Finding optimal vaccination strategies for pandemic influenza using genetic algorithms.
Identifieur interne : 000323 ( PubMed/Corpus ); précédent : 000322; suivant : 000324Finding optimal vaccination strategies for pandemic influenza using genetic algorithms.
Auteurs : Rajan Patel ; Ira M. Longini ; M Elizabeth HalloranSource :
- Journal of theoretical biology [ 0022-5193 ] ; 2005.
English descriptors
- KwdEn :
- Adolescent, Adult, Age Distribution, Aged, Algorithms, Child, Child, Preschool, Disease Outbreaks, Health Care Rationing (methods), Humans, Infant, Infant, Newborn, Influenza A virus, Influenza Vaccines (administration & dosage), Influenza, Human (epidemiology), Influenza, Human (prevention & control), Influenza, Human (transmission), Mass Vaccination (organization & administration), Middle Aged, Models, Biological, Stochastic Processes.
- MESH :
- chemical , administration & dosage : Influenza Vaccines.
- epidemiology : Influenza, Human.
- methods : Health Care Rationing.
- organization & administration : Mass Vaccination.
- prevention & control : Influenza, Human.
- transmission : Influenza, Human.
- Adolescent, Adult, Age Distribution, Aged, Algorithms, Child, Child, Preschool, Disease Outbreaks, Humans, Infant, Infant, Newborn, Influenza A virus, Middle Aged, Models, Biological, Stochastic Processes.
Abstract
In the event of pandemic influenza, only limited supplies of vaccine may be available. We use stochastic epidemic simulations, genetic algorithms (GA), and random mutation hill climbing (RMHC) to find optimal vaccine distributions to minimize the number of illnesses or deaths in the population, given limited quantities of vaccine. Due to the non-linearity, complexity and stochasticity of the epidemic process, it is not possible to solve for optimal vaccine distributions mathematically. However, we use GA and RMHC to find near optimal vaccine distributions. We model an influenza pandemic that has age-specific illness attack rates similar to the Asian pandemic in 1957-1958 caused by influenza A(H2N2), as well as a distribution similar to the Hong Kong pandemic in 1968-1969 caused by influenza A(H3N2). We find the optimal vaccine distributions given that the number of doses is limited over the range of 10-90% of the population. While GA and RMHC work well in finding optimal vaccine distributions, GA is significantly more efficient than RMHC. We show that the optimal vaccine distribution found by GA and RMHC is up to 84% more effective than random mass vaccination in the mid range of vaccine availability. GA is generalizable to the optimization of stochastic model parameters for other infectious diseases and population structures.
DOI: 10.1016/j.jtbi.2004.11.032
PubMed: 15757679
Links to Exploration step
pubmed:15757679Le document en format XML
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Longini</name></author><author><name sortKey="Halloran, M Elizabeth" sort="Halloran, M Elizabeth" uniqKey="Halloran M" first="M Elizabeth" last="Halloran">M Elizabeth Halloran</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2005">2005</date><idno type="RBID">pubmed:15757679</idno><idno type="pmid">15757679</idno><idno type="doi">10.1016/j.jtbi.2004.11.032</idno><idno type="wicri:Area/PubMed/Corpus">000323</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000323</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Finding optimal vaccination strategies for pandemic influenza using genetic algorithms.</title><author><name sortKey="Patel, Rajan" sort="Patel, Rajan" uniqKey="Patel R" first="Rajan" last="Patel">Rajan Patel</name><affiliation><nlm:affiliation>Department of Biostatistics, The Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA 30322, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Longini, Ira M" sort="Longini, Ira M" uniqKey="Longini I" first="Ira M" last="Longini">Ira M. Longini</name></author><author><name sortKey="Halloran, M Elizabeth" sort="Halloran, M Elizabeth" uniqKey="Halloran M" first="M Elizabeth" last="Halloran">M Elizabeth Halloran</name></author></analytic><series><title level="j">Journal of theoretical biology</title><idno type="ISSN">0022-5193</idno><imprint><date when="2005" type="published">2005</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Age Distribution</term><term>Aged</term><term>Algorithms</term><term>Child</term><term>Child, Preschool</term><term>Disease Outbreaks</term><term>Health Care Rationing (methods)</term><term>Humans</term><term>Infant</term><term>Infant, Newborn</term><term>Influenza A virus</term><term>Influenza Vaccines (administration & dosage)</term><term>Influenza, Human (epidemiology)</term><term>Influenza, Human (prevention & control)</term><term>Influenza, Human (transmission)</term><term>Mass Vaccination (organization & administration)</term><term>Middle Aged</term><term>Models, Biological</term><term>Stochastic Processes</term></keywords><keywords scheme="MESH" type="chemical" qualifier="administration & dosage" xml:lang="en"><term>Influenza Vaccines</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Health Care Rationing</term></keywords><keywords scheme="MESH" qualifier="organization & administration" xml:lang="en"><term>Mass Vaccination</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Age Distribution</term><term>Aged</term><term>Algorithms</term><term>Child</term><term>Child, Preschool</term><term>Disease Outbreaks</term><term>Humans</term><term>Infant</term><term>Infant, Newborn</term><term>Influenza A virus</term><term>Middle Aged</term><term>Models, Biological</term><term>Stochastic Processes</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In the event of pandemic influenza, only limited supplies of vaccine may be available. We use stochastic epidemic simulations, genetic algorithms (GA), and random mutation hill climbing (RMHC) to find optimal vaccine distributions to minimize the number of illnesses or deaths in the population, given limited quantities of vaccine. Due to the non-linearity, complexity and stochasticity of the epidemic process, it is not possible to solve for optimal vaccine distributions mathematically. However, we use GA and RMHC to find near optimal vaccine distributions. We model an influenza pandemic that has age-specific illness attack rates similar to the Asian pandemic in 1957-1958 caused by influenza A(H2N2), as well as a distribution similar to the Hong Kong pandemic in 1968-1969 caused by influenza A(H3N2). We find the optimal vaccine distributions given that the number of doses is limited over the range of 10-90% of the population. While GA and RMHC work well in finding optimal vaccine distributions, GA is significantly more efficient than RMHC. We show that the optimal vaccine distribution found by GA and RMHC is up to 84% more effective than random mass vaccination in the mid range of vaccine availability. GA is generalizable to the optimization of stochastic model parameters for other infectious diseases and population structures.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">15757679</PMID><DateCompleted><Year>2005</Year><Month>04</Month><Day>19</Day></DateCompleted><DateRevised><Year>2009</Year><Month>11</Month><Day>19</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">0022-5193</ISSN><JournalIssue CitedMedium="Print"><Volume>234</Volume><Issue>2</Issue><PubDate><Year>2005</Year><Month>May</Month><Day>21</Day></PubDate></JournalIssue><Title>Journal of theoretical biology</Title><ISOAbbreviation>J. Theor. Biol.</ISOAbbreviation></Journal><ArticleTitle>Finding optimal vaccination strategies for pandemic influenza using genetic algorithms.</ArticleTitle><Pagination><MedlinePgn>201-12</MedlinePgn></Pagination><Abstract><AbstractText>In the event of pandemic influenza, only limited supplies of vaccine may be available. We use stochastic epidemic simulations, genetic algorithms (GA), and random mutation hill climbing (RMHC) to find optimal vaccine distributions to minimize the number of illnesses or deaths in the population, given limited quantities of vaccine. Due to the non-linearity, complexity and stochasticity of the epidemic process, it is not possible to solve for optimal vaccine distributions mathematically. However, we use GA and RMHC to find near optimal vaccine distributions. We model an influenza pandemic that has age-specific illness attack rates similar to the Asian pandemic in 1957-1958 caused by influenza A(H2N2), as well as a distribution similar to the Hong Kong pandemic in 1968-1969 caused by influenza A(H3N2). We find the optimal vaccine distributions given that the number of doses is limited over the range of 10-90% of the population. While GA and RMHC work well in finding optimal vaccine distributions, GA is significantly more efficient than RMHC. We show that the optimal vaccine distribution found by GA and RMHC is up to 84% more effective than random mass vaccination in the mid range of vaccine availability. GA is generalizable to the optimization of stochastic model parameters for other infectious diseases and population structures.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Patel</LastName><ForeName>Rajan</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Department of Biostatistics, The Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA 30322, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Longini</LastName><ForeName>Ira M</ForeName><Initials>IM</Initials><Suffix>Jr</Suffix></Author><Author ValidYN="Y"><LastName>Halloran</LastName><ForeName>M Elizabeth</ForeName><Initials>ME</Initials></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>AI32042</GrantID><Acronym>AI</Acronym><Agency>NIAID NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013487">Research Support, U.S. Gov't, P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2005</Year><Month>01</Month><Day>20</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>J Theor Biol</MedlineTA><NlmUniqueID>0376342</NlmUniqueID><ISSNLinking>0022-5193</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D007252">Influenza Vaccines</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000293" MajorTopicYN="N">Adolescent</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017677" MajorTopicYN="N">Age Distribution</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" 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