Real-time predictive seasonal influenza model in Catalonia, Spain.
Identifieur interne : 000056 ( Main/Exploration ); précédent : 000055; suivant : 000057Real-time predictive seasonal influenza model in Catalonia, Spain.
Auteurs : Luca Basile [Espagne] ; Manuel Oviedo De La Fuente [Espagne] ; Nuria Torner [Espagne] ; Ana Martínez [Espagne] ; Mireia Jané [Espagne]Source :
- PloS one [ 1932-6203 ] ; 2018.
Descripteurs français
- KwdFr :
- MESH :
- épidémiologie : Grippe humaine.
- Espagne, Facteurs temps, Humains, Incertitude, Incidence, Modèles biologiques, Projets pilotes, Prévision, Saisons, Surveillance sentinelle.
English descriptors
- KwdEn :
- MESH :
- epidemiology : Influenza, Human.
- Forecasting, Humans, Incidence, Models, Biological, Pilot Projects, Seasons, Sentinel Surveillance, Spain, Time Factors, Uncertainty.
Abstract
Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010-2011 to 2013-2014) was created. A pilot test was conducted during the 2014-2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015-2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015-2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included.
DOI: 10.1371/journal.pone.0193651
PubMed: 29513710
PubMed Central: PMC5841785
Affiliations:
- Espagne
- Catalogne, Communauté de Madrid, Galice
- Barcelone, Madrid, Santiago de Compostela
- Université de Saint-Jacques-de-Compostelle
Links toward previous steps (curation, corpus...)
Le document en format XML
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xml:lang="fr">Espagne</country><wicri:regionArea>Public Health Agency of Catalonia, Barcelona</wicri:regionArea><placeName><settlement type="city">Barcelone</settlement><region nuts="2" type="region">Catalogne</region></placeName></affiliation><affiliation wicri:level="1"><nlm:affiliation>Department of Medicine, University of Barcelona Barcelona, Spain.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>Department of Medicine, University of Barcelona Barcelona</wicri:regionArea><wicri:noRegion>University of Barcelona Barcelona</wicri:noRegion></affiliation><affiliation wicri:level="3"><nlm:affiliation>CIBER Epidemiology and Public Health CIBERESP, Carlos III Health Institute, Madrid, Spain.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>CIBER Epidemiology and Public Health CIBERESP, Carlos III Health Institute, Madrid</wicri:regionArea><placeName><settlement type="city">Madrid</settlement><region nuts="2" type="region">Communauté de Madrid</region></placeName></affiliation></author><author><name sortKey="Martinez, Ana" sort="Martinez, Ana" uniqKey="Martinez A" first="Ana" last="Martínez">Ana Martínez</name><affiliation wicri:level="3"><nlm:affiliation>Public Health Agency of Catalonia, Barcelona, Spain.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>Public Health Agency of Catalonia, Barcelona</wicri:regionArea><placeName><settlement type="city">Barcelone</settlement><region nuts="2" type="region">Catalogne</region></placeName></affiliation><affiliation wicri:level="3"><nlm:affiliation>CIBER Epidemiology and Public Health CIBERESP, Carlos III Health Institute, Madrid, Spain.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>CIBER Epidemiology and Public Health CIBERESP, Carlos III Health Institute, Madrid</wicri:regionArea><placeName><settlement type="city">Madrid</settlement><region nuts="2" type="region">Communauté de Madrid</region></placeName></affiliation></author><author><name sortKey="Jane, Mireia" sort="Jane, Mireia" uniqKey="Jane M" first="Mireia" last="Jané">Mireia Jané</name><affiliation wicri:level="3"><nlm:affiliation>Public Health Agency of Catalonia, Barcelona, Spain.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>Public Health Agency of Catalonia, Barcelona</wicri:regionArea><placeName><settlement type="city">Barcelone</settlement><region nuts="2" type="region">Catalogne</region></placeName></affiliation><affiliation wicri:level="3"><nlm:affiliation>CIBER Epidemiology and Public Health CIBERESP, Carlos III Health Institute, Madrid, Spain.</nlm:affiliation><country xml:lang="fr">Espagne</country><wicri:regionArea>CIBER Epidemiology and Public Health CIBERESP, Carlos III Health Institute, Madrid</wicri:regionArea><placeName><settlement type="city">Madrid</settlement><region nuts="2" type="region">Communauté de Madrid</region></placeName></affiliation></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Forecasting (MeSH)</term><term>Humans (MeSH)</term><term>Incidence (MeSH)</term><term>Influenza, Human (epidemiology)</term><term>Models, Biological (MeSH)</term><term>Pilot Projects (MeSH)</term><term>Seasons (MeSH)</term><term>Sentinel Surveillance (MeSH)</term><term>Spain (MeSH)</term><term>Time Factors (MeSH)</term><term>Uncertainty (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Espagne (MeSH)</term><term>Facteurs temps (MeSH)</term><term>Grippe humaine (épidémiologie)</term><term>Humains (MeSH)</term><term>Incertitude (MeSH)</term><term>Incidence (MeSH)</term><term>Modèles biologiques (MeSH)</term><term>Projets pilotes (MeSH)</term><term>Prévision (MeSH)</term><term>Saisons (MeSH)</term><term>Surveillance sentinelle (MeSH)</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Forecasting</term><term>Humans</term><term>Incidence</term><term>Models, Biological</term><term>Pilot Projects</term><term>Seasons</term><term>Sentinel Surveillance</term><term>Spain</term><term>Time Factors</term><term>Uncertainty</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Espagne</term><term>Facteurs temps</term><term>Humains</term><term>Incertitude</term><term>Incidence</term><term>Modèles biologiques</term><term>Projets pilotes</term><term>Prévision</term><term>Saisons</term><term>Surveillance sentinelle</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010-2011 to 2013-2014) was created. A pilot test was conducted during the 2014-2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015-2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015-2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">29513710</PMID><DateCompleted><Year>2018</Year><Month>06</Month><Day>28</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>14</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>13</Volume><Issue>3</Issue><PubDate><Year>2018</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>Real-time predictive seasonal influenza model in Catalonia, Spain.</ArticleTitle><Pagination><MedlinePgn>e0193651</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0193651</ELocationID><Abstract><AbstractText>Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010-2011 to 2013-2014) was created. A pilot test was conducted during the 2014-2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015-2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015-2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Basile</LastName><ForeName>Luca</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Public Health Agency of Catalonia, Barcelona, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Oviedo de la Fuente</LastName><ForeName>Manuel</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Technological Institute for Industrial Mathematics (ITMATI), Campus Vida, Santiago de Compostela, Spain.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>MODESTYA Group, Department of Statistics, Mathematical Analysis and Optimization, University of Santiago de Compostela, Santiago de Compostela, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Torner</LastName><ForeName>Nuria</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>Public Health Agency of Catalonia, Barcelona, Spain.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Medicine, University of Barcelona Barcelona, Spain.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>CIBER Epidemiology and Public Health CIBERESP, Carlos III Health Institute, Madrid, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Martínez</LastName><ForeName>Ana</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Public Health Agency of Catalonia, Barcelona, Spain.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>CIBER Epidemiology and Public Health CIBERESP, Carlos III Health Institute, Madrid, Spain.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jané</LastName><ForeName>Mireia</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Public Health Agency of Catalonia, Barcelona, Spain.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>CIBER Epidemiology and Public Health CIBERESP, Carlos III Health Institute, Madrid, Spain.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>03</Month><Day>07</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D005544" MajorTopicYN="N">Forecasting</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015994" MajorTopicYN="N">Incidence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007251" 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IdType="pubmed">23034894</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Espagne</li></country><region><li>Catalogne</li><li>Communauté de Madrid</li><li>Galice</li></region><settlement><li>Barcelone</li><li>Madrid</li><li>Santiago de Compostela</li></settlement><orgName><li>Université de Saint-Jacques-de-Compostelle</li></orgName></list><tree><country name="Espagne"><region name="Catalogne"><name sortKey="Basile, Luca" sort="Basile, Luca" uniqKey="Basile L" first="Luca" last="Basile">Luca Basile</name></region><name sortKey="Jane, Mireia" sort="Jane, Mireia" uniqKey="Jane M" first="Mireia" last="Jané">Mireia Jané</name><name sortKey="Jane, Mireia" sort="Jane, Mireia" uniqKey="Jane M" first="Mireia" last="Jané">Mireia Jané</name><name sortKey="Martinez, Ana" sort="Martinez, Ana" uniqKey="Martinez A" first="Ana" last="Martínez">Ana Martínez</name><name sortKey="Martinez, Ana" sort="Martinez, Ana" uniqKey="Martinez A" first="Ana" last="Martínez">Ana Martínez</name><name sortKey="Oviedo De La Fuente, Manuel" sort="Oviedo De La Fuente, Manuel" uniqKey="Oviedo De La Fuente M" first="Manuel" last="Oviedo De La Fuente">Manuel Oviedo De La Fuente</name><name sortKey="Oviedo De La Fuente, Manuel" sort="Oviedo De La Fuente, Manuel" uniqKey="Oviedo De La Fuente M" first="Manuel" last="Oviedo De La Fuente">Manuel Oviedo De La Fuente</name><name sortKey="Torner, Nuria" sort="Torner, Nuria" uniqKey="Torner N" first="Nuria" last="Torner">Nuria Torner</name><name sortKey="Torner, Nuria" sort="Torner, Nuria" uniqKey="Torner N" first="Nuria" last="Torner">Nuria Torner</name><name sortKey="Torner, Nuria" sort="Torner, Nuria" uniqKey="Torner N" first="Nuria" last="Torner">Nuria Torner</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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