Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/27530
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dc.contributor.authorBERAUD, Guillaume-
dc.date.accessioned2018-12-19T13:51:59Z-
dc.date.available2018-12-19T13:51:59Z-
dc.date.issued2018-
dc.identifier.citationVACCINE, 36(36), p. 5366-5372-
dc.identifier.issn0264-410X-
dc.identifier.urihttp://hdl.handle.net/1942/27530-
dc.description.abstractInfection transmission is a complex and dynamic process, and is therefore difficult to assess. Consequently, mathematical models are a useful tool to understand any leverage on this transmission, such as vaccination. Models can provide guidance to implement an optimal vaccination campaign whether it concerns the fraction of the population or the age-group to be vaccinated. Mathematical models can also provide insights on counter-intuitive collateral effects of vaccination campaign, given the possibility that the overall benefits for the general population may hide deleterious effects on some sub-groups. As a large proportion of the population is now vaccinated, complex modelling taking into account individual and population heterogeneity and behaviour is necessary although challenging. But the most crucial aspect in the future of mathematical modelling still consists in obtaining precise and exhaustive data. (C) 2017 Elsevier Ltd. All rights reserved.-
dc.language.isoen-
dc.publisherELSEVIER SCI LTD-
dc.subject.otherMathematical models; Vaccination-
dc.subject.otherMathematical models; Vaccination-
dc.titleMathematical models and vaccination strategies-
dc.typeJournal Contribution-
local.bibliographicCitation.conferencedateAPR 09-12, 2016-
local.bibliographicCitation.conferencename26th European Congress of Clinical Microbiology and Infectious Diseases (ECCMID)-
local.bibliographicCitation.conferenceplaceAmsterdam, NETHERLANDS-
dc.identifier.epage5372-
dc.identifier.issue36-
dc.identifier.spage5366-
dc.identifier.volume36-
local.format.pages7-
local.bibliographicCitation.jcatA1-
dc.description.notes[Beraud, Guillaume] CHU Poitiers, Med Interne & Malad Infect, Poitiers, France. [Beraud, Guillaume] Univ Droit & Sante Lille 2, EA2694, Lille, France. [Beraud, Guillaume] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Hasselt, Belgium.-
local.publisher.placeOXFORD-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.identifier.doi10.1016/j.vaccine.2017.10.014-
dc.identifier.isi000444360200004-
item.validationecoom 2019-
item.contributorBERAUD, Guillaume-
item.accessRightsRestricted Access-
item.fullcitationBERAUD, Guillaume (2018) Mathematical models and vaccination strategies. In: VACCINE, 36(36), p. 5366-5372.-
item.fulltextWith Fulltext-
crisitem.journal.issn0264-410X-
crisitem.journal.eissn1873-2518-
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