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Modeling antibiotic resistance in hospitals: The impact of minimizing treatment duration

Identifieur interne : 000123 ( Pmc/Checkpoint ); précédent : 000122; suivant : 000124

Modeling antibiotic resistance in hospitals: The impact of minimizing treatment duration

Auteurs : Erika M. C. D Gata [États-Unis] ; Pierre Magal [France] ; Damien Olivier [France] ; Shigui Ruan [États-Unis] ; Glenn F. Webb [États-Unis]

Source :

RBID : PMC:2432019

Abstract

Infections caused by antibiotic-resistant pathogens are a global public health problem. Numerous individual- and population-level factors contribute to the emergence and spread of these pathogens. An individual-based model (IBM), formulated as a system of stochastically determined events, was developed to describe the complexities of the transmission dynamics of antibiotic-resistant bacteria. To simplify the interpretation and application of the model’s conclusions, a corresponding deterministic model was created, which describes the average behavior of the IBM over a large number of simulations. The integration of these two model systems provides a quantitative analysis of the emergence and spread of antibiotic-resistant bacteria, and demonstrates that early initiation of treatment and minimization of its duration mitigates antibiotic resistance epidemics in hospitals.


Url:
DOI: 10.1016/j.jtbi.2007.08.011
PubMed: 17905310
PubMed Central: 2432019


Affiliations:


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PMC:2432019

Le document en format XML

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Beth Israel Deaconess Medical Center, Harvard University, Boston, MA 02215, USA</aff>
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Department of Mathematics, Université du Havre, 76058 Le Havre, France</aff>
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Department of Computer Sciences, Université du Havre, 76058 Le Havre, France</aff>
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Department of Mathematics, University of Miami, Coral Gables, FL 33124-4250, USA</aff>
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Department of Mathematics, Vanderbilt University, Nashville, TN 37240, USA</aff>
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<corresp id="FN1">*Corresponding author. Tel.: +1 617 667 8127; fax: +1 617 667 7251. E-mail address:
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(E.M.C. D’Agata)</corresp>
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<p id="P1">Infections caused by antibiotic-resistant pathogens are a global public health problem. Numerous individual- and population-level factors contribute to the emergence and spread of these pathogens. An individual-based model (IBM), formulated as a system of stochastically determined events, was developed to describe the complexities of the transmission dynamics of antibiotic-resistant bacteria. To simplify the interpretation and application of the model’s conclusions, a corresponding deterministic model was created, which describes the average behavior of the IBM over a large number of simulations. The integration of these two model systems provides a quantitative analysis of the emergence and spread of antibiotic-resistant bacteria, and demonstrates that early initiation of treatment and minimization of its duration mitigates antibiotic resistance epidemics in hospitals.</p>
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