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dc.contributor.authorGrundmann, Hajo
dc.contributor.authorHellriegel, B
dc.date.accessioned2007-01-05T08:42:34Z
dc.date.available2007-01-05T08:42:34Z
dc.date.issued2006-01-01
dc.identifier.citationLancet Infect Dis 2006, 6(1):39-45en
dc.identifier.issn1473-3099
dc.identifier.pmid16377533
dc.identifier.doi10.1016/S1473-3099(05)70325-X
dc.identifier.urihttp://hdl.handle.net/10029/6918
dc.description.abstractHealth-care-associated infections caused by antibiotic-resistant pathogens have become a menace in hospitals worldwide and infection control measures have lead to vastly different outcomes in different countries. During the past 6 years, a theoretical framework based on mathematical models has emerged that provides solid and testable hypotheses and opens the road to a quantitative assessment of the main obstructions that undermine current efforts to control the spread of health-care-associated infections in hospitals and communities. We aim to explain to a broader audience of professionals in health care, infection control, and health systems administration some of these models that can improve the understanding of the hidden dynamics of health-care-associated infections. We also appraise their usefulness and limitations as an innovative research and decision tool for control purposes.
dc.format.extent603324 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.titleMathematical modelling: a tool for hospital infection control.en
dc.typeArticleen
dc.format.digYES
refterms.dateFOA2018-12-18T14:46:12Z
html.description.abstractHealth-care-associated infections caused by antibiotic-resistant pathogens have become a menace in hospitals worldwide and infection control measures have lead to vastly different outcomes in different countries. During the past 6 years, a theoretical framework based on mathematical models has emerged that provides solid and testable hypotheses and opens the road to a quantitative assessment of the main obstructions that undermine current efforts to control the spread of health-care-associated infections in hospitals and communities. We aim to explain to a broader audience of professionals in health care, infection control, and health systems administration some of these models that can improve the understanding of the hidden dynamics of health-care-associated infections. We also appraise their usefulness and limitations as an innovative research and decision tool for control purposes.


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