An exploratory method for estimating the changing speed of epidemic waves from historical data.
Identifieur interne : 000551 ( Main/Exploration ); précédent : 000550; suivant : 000552An exploratory method for estimating the changing speed of epidemic waves from historical data.
Auteurs : Andrew D. Cliff [Royaume-Uni] ; Peter Haggett ; Matthew Smallman-RaynorSource :
- International journal of epidemiology [ 1464-3685 ] ; 2008.
Descripteurs français
- KwdFr :
- Analyse de survie (MeSH), Espagne (épidémiologie), Femelle (MeSH), Grippe humaine (diagnostic), Grippe humaine (épidémiologie), Hong Kong (épidémiologie), Humains (MeSH), Islande (épidémiologie), Modèles théoriques (MeSH), Morbidité (tendances), Mâle (MeSH), Prévalence (MeSH), Royaume-Uni (épidémiologie), Sensibilité et spécificité (MeSH), Sous-type H1N1 du virus de la grippe A (MeSH), Sous-type H2N2 du virus de la grippe A (MeSH), Sous-type H3N2 du virus de la grippe A (MeSH), Surveillance sentinelle (MeSH), Valeur prédictive des tests (MeSH), Épidémies de maladies (MeSH), Études de cohortes (MeSH).
- MESH :
- diagnostic : Grippe humaine.
- tendances : Morbidité.
- épidémiologie : Espagne, Grippe humaine, Hong Kong, Islande, Royaume-Uni.
- Analyse de survie, Femelle, Humains, Modèles théoriques, Mâle, Prévalence, Sensibilité et spécificité, Sous-type H1N1 du virus de la grippe A, Sous-type H2N2 du virus de la grippe A, Sous-type H3N2 du virus de la grippe A, Surveillance sentinelle, Valeur prédictive des tests, Épidémies de maladies, Études de cohortes.
English descriptors
- KwdEn :
- Cohort Studies (MeSH), Disease Outbreaks (MeSH), Female (MeSH), Hong Kong (epidemiology), Humans (MeSH), Iceland (epidemiology), Influenza A Virus, H1N1 Subtype (MeSH), Influenza A Virus, H2N2 Subtype (MeSH), Influenza A Virus, H3N2 Subtype (MeSH), Influenza, Human (diagnosis), Influenza, Human (epidemiology), Male (MeSH), Models, Theoretical (MeSH), Morbidity (trends), Predictive Value of Tests (MeSH), Prevalence (MeSH), Sensitivity and Specificity (MeSH), Sentinel Surveillance (MeSH), Spain (epidemiology), Survival Analysis (MeSH), United Kingdom (epidemiology).
- MESH :
- diagnosis : Influenza, Human.
- epidemiology : Hong Kong, Iceland, Influenza, Human, Spain, United Kingdom.
- trends : Morbidity.
- Cohort Studies, Disease Outbreaks, Female, Humans, Influenza A Virus, H1N1 Subtype, Influenza A Virus, H2N2 Subtype, Influenza A Virus, H3N2 Subtype, Male, Models, Theoretical, Predictive Value of Tests, Prevalence, Sensitivity and Specificity, Sentinel Surveillance, Survival Analysis.
Abstract
BACKGROUND
Historical data are necessary to establish long-term trends in disease incidence but pose analytical problems since their accuracy and reliability may be poorly specified.
METHODS
A robust measure of the spatial velocity, R(0A), of epidemic waves from space-time series is proposed using binary data. The method was applied to the historical records of influenza morbidity for the island of Iceland over a 61-year period of influenza seasons from 1915-16 to 1975-76.
RESULTS
The onset of influenza waves tended to speed up over the period studied and the three pandemic waves associated with viral shifts in influenza A [Spanish influenza H1N1 (1918-19), Asian influenza H2N2 (1957-58) and Hong Kong influenza H3N2 (1968-69)] spread more rapidly around the island and struck earlier in the influenza season than did inter-pandemic waves, even when the latter were equally intensive as measured by total number of cases and case incidence.
DISCUSSION
The potential for using R(0A) in a real-time context is explored using French influenza data.
CONCLUSIONS
The new measure of wave velocity appears to be applicable to those historical time series where breakdown into regional or local areas is available. The study is being extended to (i) other countries where similar influenza time series are available and (ii) to other diseases within Iceland.
DOI: 10.1093/ije/dym240
PubMed: 18056121
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Cliff</name><affiliation wicri:level="4"><nlm:affiliation>Department of Geography, University of Cambridge, Cambridge CB2 3EN, UK.</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Department of Geography, University of Cambridge, Cambridge CB2 3EN</wicri:regionArea><orgName type="university">Université de Cambridge</orgName><placeName><settlement type="city">Cambridge</settlement><region type="country">Angleterre</region><region type="région" nuts="1">Angleterre de l'Est</region></placeName></affiliation></author><author><name sortKey="Haggett, Peter" sort="Haggett, Peter" uniqKey="Haggett P" first="Peter" last="Haggett">Peter Haggett</name></author><author><name sortKey="Smallman Raynor, Matthew" sort="Smallman Raynor, Matthew" uniqKey="Smallman Raynor M" first="Matthew" last="Smallman-Raynor">Matthew Smallman-Raynor</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2008">2008</date><idno type="RBID">pubmed:18056121</idno><idno type="pmid">18056121</idno><idno type="doi">10.1093/ije/dym240</idno><idno type="wicri:Area/Main/Corpus">00547</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">00547</idno><idno type="wicri:Area/Main/Curation">000547</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000547</idno><idno type="wicri:Area/Main/Exploration">000547</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">An exploratory method for estimating the changing speed of epidemic waves from historical data.</title><author><name sortKey="Cliff, Andrew D" sort="Cliff, Andrew D" uniqKey="Cliff A" first="Andrew D" last="Cliff">Andrew D. Cliff</name><affiliation wicri:level="4"><nlm:affiliation>Department of Geography, University of Cambridge, Cambridge CB2 3EN, UK.</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Department of Geography, University of Cambridge, Cambridge CB2 3EN</wicri:regionArea><orgName type="university">Université de Cambridge</orgName><placeName><settlement type="city">Cambridge</settlement><region type="country">Angleterre</region><region type="région" nuts="1">Angleterre de l'Est</region></placeName></affiliation></author><author><name sortKey="Haggett, Peter" sort="Haggett, Peter" uniqKey="Haggett P" first="Peter" last="Haggett">Peter Haggett</name></author><author><name sortKey="Smallman Raynor, Matthew" sort="Smallman Raynor, Matthew" uniqKey="Smallman Raynor M" first="Matthew" last="Smallman-Raynor">Matthew Smallman-Raynor</name></author></analytic><series><title level="j">International journal of epidemiology</title><idno type="eISSN">1464-3685</idno><imprint><date when="2008" type="published">2008</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Cohort Studies (MeSH)</term><term>Disease Outbreaks (MeSH)</term><term>Female (MeSH)</term><term>Hong Kong (epidemiology)</term><term>Humans (MeSH)</term><term>Iceland (epidemiology)</term><term>Influenza A Virus, H1N1 Subtype (MeSH)</term><term>Influenza A Virus, H2N2 Subtype (MeSH)</term><term>Influenza A Virus, H3N2 Subtype (MeSH)</term><term>Influenza, Human (diagnosis)</term><term>Influenza, Human (epidemiology)</term><term>Male (MeSH)</term><term>Models, Theoretical (MeSH)</term><term>Morbidity (trends)</term><term>Predictive Value of Tests (MeSH)</term><term>Prevalence (MeSH)</term><term>Sensitivity and Specificity (MeSH)</term><term>Sentinel Surveillance (MeSH)</term><term>Spain (epidemiology)</term><term>Survival Analysis (MeSH)</term><term>United Kingdom (epidemiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Analyse de survie (MeSH)</term><term>Espagne (épidémiologie)</term><term>Femelle (MeSH)</term><term>Grippe humaine (diagnostic)</term><term>Grippe humaine (épidémiologie)</term><term>Hong Kong (épidémiologie)</term><term>Humains (MeSH)</term><term>Islande (épidémiologie)</term><term>Modèles théoriques (MeSH)</term><term>Morbidité (tendances)</term><term>Mâle (MeSH)</term><term>Prévalence (MeSH)</term><term>Royaume-Uni (épidémiologie)</term><term>Sensibilité et spécificité (MeSH)</term><term>Sous-type H1N1 du virus de la grippe A (MeSH)</term><term>Sous-type H2N2 du virus de la grippe A (MeSH)</term><term>Sous-type H3N2 du virus de la grippe A (MeSH)</term><term>Surveillance sentinelle (MeSH)</term><term>Valeur prédictive des tests (MeSH)</term><term>Épidémies de maladies (MeSH)</term><term>Études de cohortes (MeSH)</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Hong Kong</term><term>Iceland</term><term>Influenza, Human</term><term>Spain</term><term>United Kingdom</term></keywords><keywords scheme="MESH" qualifier="tendances" xml:lang="fr"><term>Morbidité</term></keywords><keywords scheme="MESH" qualifier="trends" xml:lang="en"><term>Morbidity</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Espagne</term><term>Grippe humaine</term><term>Hong Kong</term><term>Islande</term><term>Royaume-Uni</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Cohort Studies</term><term>Disease Outbreaks</term><term>Female</term><term>Humans</term><term>Influenza A Virus, H1N1 Subtype</term><term>Influenza A Virus, H2N2 Subtype</term><term>Influenza A Virus, H3N2 Subtype</term><term>Male</term><term>Models, Theoretical</term><term>Predictive Value of Tests</term><term>Prevalence</term><term>Sensitivity and Specificity</term><term>Sentinel Surveillance</term><term>Survival Analysis</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse de survie</term><term>Femelle</term><term>Humains</term><term>Modèles théoriques</term><term>Mâle</term><term>Prévalence</term><term>Sensibilité et spécificité</term><term>Sous-type H1N1 du virus de la grippe A</term><term>Sous-type H2N2 du virus de la grippe A</term><term>Sous-type H3N2 du virus de la grippe A</term><term>Surveillance sentinelle</term><term>Valeur prédictive des tests</term><term>Épidémies de maladies</term><term>Études de cohortes</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>Historical data are necessary to establish long-term trends in disease incidence but pose analytical problems since their accuracy and reliability may be poorly specified.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>A robust measure of the spatial velocity, R(0A), of epidemic waves from space-time series is proposed using binary data. The method was applied to the historical records of influenza morbidity for the island of Iceland over a 61-year period of influenza seasons from 1915-16 to 1975-76.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>The onset of influenza waves tended to speed up over the period studied and the three pandemic waves associated with viral shifts in influenza A [Spanish influenza H1N1 (1918-19), Asian influenza H2N2 (1957-58) and Hong Kong influenza H3N2 (1968-69)] spread more rapidly around the island and struck earlier in the influenza season than did inter-pandemic waves, even when the latter were equally intensive as measured by total number of cases and case incidence.</p></div><div type="abstract" xml:lang="en"><p><b>DISCUSSION</b></p><p>The potential for using R(0A) in a real-time context is explored using French influenza data.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>The new measure of wave velocity appears to be applicable to those historical time series where breakdown into regional or local areas is available. The study is being extended to (i) other countries where similar influenza time series are available and (ii) to other diseases within Iceland.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">18056121</PMID><DateCompleted><Year>2008</Year><Month>03</Month><Day>27</Day></DateCompleted><DateRevised><Year>2016</Year><Month>11</Month><Day>24</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1464-3685</ISSN><JournalIssue CitedMedium="Internet"><Volume>37</Volume><Issue>1</Issue><PubDate><Year>2008</Year><Month>Feb</Month></PubDate></JournalIssue><Title>International journal of epidemiology</Title><ISOAbbreviation>Int J Epidemiol</ISOAbbreviation></Journal><ArticleTitle>An exploratory method for estimating the changing speed of epidemic waves from historical data.</ArticleTitle><Pagination><MedlinePgn>106-12</MedlinePgn></Pagination><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Historical data are necessary to establish long-term trends in disease incidence but pose analytical problems since their accuracy and reliability may be poorly specified.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">A robust measure of the spatial velocity, R(0A), of epidemic waves from space-time series is proposed using binary data. The method was applied to the historical records of influenza morbidity for the island of Iceland over a 61-year period of influenza seasons from 1915-16 to 1975-76.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">The onset of influenza waves tended to speed up over the period studied and the three pandemic waves associated with viral shifts in influenza A [Spanish influenza H1N1 (1918-19), Asian influenza H2N2 (1957-58) and Hong Kong influenza H3N2 (1968-69)] spread more rapidly around the island and struck earlier in the influenza season than did inter-pandemic waves, even when the latter were equally intensive as measured by total number of cases and case incidence.</AbstractText><AbstractText Label="DISCUSSION" NlmCategory="CONCLUSIONS">The potential for using R(0A) in a real-time context is explored using French influenza data.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">The new measure of wave velocity appears to be applicable to those historical time series where breakdown into regional or local areas is available. The study is being extended to (i) other countries where similar influenza time series are available and (ii) to other diseases within Iceland.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Cliff</LastName><ForeName>Andrew D</ForeName><Initials>AD</Initials><AffiliationInfo><Affiliation>Department of Geography, University of Cambridge, Cambridge CB2 3EN, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Haggett</LastName><ForeName>Peter</ForeName><Initials>P</Initials></Author><Author ValidYN="Y"><LastName>Smallman-Raynor</LastName><ForeName>Matthew</ForeName><Initials>M</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><Agency>Wellcome Trust</Agency><Country>United Kingdom</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D003160">Comparative Study</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research 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sortKey="Haggett, Peter" sort="Haggett, Peter" uniqKey="Haggett P" first="Peter" last="Haggett">Peter Haggett</name><name sortKey="Smallman Raynor, Matthew" sort="Smallman Raynor, Matthew" uniqKey="Smallman Raynor M" first="Matthew" last="Smallman-Raynor">Matthew Smallman-Raynor</name></noCountry><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Cliff, Andrew D" sort="Cliff, Andrew D" uniqKey="Cliff A" first="Andrew D" last="Cliff">Andrew D. 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