Defining epidemics in computer simulation models: How do definitions influence conclusions?
Identifieur interne : 000105 ( Main/Exploration ); précédent : 000104; suivant : 000106Defining epidemics in computer simulation models: How do definitions influence conclusions?
Auteurs : Carolyn Orbann [États-Unis] ; Lisa Sattenspiel [États-Unis] ; Erin Miller [États-Unis] ; Jessica Dimka [États-Unis]Source :
- Epidemics [ 1878-0067 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
- épidémiologie : Grippe humaine, Rougeole, Terre-Neuve-et-Labrador.
- Humains, Simulation numérique, Épidémies.
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
Affiliations:
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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. 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