creatorcontrib>Mar, Javier</creatorcontrib><creatorcontrib>Stollenwerk, Nico</creatorcontrib><title>Modelling COVID 19 in the Basque Country from introduction to control measure response</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><description>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
λ
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.</description><subject>631/114</subject><subject>631/553</subject><subject>692/699</subject><subject>Betacoronavirus - isolation & purification</subject><subject>Coronavirus Infections - epidemiology</subject><subject>Coronavirus Infections - pathology</subject><subject>Coronavirus Infections - prevention & control</subject><subject>Coronavirus Infections - virology</subject><subject>COVID-19</subject><subject>Humanities and Social Sciences</subject><subject>Humans</subject><subject>Models, Theoretical</subject><subject>multidisciplinary</subject><subject>Pandemics - prevention & control</subject><subject>Pneumonia, Viral - epidemiology</subject><subject>Pneumonia, Viral - pathology</subject><subject>Pneumonia, Viral - prevention & control</subject><subject>Pneumonia, Viral - virology</subject><subject>Quarantine</subject><subject>SARS-CoV-2</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Spain - epidemiology</subject><issn>2045-2322</issn><issn>2045-2322</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9UU1PAjEQbYxGCPIHPJgevaxO2_3qxUTxiwTDRbk2pdvCkt0ttrsm_HuLIMGLc-lk3ps303kIXRK4IcDyWx-ThOcRUIiymOVpRE5Qn0KcRJRRenqU99DQ-xWESCiPCT9HPcYgyQjhfTR7s4WuqrJZ4NF0Nn7EhOOywe1S4wfpPzuNR7ZrWrfBxtk6QK2zRafa0gaSxcpuCxWutfSd09hpv7aN1xfozMjK6-H-HaCP56f30Ws0mb6MR_eTSCXA26jQClKTQyxTBmDSuSGpyUgeykzFhkoilaFJnKU6ywjMacEUl0YBsACoORugu53uupvXulA6rCMrsXZlLd1GWFmKv0hTLsXCfoksSSHPsyBwvRdwNvzWt6IuvQoXkY22nRc0TkieAuE8UOmOqpz13mlzGENAbD0RO09E8ET8eCJIaLo6XvDQ8utAILAdwQeoWWgnVrZzTTjaf7Lfzy-Y5A</recordid><startdate>20201014</startdate><enddate>20201014</enddate><creator>Aguiar, Maíra</creator><creator>Ortuondo, Eduardo Millán</creator><creator>Bidaurrazaga Van-Dierdonck, Joseba</creator><creator>Mar, Javier</creator><creator>Stollenwerk, Nico</creator><general>Nature Publishing Group UK</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20201014</creationdate><title>Modelling COVID 19 in the Basque Country from introduction to control measure response</title><author>Aguiar, Maíra ; Ortuondo, Eduardo Millán ; Bidaurrazaga Van-Dierdonck, Joseba ; Mar, Javier ; Stollenwerk, Nico</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c509t-dec06f804a6300f6bf16f718ec03c4f2a1acf25476e7710b2d3c9afc003acfcb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>631/114</topic><topic>631/553</topic><topic>692/699</topic><topic>Betacoronavirus - isolation & purification</topic><topic>Coronavirus Infections - epidemiology</topic><topic>Coronavirus Infections - pathology</topic><topic>Coronavirus Infections - prevention & control</topic><topic>Coronavirus Infections - virology</topic><topic>COVID-19</topic><topic>Humanities and Social Sciences</topic><topic>Humans</topic><topic>Models, Theoretical</topic><topic>multidisciplinary</topic><topic>Pandemics - prevention & control</topic><topic>Pneumonia, Viral - epidemiology</topic><topic>Pneumonia, Viral - pathology</topic><topic>Pneumonia, Viral - prevention & control</topic><topic>Pneumonia, Viral - virology</topic><topic>Quarantine</topic><topic>SARS-CoV-2</topic><topic>Science</topic><topic>Science (multidisciplinary)</topic><topic>Spain - epidemiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Aguiar, Maíra</creatorcontrib><creatorcontrib>Ortuondo, Eduardo Millán</creatorcontrib><creatorcontrib>Bidaurrazaga Van-Dierdonck, Joseba</creatorcontrib><creatorcontrib>Mar, Javier</creatorcontrib><creatorcontrib>Stollenwerk, Nico</creatorcontrib><collection>Springer Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Scientific reports</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aguiar, Maíra</au><au>Ortuondo, Eduardo Millán</au><au>Bidaurrazaga Van-Dierdonck, Joseba</au><au>Mar, Javier</au><au>Stollenwerk, Nico</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modelling COVID 19 in the Basque Country from introduction to control measure response</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2020-10-14</date><risdate>2020</risdate><volume>10</volume><issue>1</issue><spage>17306</spage><epage>17306</epage><pages>17306-17306</pages><artnum>17306</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><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
λ
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.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>33057119</pmid><doi>10.1038/s41598-020-74386-1</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | 631/114 631/553 692/699 Betacoronavirus - isolation & purification Coronavirus Infections - epidemiology Coronavirus Infections - pathology Coronavirus Infections - prevention & control Coronavirus Infections - virology COVID-19 Humanities and Social Sciences Humans Models, Theoretical multidisciplinary Pandemics - prevention & control Pneumonia, Viral - epidemiology Pneumonia, Viral - pathology Pneumonia, Viral - prevention & control Pneumonia, Viral - virology Quarantine SARS-CoV-2 Science Science (multidisciplinary) Spain - epidemiology |
title | Modelling COVID 19 in the Basque Country from introduction to control measure response |
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