Vaccination against 2009 pandemic H1N1 in a population dynamical model of Vancouver, Canada: timing is everything.
Identifieur interne : 000497 ( Main/Exploration ); précédent : 000496; suivant : 000498Vaccination against 2009 pandemic H1N1 in a population dynamical model of Vancouver, Canada: timing is everything.
Auteurs : Jessica M. Conway [Canada] ; Ashleigh R. Tuite ; David N. Fisman ; Nathaniel Hupert ; Rafael Meza ; Bahman Davoudi ; Krista English ; P. Van Den Driessche ; Fred Brauer ; Junling Ma ; Lauren Ancel Meyers ; Marek Smieja ; Amy Greer ; Danuta M. Skowronski ; David L. Buckeridge ; Jeffrey C. Kwong ; Jianhong Wu ; Seyed M. Moghadas ; Daniel Coombs ; Robert C. Brunham ; Babak PourbohloulSource :
- BMC public health [ 1471-2458 ] ; 2011.
Descripteurs français
- KwdFr :
- Adolescent (MeSH), Adulte (MeSH), Adulte d'âge moyen (MeSH), Colombie-Britannique (épidémiologie), Enfant (MeSH), Enfant d'âge préscolaire (MeSH), Femelle (MeSH), Grippe humaine (mortalité), Grippe humaine (prévention et contrôle), Grippe humaine (virologie), Grippe humaine (épidémiologie), Humains (MeSH), Jeune adulte (MeSH), Modèles théoriques (MeSH), Mâle (MeSH), Nourrisson (MeSH), Pandémies (MeSH), Programmes de vaccination (normes), Programmes de vaccination (organisation et administration), Sous-type H1N1 du virus de la grippe A (effets des médicaments et des substances chimiques), Sous-type H1N1 du virus de la grippe A (isolement et purification), Sujet âgé (MeSH), Surveillance de la population (MeSH), Vaccins antigrippaux (usage thérapeutique).
- MESH :
- effets des médicaments et des substances chimiques : Sous-type H1N1 du virus de la grippe A.
- isolement et purification : Sous-type H1N1 du virus de la grippe A.
- mortalité : Grippe humaine.
- normes : Programmes de vaccination.
- organisation et administration : Programmes de vaccination.
- prévention et contrôle : Grippe humaine.
- usage thérapeutique : Vaccins antigrippaux.
- virologie : Grippe humaine.
- épidémiologie : Colombie-Britannique, Grippe humaine.
- Adolescent, Adulte, Adulte d'âge moyen, Enfant, Enfant d'âge préscolaire, Femelle, Humains, Jeune adulte, Modèles théoriques, Mâle, Nourrisson, Pandémies, Sujet âgé, Surveillance de la population.
English descriptors
- KwdEn :
- Adolescent (MeSH), Adult (MeSH), Aged (MeSH), British Columbia (epidemiology), Child (MeSH), Child, Preschool (MeSH), Female (MeSH), Humans (MeSH), Immunization Programs (organization & administration), Immunization Programs (standards), Infant (MeSH), Influenza A Virus, H1N1 Subtype (drug effects), Influenza A Virus, H1N1 Subtype (isolation & purification), Influenza Vaccines (therapeutic use), Influenza, Human (epidemiology), Influenza, Human (mortality), Influenza, Human (prevention & control), Influenza, Human (virology), Male (MeSH), Middle Aged (MeSH), Models, Theoretical (MeSH), Outcome Assessment, Health Care (MeSH), Pandemics (MeSH), Population Surveillance (MeSH), Young Adult (MeSH).
- MESH :
- chemical , therapeutic use : Influenza Vaccines.
- geographic , epidemiology : British Columbia.
- drug effects : Influenza A Virus, H1N1 Subtype.
- epidemiology : Influenza, Human.
- isolation & purification : Influenza A Virus, H1N1 Subtype.
- mortality : Influenza, Human.
- organization & administration : Immunization Programs.
- prevention & control : Influenza, Human.
- standards : Immunization Programs.
- virology : Influenza, Human.
- Adolescent, Adult, Aged, Child, Child, Preschool, Female, Humans, Infant, Male, Middle Aged, Models, Theoretical, Outcome Assessment, Health Care, Pandemics, Population Surveillance, Young Adult.
Abstract
BACKGROUND
Much remains unknown about the effect of timing and prioritization of vaccination against pandemic (pH1N1) 2009 virus on health outcomes. We adapted a city-level contact network model to study different campaigns on influenza morbidity and mortality.
METHODS
We modeled different distribution strategies initiated between July and November 2009 using a compartmental epidemic model that includes age structure and transmission network dynamics. The model represents the Greater Vancouver Regional District, a major North American city and surrounding suburbs with a population of 2 million, and is parameterized using data from the British Columbia Ministry of Health, published studies, and expert opinion. Outcomes are expressed as the number of infections and deaths averted due to vaccination.
RESULTS
The model output was consistent with provincial surveillance data. Assuming a basic reproduction number = 1.4, an 8-week vaccination campaign initiated 2 weeks before the epidemic onset reduced morbidity and mortality by 79-91% and 80-87%, respectively, compared to no vaccination. Prioritizing children and parents for vaccination may have reduced transmission compared to actual practice, but the mortality benefit of this strategy appears highly sensitive to campaign timing. Modeling the actual late October start date resulted in modest reductions in morbidity and mortality (13-25% and 16-20%, respectively) with little variation by prioritization scheme.
CONCLUSION
Delays in vaccine production due to technological or logistical barriers may reduce potential benefits of vaccination for pandemic influenza, and these temporal effects can outweigh any additional theoretical benefits from population targeting. Careful modeling may provide decision makers with estimates of these effects before the epidemic peak to guide production goals and inform policy. Integration of real-time surveillance data with mathematical models holds the promise of enabling public health planners to optimize the community benefits from proposed interventions before the pandemic peak.
DOI: 10.1186/1471-2458-11-932
PubMed: 22168242
PubMed Central: PMC3280345
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Conway</name><affiliation wicri:level="1"><nlm:affiliation>Division of Mathematical Modeling, University of British Columbia Centre for Disease Control, 655 West 12th Avenue, V5Z 4R4 Vancouver, British Columbia, Canada.</nlm:affiliation><country xml:lang="fr">Canada</country><wicri:regionArea>Division of Mathematical Modeling, University of British Columbia Centre for Disease Control, 655 West 12th Avenue, V5Z 4R4 Vancouver, British Columbia</wicri:regionArea><wicri:noRegion>British Columbia</wicri:noRegion></affiliation></author><author><name sortKey="Tuite, Ashleigh R" sort="Tuite, Ashleigh R" uniqKey="Tuite A" first="Ashleigh R" last="Tuite">Ashleigh R. Tuite</name></author><author><name sortKey="Fisman, David N" sort="Fisman, David N" uniqKey="Fisman D" first="David N" last="Fisman">David N. Fisman</name></author><author><name sortKey="Hupert, Nathaniel" sort="Hupert, Nathaniel" uniqKey="Hupert N" first="Nathaniel" last="Hupert">Nathaniel Hupert</name></author><author><name sortKey="Meza, Rafael" sort="Meza, Rafael" uniqKey="Meza R" first="Rafael" last="Meza">Rafael Meza</name></author><author><name sortKey="Davoudi, Bahman" sort="Davoudi, Bahman" uniqKey="Davoudi B" first="Bahman" last="Davoudi">Bahman Davoudi</name></author><author><name sortKey="English, Krista" sort="English, Krista" uniqKey="English K" first="Krista" last="English">Krista English</name></author><author><name sortKey="Van Den Driessche, P" sort="Van Den Driessche, P" uniqKey="Van Den Driessche P" first="P" last="Van Den Driessche">P. Van Den Driessche</name></author><author><name sortKey="Brauer, Fred" sort="Brauer, Fred" uniqKey="Brauer F" first="Fred" last="Brauer">Fred Brauer</name></author><author><name sortKey="Ma, Junling" sort="Ma, Junling" uniqKey="Ma J" first="Junling" last="Ma">Junling Ma</name></author><author><name sortKey="Meyers, Lauren Ancel" sort="Meyers, Lauren Ancel" uniqKey="Meyers L" first="Lauren Ancel" last="Meyers">Lauren Ancel Meyers</name></author><author><name sortKey="Smieja, Marek" sort="Smieja, Marek" uniqKey="Smieja M" first="Marek" last="Smieja">Marek Smieja</name></author><author><name sortKey="Greer, Amy" sort="Greer, Amy" uniqKey="Greer A" first="Amy" last="Greer">Amy Greer</name></author><author><name sortKey="Skowronski, Danuta M" sort="Skowronski, Danuta M" uniqKey="Skowronski D" first="Danuta M" last="Skowronski">Danuta M. Skowronski</name></author><author><name sortKey="Buckeridge, David L" sort="Buckeridge, David L" uniqKey="Buckeridge D" first="David L" last="Buckeridge">David L. Buckeridge</name></author><author><name sortKey="Kwong, Jeffrey C" sort="Kwong, Jeffrey C" uniqKey="Kwong J" first="Jeffrey C" last="Kwong">Jeffrey C. Kwong</name></author><author><name sortKey="Wu, Jianhong" sort="Wu, Jianhong" uniqKey="Wu J" first="Jianhong" last="Wu">Jianhong Wu</name></author><author><name sortKey="Moghadas, Seyed M" sort="Moghadas, Seyed M" uniqKey="Moghadas S" first="Seyed M" last="Moghadas">Seyed M. Moghadas</name></author><author><name sortKey="Coombs, Daniel" sort="Coombs, Daniel" uniqKey="Coombs D" first="Daniel" last="Coombs">Daniel Coombs</name></author><author><name sortKey="Brunham, Robert C" sort="Brunham, Robert C" uniqKey="Brunham R" first="Robert C" last="Brunham">Robert C. Brunham</name></author><author><name sortKey="Pourbohloul, Babak" sort="Pourbohloul, Babak" uniqKey="Pourbohloul B" first="Babak" last="Pourbohloul">Babak Pourbohloul</name></author></analytic><series><title level="j">BMC public health</title><idno type="eISSN">1471-2458</idno><imprint><date when="2011" type="published">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent (MeSH)</term><term>Adult (MeSH)</term><term>Aged (MeSH)</term><term>British Columbia (epidemiology)</term><term>Child (MeSH)</term><term>Child, Preschool (MeSH)</term><term>Female (MeSH)</term><term>Humans (MeSH)</term><term>Immunization Programs (organization & administration)</term><term>Immunization Programs (standards)</term><term>Infant (MeSH)</term><term>Influenza A Virus, H1N1 Subtype (drug effects)</term><term>Influenza A Virus, H1N1 Subtype (isolation & purification)</term><term>Influenza Vaccines (therapeutic use)</term><term>Influenza, Human (epidemiology)</term><term>Influenza, Human (mortality)</term><term>Influenza, Human (prevention & control)</term><term>Influenza, Human (virology)</term><term>Male (MeSH)</term><term>Middle Aged (MeSH)</term><term>Models, Theoretical (MeSH)</term><term>Outcome Assessment, Health Care (MeSH)</term><term>Pandemics (MeSH)</term><term>Population Surveillance (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>Colombie-Britannique (épidémiologie)</term><term>Enfant (MeSH)</term><term>Enfant d'âge préscolaire (MeSH)</term><term>Femelle (MeSH)</term><term>Grippe humaine (mortalité)</term><term>Grippe humaine (prévention et contrôle)</term><term>Grippe humaine (virologie)</term><term>Grippe humaine (épidémiologie)</term><term>Humains (MeSH)</term><term>Jeune adulte (MeSH)</term><term>Modèles théoriques (MeSH)</term><term>Mâle (MeSH)</term><term>Nourrisson (MeSH)</term><term>Pandémies (MeSH)</term><term>Programmes de vaccination (normes)</term><term>Programmes de vaccination (organisation et administration)</term><term>Sous-type H1N1 du virus de la grippe A (effets des médicaments et des substances chimiques)</term><term>Sous-type H1N1 du virus de la grippe A (isolement et purification)</term><term>Sujet âgé (MeSH)</term><term>Surveillance de la population (MeSH)</term><term>Vaccins antigrippaux (usage thérapeutique)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="therapeutic use" xml:lang="en"><term>Influenza Vaccines</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>British Columbia</term></keywords><keywords scheme="MESH" qualifier="drug effects" xml:lang="en"><term>Influenza A Virus, H1N1 Subtype</term></keywords><keywords scheme="MESH" qualifier="effets des médicaments et des substances chimiques" xml:lang="fr"><term>Sous-type H1N1 du virus de la grippe A</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="isolation & purification" xml:lang="en"><term>Influenza A Virus, H1N1 Subtype</term></keywords><keywords scheme="MESH" qualifier="isolement et purification" xml:lang="fr"><term>Sous-type H1N1 du virus de la grippe A</term></keywords><keywords scheme="MESH" qualifier="mortality" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="mortalité" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" qualifier="normes" xml:lang="fr"><term>Programmes de vaccination</term></keywords><keywords scheme="MESH" qualifier="organisation et administration" xml:lang="fr"><term>Programmes de vaccination</term></keywords><keywords scheme="MESH" qualifier="organization & administration" xml:lang="en"><term>Immunization Programs</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="prévention et contrôle" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" qualifier="standards" xml:lang="en"><term>Immunization Programs</term></keywords><keywords scheme="MESH" qualifier="usage thérapeutique" xml:lang="fr"><term>Vaccins antigrippaux</term></keywords><keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Grippe humaine</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Colombie-Britannique</term><term>Grippe humaine</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Aged</term><term>Child</term><term>Child, Preschool</term><term>Female</term><term>Humans</term><term>Infant</term><term>Male</term><term>Middle Aged</term><term>Models, Theoretical</term><term>Outcome Assessment, Health Care</term><term>Pandemics</term><term>Population Surveillance</term><term>Young Adult</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Enfant</term><term>Enfant d'âge préscolaire</term><term>Femelle</term><term>Humains</term><term>Jeune adulte</term><term>Modèles théoriques</term><term>Mâle</term><term>Nourrisson</term><term>Pandémies</term><term>Sujet âgé</term><term>Surveillance de la population</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>Much remains unknown about the effect of timing and prioritization of vaccination against pandemic (pH1N1) 2009 virus on health outcomes. We adapted a city-level contact network model to study different campaigns on influenza morbidity and mortality.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>We modeled different distribution strategies initiated between July and November 2009 using a compartmental epidemic model that includes age structure and transmission network dynamics. The model represents the Greater Vancouver Regional District, a major North American city and surrounding suburbs with a population of 2 million, and is parameterized using data from the British Columbia Ministry of Health, published studies, and expert opinion. Outcomes are expressed as the number of infections and deaths averted due to vaccination.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>The model output was consistent with provincial surveillance data. Assuming a basic reproduction number = 1.4, an 8-week vaccination campaign initiated 2 weeks before the epidemic onset reduced morbidity and mortality by 79-91% and 80-87%, respectively, compared to no vaccination. Prioritizing children and parents for vaccination may have reduced transmission compared to actual practice, but the mortality benefit of this strategy appears highly sensitive to campaign timing. Modeling the actual late October start date resulted in modest reductions in morbidity and mortality (13-25% and 16-20%, respectively) with little variation by prioritization scheme.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSION</b></p><p>Delays in vaccine production due to technological or logistical barriers may reduce potential benefits of vaccination for pandemic influenza, and these temporal effects can outweigh any additional theoretical benefits from population targeting. Careful modeling may provide decision makers with estimates of these effects before the epidemic peak to guide production goals and inform policy. Integration of real-time surveillance data with mathematical models holds the promise of enabling public health planners to optimize the community benefits from proposed interventions before the pandemic peak.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">22168242</PMID><DateCompleted><Year>2012</Year><Month>05</Month><Day>31</Day></DateCompleted><DateRevised><Year>2019</Year><Month>12</Month><Day>10</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2458</ISSN><JournalIssue CitedMedium="Internet"><Volume>11</Volume><PubDate><Year>2011</Year><Month>Dec</Month><Day>14</Day></PubDate></JournalIssue><Title>BMC public health</Title><ISOAbbreviation>BMC Public Health</ISOAbbreviation></Journal><ArticleTitle>Vaccination against 2009 pandemic H1N1 in a population dynamical model of Vancouver, Canada: timing is everything.</ArticleTitle><Pagination><MedlinePgn>932</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/1471-2458-11-932</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">Much remains unknown about the effect of timing and prioritization of vaccination against pandemic (pH1N1) 2009 virus on health outcomes. We adapted a city-level contact network model to study different campaigns on influenza morbidity and mortality.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">We modeled different distribution strategies initiated between July and November 2009 using a compartmental epidemic model that includes age structure and transmission network dynamics. The model represents the Greater Vancouver Regional District, a major North American city and surrounding suburbs with a population of 2 million, and is parameterized using data from the British Columbia Ministry of Health, published studies, and expert opinion. Outcomes are expressed as the number of infections and deaths averted due to vaccination.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">The model output was consistent with provincial surveillance data. Assuming a basic reproduction number = 1.4, an 8-week vaccination campaign initiated 2 weeks before the epidemic onset reduced morbidity and mortality by 79-91% and 80-87%, respectively, compared to no vaccination. Prioritizing children and parents for vaccination may have reduced transmission compared to actual practice, but the mortality benefit of this strategy appears highly sensitive to campaign timing. Modeling the actual late October start date resulted in modest reductions in morbidity and mortality (13-25% and 16-20%, respectively) with little variation by prioritization scheme.</AbstractText><AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">Delays in vaccine production due to technological or logistical barriers may reduce potential benefits of vaccination for pandemic influenza, and these temporal effects can outweigh any additional theoretical benefits from population targeting. Careful modeling may provide decision makers with estimates of these effects before the epidemic peak to guide production goals and inform policy. Integration of real-time surveillance data with mathematical models holds the promise of enabling public health planners to optimize the community benefits from proposed interventions before the pandemic peak.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Conway</LastName><ForeName>Jessica M</ForeName><Initials>JM</Initials><AffiliationInfo><Affiliation>Division of Mathematical Modeling, University of British Columbia Centre for Disease Control, 655 West 12th Avenue, V5Z 4R4 Vancouver, British Columbia, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tuite</LastName><ForeName>Ashleigh R</ForeName><Initials>AR</Initials></Author><Author ValidYN="Y"><LastName>Fisman</LastName><ForeName>David N</ForeName><Initials>DN</Initials></Author><Author ValidYN="Y"><LastName>Hupert</LastName><ForeName>Nathaniel</ForeName><Initials>N</Initials></Author><Author ValidYN="Y"><LastName>Meza</LastName><ForeName>Rafael</ForeName><Initials>R</Initials></Author><Author ValidYN="Y"><LastName>Davoudi</LastName><ForeName>Bahman</ForeName><Initials>B</Initials></Author><Author ValidYN="Y"><LastName>English</LastName><ForeName>Krista</ForeName><Initials>K</Initials></Author><Author ValidYN="Y"><LastName>van den Driessche</LastName><ForeName>P</ForeName><Initials>P</Initials></Author><Author ValidYN="Y"><LastName>Brauer</LastName><ForeName>Fred</ForeName><Initials>F</Initials></Author><Author ValidYN="Y"><LastName>Ma</LastName><ForeName>Junling</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Meyers</LastName><ForeName>Lauren Ancel</ForeName><Initials>LA</Initials></Author><Author ValidYN="Y"><LastName>Smieja</LastName><ForeName>Marek</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Greer</LastName><ForeName>Amy</ForeName><Initials>A</Initials></Author><Author ValidYN="Y"><LastName>Skowronski</LastName><ForeName>Danuta M</ForeName><Initials>DM</Initials></Author><Author ValidYN="Y"><LastName>Buckeridge</LastName><ForeName>David L</ForeName><Initials>DL</Initials></Author><Author ValidYN="Y"><LastName>Kwong</LastName><ForeName>Jeffrey C</ForeName><Initials>JC</Initials></Author><Author ValidYN="Y"><LastName>Wu</LastName><ForeName>Jianhong</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Moghadas</LastName><ForeName>Seyed M</ForeName><Initials>SM</Initials></Author><Author ValidYN="Y"><LastName>Coombs</LastName><ForeName>Daniel</ForeName><Initials>D</Initials></Author><Author ValidYN="Y"><LastName>Brunham</LastName><ForeName>Robert C</ForeName><Initials>RC</Initials></Author><Author ValidYN="Y"><LastName>Pourbohloul</LastName><ForeName>Babak</ForeName><Initials>B</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>U01 GM087719</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH 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