A model of drug resistance with infection by health care workers
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The Ohio State University, Department of Mathematics, Columbus, OH 43210
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The Ohio State University, Mathematical Biosciences Institute, Columbus, OH 43210
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Iowa State University, Department of Mathematics, 482 Carver Hall Ames, IA 50011
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Received:
01 March 2010
Accepted:
29 June 2018
Published:
01 October 2010
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MSC :
Primary: 92C60; Secondary: 34B60, 35L65.
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Antibiotic resistant organisms (ARO) pose an increasing serious
threat in hospitals. One of the most life threatening ARO is
methicillin-resistant staphylococcus aureus (MRSA). In this paper,
we introduced a new mathematical model which focuses on the
evolution of two bacterial strains, drug-resistant and non-drug
resistant, residing within the population of patients and health
care workers in a hospital. The model predicts that as soon as drug
is administered, the average load of the non-resistant bacteria will
decrease and eventually (after 6 weeks of the model's simulation)
reach a very low level. However, the average load of drug-resistant
bacteria will initially decrease, after treatment, but will later
bounce back and remain at a high level. This level can be made lower
if larger amount of drug is given or if the contact between health
care workers and patients is reduced.
Citation: Avner Friedman, Najat Ziyadi, Khalid Boushaba. A model of drug resistance with infection by health care workers[J]. Mathematical Biosciences and Engineering, 2010, 7(4): 779-792. doi: 10.3934/mbe.2010.7.779
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Abstract
Antibiotic resistant organisms (ARO) pose an increasing serious
threat in hospitals. One of the most life threatening ARO is
methicillin-resistant staphylococcus aureus (MRSA). In this paper,
we introduced a new mathematical model which focuses on the
evolution of two bacterial strains, drug-resistant and non-drug
resistant, residing within the population of patients and health
care workers in a hospital. The model predicts that as soon as drug
is administered, the average load of the non-resistant bacteria will
decrease and eventually (after 6 weeks of the model's simulation)
reach a very low level. However, the average load of drug-resistant
bacteria will initially decrease, after treatment, but will later
bounce back and remain at a high level. This level can be made lower
if larger amount of drug is given or if the contact between health
care workers and patients is reduced.
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