Defining epidemics in computer simulation models: How do definitions influence conclusions?
Identifieur interne : 000118 ( Main/Corpus ); précédent : 000117; suivant : 000119Defining epidemics in computer simulation models: How do definitions influence conclusions?
Auteurs : Carolyn Orbann ; Lisa Sattenspiel ; Erin Miller ; Jessica DimkaSource :
- Epidemics [ 1878-0067 ] ; 2017.
English descriptors
- KwdEn :
- MESH :
- epidemiology : Influenza, Human, Measles, Newfoundland and Labrador.
- statistics & numerical data : Computer Simulation, Epidemics.
- Humans.
Abstract
Computer models have proven to be useful tools in studying epidemic disease in human populations. Such models are being used by a broader base of researchers, and it has become more important to ensure that descriptions of model construction and data analyses are clear and communicate important features of model structure. Papers describing computer models of infectious disease often lack a clear description of how the data are aggregated and whether or not non-epidemic runs are excluded from analyses. Given that there is no concrete quantitative definition of what constitutes an epidemic within the public health literature, each modeler must decide on a strategy for identifying epidemics during simulation runs. Here, an SEIR model was used to test the effects of how varying the cutoff for considering a run an epidemic changes potential interpretations of simulation outcomes. Varying the cutoff from 0% to 15% of the model population ever infected with the illness generated significant differences in numbers of dead and timing variables. These results are important for those who use models to form public health policy, in which questions of timing or implementation of interventions might be answered using findings from computer simulation models.
DOI: 10.1016/j.epidem.2016.12.001
PubMed: 28027840
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pubmed:28027840Le document en format XML
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Electronic address: sattenspiell@missouri.edu.</nlm:affiliation></affiliation></author><author><name sortKey="Miller, Erin" sort="Miller, Erin" uniqKey="Miller E" first="Erin" last="Miller">Erin Miller</name><affiliation><nlm:affiliation>Department of Anthropology, University of Missouri, Columbia, MO, 65211, USA. Electronic address: elmvw9@mail.missouri.edu.</nlm:affiliation></affiliation></author><author><name sortKey="Dimka, Jessica" sort="Dimka, Jessica" uniqKey="Dimka J" first="Jessica" last="Dimka">Jessica Dimka</name><affiliation><nlm:affiliation>Department of Anthropology, University of Missouri, Columbia, MO, 65211, USA. Electronic address: DimkaJ@missouri.edu.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2017">2017</date><idno type="RBID">pubmed:28027840</idno><idno type="pmid">28027840</idno><idno type="doi">10.1016/j.epidem.2016.12.001</idno><idno type="wicri:Area/Main/Corpus">000118</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000118</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Defining epidemics in computer simulation models: How do definitions influence conclusions?</title><author><name sortKey="Orbann, Carolyn" sort="Orbann, Carolyn" uniqKey="Orbann C" first="Carolyn" last="Orbann">Carolyn Orbann</name><affiliation><nlm:affiliation>Department of Health Sciences, University of Missouri, Columbia, MO, 65211, USA. Electronic address: orbannc@health.missouri.edu.</nlm:affiliation></affiliation></author><author><name sortKey="Sattenspiel, Lisa" sort="Sattenspiel, Lisa" uniqKey="Sattenspiel L" first="Lisa" last="Sattenspiel">Lisa Sattenspiel</name><affiliation><nlm:affiliation>Department of Anthropology, University of Missouri, Columbia, MO, 65211, USA. Electronic address: sattenspiell@missouri.edu.</nlm:affiliation></affiliation></author><author><name sortKey="Miller, Erin" sort="Miller, Erin" uniqKey="Miller E" first="Erin" last="Miller">Erin Miller</name><affiliation><nlm:affiliation>Department of Anthropology, University of Missouri, Columbia, MO, 65211, USA. Electronic address: elmvw9@mail.missouri.edu.</nlm:affiliation></affiliation></author><author><name sortKey="Dimka, Jessica" sort="Dimka, Jessica" uniqKey="Dimka J" first="Jessica" last="Dimka">Jessica Dimka</name><affiliation><nlm:affiliation>Department of Anthropology, University of Missouri, Columbia, MO, 65211, USA. Electronic address: DimkaJ@missouri.edu.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Epidemics</title><idno type="eISSN">1878-0067</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Computer Simulation (statistics & numerical data)</term><term>Epidemics (statistics & numerical data)</term><term>Humans (MeSH)</term><term>Influenza, Human (epidemiology)</term><term>Measles (epidemiology)</term><term>Newfoundland and Labrador (epidemiology)</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza, Human</term><term>Measles</term><term>Newfoundland and Labrador</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Computer Simulation</term><term>Epidemics</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Computer models have proven to be useful tools in studying epidemic disease in human populations. Such models are being used by a broader base of researchers, and it has become more important to ensure that descriptions of model construction and data analyses are clear and communicate important features of model structure. Papers describing computer models of infectious disease often lack a clear description of how the data are aggregated and whether or not non-epidemic runs are excluded from analyses. Given that there is no concrete quantitative definition of what constitutes an epidemic within the public health literature, each modeler must decide on a strategy for identifying epidemics during simulation runs. Here, an SEIR model was used to test the effects of how varying the cutoff for considering a run an epidemic changes potential interpretations of simulation outcomes. Varying the cutoff from 0% to 15% of the model population ever infected with the illness generated significant differences in numbers of dead and timing variables. These results are important for those who use models to form public health policy, in which questions of timing or implementation of interventions might be answered using findings from computer simulation models.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">28027840</PMID><DateCompleted><Year>2018</Year><Month>04</Month><Day>04</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>02</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1878-0067</ISSN><JournalIssue CitedMedium="Internet"><Volume>19</Volume><PubDate><Year>2017</Year><Month>06</Month></PubDate></JournalIssue><Title>Epidemics</Title><ISOAbbreviation>Epidemics</ISOAbbreviation></Journal><ArticleTitle>Defining epidemics in computer simulation models: How do definitions influence conclusions?</ArticleTitle><Pagination><MedlinePgn>24-32</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S1755-4365(16)30062-7</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.epidem.2016.12.001</ELocationID><Abstract><AbstractText>Computer models have proven to be useful tools in studying epidemic disease in human populations. Such models are being used by a broader base of researchers, and it has become more important to ensure that descriptions of model construction and data analyses are clear and communicate important features of model structure. Papers describing computer models of infectious disease often lack a clear description of how the data are aggregated and whether or not non-epidemic runs are excluded from analyses. Given that there is no concrete quantitative definition of what constitutes an epidemic within the public health literature, each modeler must decide on a strategy for identifying epidemics during simulation runs. Here, an SEIR model was used to test the effects of how varying the cutoff for considering a run an epidemic changes potential interpretations of simulation outcomes. Varying the cutoff from 0% to 15% of the model population ever infected with the illness generated significant differences in numbers of dead and timing variables. These results are important for those who use models to form public health policy, in which questions of timing or implementation of interventions might be answered using findings from computer simulation models.</AbstractText><CopyrightInformation>Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Orbann</LastName><ForeName>Carolyn</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>Department of Health Sciences, University of Missouri, Columbia, MO, 65211, USA. Electronic address: orbannc@health.missouri.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sattenspiel</LastName><ForeName>Lisa</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Department of Anthropology, University of Missouri, Columbia, MO, 65211, USA. Electronic address: sattenspiell@missouri.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Miller</LastName><ForeName>Erin</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Department of Anthropology, University of Missouri, Columbia, MO, 65211, USA. Electronic address: elmvw9@mail.missouri.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Dimka</LastName><ForeName>Jessica</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Department of Anthropology, University of Missouri, Columbia, MO, 65211, USA. Electronic address: DimkaJ@missouri.edu.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>12</Month><Day>12</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Epidemics</MedlineTA><NlmUniqueID>101484711</NlmUniqueID><ISSNLinking>1878-0067</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D058872" MajorTopicYN="N">Epidemics</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008457" MajorTopicYN="N">Measles</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D009523" MajorTopicYN="N">Newfoundland and Labrador</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Agent-based simulation</Keyword><Keyword MajorTopicYN="Y">Epidemic criteria</Keyword><Keyword MajorTopicYN="Y">Infectious disease history</Keyword><Keyword MajorTopicYN="Y">Infectious disease modeling</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2015</Year><Month>11</Month><Day>20</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2016</Year><Month>08</Month><Day>11</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2016</Year><Month>12</Month><Day>11</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2016</Year><Month>12</Month><Day>29</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2018</Year><Month>4</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2016</Year><Month>12</Month><Day>29</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">28027840</ArticleId><ArticleId IdType="pii">S1755-4365(16)30062-7</ArticleId><ArticleId IdType="doi">10.1016/j.epidem.2016.12.001</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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