Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015).
Identifieur interne : 000876 ( Ncbi/Merge ); précédent : 000875; suivant : 000877Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015).
Auteurs : Lander Willem [Belgique] ; Frederik Verelst [Belgique] ; Joke Bilcke [Belgique] ; Niel Hens [Belgique] ; Philippe Beutels [Belgique]Source :
- BMC infectious diseases [ 1471-2334 ] ; 2017.
Descripteurs français
- KwdFr :
- MESH :
- usage thérapeutique : Vaccins.
- économie : Transmission de maladie infectieuse.
- Humains, Modèles théoriques, Terminologie comme sujet, Transmission de maladie infectieuse, Épidémies.
English descriptors
- KwdEn :
- MESH :
- chemical , therapeutic use : Vaccines.
- economics : Disease Transmission, Infectious.
- prevention & control : Disease Transmission, Infectious.
- Epidemics, Humans, Models, Theoretical, Terminology as Topic.
Abstract
Individual-based models (IBMs) are useful to simulate events subject to stochasticity and/or heterogeneity, and have become well established to model the potential (re)emergence of pathogens (e.g., pandemic influenza, bioterrorism). Individual heterogeneity at the host and pathogen level is increasingly documented to influence transmission of endemic diseases and it is well understood that the final stages of elimination strategies for vaccine-preventable childhood diseases (e.g., polio, measles) are subject to stochasticity. Even so it appears IBMs for both these phenomena are not well established. We review a decade of IBM publications aiming to obtain insights in their advantages, pitfalls and rationale for use and to make recommendations facilitating knowledge transfer within and across disciplines.
DOI: 10.1186/s12879-017-2699-8
PubMed: 28893198
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nuts="2">Province d'Anvers</region></placeName></affiliation></author><author><name sortKey="Verelst, Frederik" sort="Verelst, Frederik" uniqKey="Verelst F" first="Frederik" last="Verelst">Frederik Verelst</name><affiliation wicri:level="4"><nlm:affiliation>Centre for Health Economics Research & Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.</nlm:affiliation><country xml:lang="fr">Belgique</country><wicri:regionArea>Centre for Health Economics Research & Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp</wicri:regionArea><orgName type="university">Université d'Anvers</orgName><placeName><settlement type="city">Anvers</settlement><region>Région flamande</region><region type="district" nuts="2">Province d'Anvers</region></placeName></affiliation></author><author><name sortKey="Bilcke, Joke" sort="Bilcke, Joke" uniqKey="Bilcke J" first="Joke" last="Bilcke">Joke Bilcke</name><affiliation wicri:level="4"><nlm:affiliation>Centre for Health Economics Research & Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.</nlm:affiliation><country xml:lang="fr">Belgique</country><wicri:regionArea>Centre for Health Economics Research & Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp</wicri:regionArea><orgName type="university">Université d'Anvers</orgName><placeName><settlement type="city">Anvers</settlement><region>Région flamande</region><region type="district" nuts="2">Province d'Anvers</region></placeName></affiliation></author><author><name sortKey="Hens, Niel" sort="Hens, Niel" uniqKey="Hens N" first="Niel" last="Hens">Niel Hens</name><affiliation wicri:level="4"><nlm:affiliation>Centre for Health Economics Research & Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.</nlm:affiliation><country xml:lang="fr">Belgique</country><wicri:regionArea>Centre for Health Economics Research & Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp</wicri:regionArea><orgName type="university">Université d'Anvers</orgName><placeName><settlement type="city">Anvers</settlement><region>Région flamande</region><region type="district" nuts="2">Province d'Anvers</region></placeName></affiliation></author><author><name sortKey="Beutels, Philippe" sort="Beutels, Philippe" uniqKey="Beutels P" first="Philippe" last="Beutels">Philippe Beutels</name><affiliation wicri:level="4"><nlm:affiliation>Centre for Health Economics Research & Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.</nlm:affiliation><country xml:lang="fr">Belgique</country><wicri:regionArea>Centre for Health Economics Research & Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp</wicri:regionArea><orgName type="university">Université d'Anvers</orgName><placeName><settlement type="city">Anvers</settlement><region>Région flamande</region><region type="district" nuts="2">Province d'Anvers</region></placeName></affiliation></author></analytic><series><title level="j">BMC infectious diseases</title><idno type="eISSN">1471-2334</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Disease Transmission, Infectious (economics)</term><term>Disease Transmission, Infectious (prevention & control)</term><term>Epidemics</term><term>Humans</term><term>Models, Theoretical</term><term>Terminology as Topic</term><term>Vaccines (therapeutic use)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Humains</term><term>Modèles théoriques</term><term>Terminologie comme sujet</term><term>Transmission de maladie infectieuse ()</term><term>Transmission de maladie infectieuse (économie)</term><term>Vaccins (usage thérapeutique)</term><term>Épidémies</term></keywords><keywords scheme="MESH" type="chemical" qualifier="therapeutic use" xml:lang="en"><term>Vaccines</term></keywords><keywords scheme="MESH" qualifier="economics" xml:lang="en"><term>Disease Transmission, Infectious</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Disease Transmission, Infectious</term></keywords><keywords scheme="MESH" qualifier="usage thérapeutique" xml:lang="fr"><term>Vaccins</term></keywords><keywords scheme="MESH" qualifier="économie" xml:lang="fr"><term>Transmission de maladie infectieuse</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Epidemics</term><term>Humans</term><term>Models, Theoretical</term><term>Terminology as Topic</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Humains</term><term>Modèles théoriques</term><term>Terminologie comme sujet</term><term>Transmission de maladie infectieuse</term><term>Épidémies</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Individual-based models (IBMs) are useful to simulate events subject to stochasticity and/or heterogeneity, and have become well established to model the potential (re)emergence of pathogens (e.g., pandemic influenza, bioterrorism). Individual heterogeneity at the host and pathogen level is increasingly documented to influence transmission of endemic diseases and it is well understood that the final stages of elimination strategies for vaccine-preventable childhood diseases (e.g., polio, measles) are subject to stochasticity. Even so it appears IBMs for both these phenomena are not well established. We review a decade of IBM publications aiming to obtain insights in their advantages, pitfalls and rationale for use and to make recommendations facilitating knowledge transfer within and across disciplines.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">28893198</PMID><DateCompleted><Year>2018</Year><Month>03</Month><Day>05</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>02</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1471-2334</ISSN><JournalIssue CitedMedium="Internet"><Volume>17</Volume><Issue>1</Issue><PubDate><Year>2017</Year><Month>09</Month><Day>11</Day></PubDate></JournalIssue><Title>BMC infectious diseases</Title><ISOAbbreviation>BMC Infect. Dis.</ISOAbbreviation></Journal><ArticleTitle>Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015).</ArticleTitle><Pagination><MedlinePgn>612</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s12879-017-2699-8</ELocationID><Abstract><AbstractText Label="BACKGROUND">Individual-based models (IBMs) are useful to simulate events subject to stochasticity and/or heterogeneity, and have become well established to model the potential (re)emergence of pathogens (e.g., pandemic influenza, bioterrorism). Individual heterogeneity at the host and pathogen level is increasingly documented to influence transmission of endemic diseases and it is well understood that the final stages of elimination strategies for vaccine-preventable childhood diseases (e.g., polio, measles) are subject to stochasticity. Even so it appears IBMs for both these phenomena are not well established. We review a decade of IBM publications aiming to obtain insights in their advantages, pitfalls and rationale for use and to make recommendations facilitating knowledge transfer within and across disciplines.</AbstractText><AbstractText Label="METHODS">We systematically identified publications in Web of Science and PubMed from 2006-2015 based on title/abstract/keywords screening (and full-text if necessary) to retrieve topics, modeling purposes and general specifications. We extracted detailed modeling features from papers on established vaccine-preventable childhood diseases based on full-text screening.</AbstractText><AbstractText Label="RESULTS">We identified 698 papers, which applied an IBM for infectious disease transmission, and listed these in a reference database, describing their general characteristics. The diversity of disease-topics and overall publication frequency have increased over time (38 to 115 annual publications from 2006 to 2015). The inclusion of intervention strategies (8 to 52) and economic consequences (1 to 20) are increasing, to the detriment of purely theoretical explorations. Unfortunately, terminology used to describe IBMs is inconsistent and ambiguous. We retrieved 24 studies on a vaccine-preventable childhood disease (covering 7 different diseases), with publication frequency increasing from the first such study published in 2008. IBMs have been useful to explore heterogeneous between- and within-host interactions, but combined applications are still sparse. The amount of missing information on model characteristics and study design is remarkable.</AbstractText><AbstractText Label="CONCLUSIONS">IBMs are suited to combine heterogeneous within- and between-host interactions, which offers many opportunities, especially to analyze targeted interventions for endemic infections. We advocate the exchange of (open-source) platforms and stress the need for consistent "branding". Using (existing) conventions and reporting protocols would stimulate cross-fertilization between research groups and fields, and ultimately policy making in decades to come.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Willem</LastName><ForeName>Lander</ForeName><Initials>L</Initials><Identifier Source="ORCID">0000-0002-9210-1196</Identifier><AffiliationInfo><Affiliation>Centre for Health Economics Research & Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium. lander.willem@uantwerp.be.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Verelst</LastName><ForeName>Frederik</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>Centre for Health Economics Research & Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bilcke</LastName><ForeName>Joke</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Centre for Health Economics Research & Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hens</LastName><ForeName>Niel</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>Centre for Health Economics Research & Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Interuniversity Institute for Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Beutels</LastName><ForeName>Philippe</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Centre for Health Economics Research & Modeling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D017418">Meta-Analysis</PublicationType><PublicationType UI="D000078182">Systematic Review</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2017</Year><Month>09</Month><Day>11</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>BMC Infect Dis</MedlineTA><NlmUniqueID>100968551</NlmUniqueID><ISSNLinking>1471-2334</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D014612">Vaccines</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D018562" MajorTopicYN="Y">Disease Transmission, Infectious</DescriptorName><QualifierName UI="Q000191" MajorTopicYN="N">economics</QualifierName><QualifierName UI="Q000517" MajorTopicYN="N">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D058872" MajorTopicYN="N">Epidemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008962" MajorTopicYN="Y">Models, Theoretical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009626" MajorTopicYN="N">Terminology as Topic</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014612" MajorTopicYN="N">Vaccines</DescriptorName><QualifierName UI="Q000627" MajorTopicYN="N">therapeutic use</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Agent-based</Keyword><Keyword MajorTopicYN="Y">Dynamics</Keyword><Keyword MajorTopicYN="Y">Emerging diseases</Keyword><Keyword MajorTopicYN="Y">Endemic diseases</Keyword><Keyword MajorTopicYN="Y">Individual-based</Keyword><Keyword MajorTopicYN="Y">Mathematical epidemiology</Keyword><Keyword MajorTopicYN="Y">Modeling</Keyword><Keyword MajorTopicYN="Y">Networks</Keyword><Keyword MajorTopicYN="Y">ODD protocol</Keyword><Keyword MajorTopicYN="Y">Transmission</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2016</Year><Month>11</Month><Day>25</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2017</Year><Month>08</Month><Day>22</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2017</Year><Month>9</Month><Day>13</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate 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