Modeling antibiotic resistance in hospitals: The impact of minimizing treatment duration
Identifieur interne : 000123 ( Pmc/Checkpoint ); précédent : 000122; suivant : 000124Modeling 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 :
- Journal of theoretical biology [ 0022-5193 ] ; 2007.
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:
- France, États-Unis
- Floride, Haute-Normandie, Massachusetts, Région Normandie, Tennessee
- Cambridge (Massachusetts), Le Havre
- Université Harvard, Université du Havre
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<front><div type="abstract" xml:lang="en"><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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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>
<author-notes><corresp id="FN1">*Corresponding author. Tel.: +1 617 667 8127; fax: +1 617 667 7251. E-mail address: <email>edagata@bidmc.harvard.edu</email>
(E.M.C. D’Agata)</corresp>
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<abstract><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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