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dc.contributor.authorKristinsson, KG
dc.date.accessioned2008-07-29T11:32:21Z
dc.date.available2008-07-29T11:32:21Z
dc.date.issued2001-09-15
dc.date.submitted2008-07-29
dc.identifier.citationClin. Infect. Dis. 2001, 33 Suppl 3:S174-9en
dc.identifier.issn1058-4838
dc.identifier.pmid11524716
dc.identifier.doi10.1086/321845
dc.identifier.urihttp://hdl.handle.net/2336/33573
dc.descriptionTo access full text version of this article. Please click on the hyperlink "View/Open" at the bottom of this pageen
dc.description.abstractPossible interventions to minimize resistance rates are numerous and can involve reduction and/or change in antimicrobial use, infection control, and vaccinations. As mathematical models are becoming more realistic they can be useful to quantitatively evaluate the relative contribution of individual risk factors and for the planning of future intervention strategies. The fitness cost associated with resistance is an important parameter and small differences can have a profound effect on the results. The mathematical models presented for communities predicted that even with cessation of antibiotic use, the decline in resistance frequency would be slow. This contrasts with successful interventions in Finland and Iceland. Future models have to include important variables such as herd immunity and take into account the heterogeneity of open communities. Provision of susceptible strains from areas with low resistance rates to areas with high resistance rates can have a profound effect on the success of interventions to minimize resistance.
dc.language.isoenen
dc.publisherThe University of Chicago Pressen
dc.relation.urlhttp://www.journals.uchicago.edu/doi/abs/10.1086/321845en
dc.subject.meshAnti-Bacterial Agentsen
dc.subject.meshDrug Resistance, Bacterialen
dc.subject.meshHospitalsen
dc.subject.meshHumansen
dc.subject.meshMathematical Computingen
dc.subject.meshModels, Biologicalen
dc.subject.meshResidence Characteristicsen
dc.titleMathematical models as tools for evaluating the effectiveness of interventions: a comment on Levin.en
dc.typeArticleen
dc.contributor.departmentDepartment of Microbiology, National University Hospital, Reykjavik, Iceland. karl@rsp.isen
dc.identifier.journalClinical infectious diseases : an official publication of the Infectious Diseases Society of Americaen
refterms.dateFOA2018-09-12T13:55:42Z
html.description.abstractPossible interventions to minimize resistance rates are numerous and can involve reduction and/or change in antimicrobial use, infection control, and vaccinations. As mathematical models are becoming more realistic they can be useful to quantitatively evaluate the relative contribution of individual risk factors and for the planning of future intervention strategies. The fitness cost associated with resistance is an important parameter and small differences can have a profound effect on the results. The mathematical models presented for communities predicted that even with cessation of antibiotic use, the decline in resistance frequency would be slow. This contrasts with successful interventions in Finland and Iceland. Future models have to include important variables such as herd immunity and take into account the heterogeneity of open communities. Provision of susceptible strains from areas with low resistance rates to areas with high resistance rates can have a profound effect on the success of interventions to minimize resistance.


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