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Optimizing Hospital Infection Control: The Role of Mathematical Modeling

Published online by Cambridge University Press:  10 May 2016

Tan N. Doan ,
David C. M. Kong ,
Carl M. J. Kirkpatrick  and
Emma S. McBryde
Tan N. Doan
Affiliation:
Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia
David C. M. Kong
Affiliation:
Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia
Carl M. J. Kirkpatrick
Affiliation:
Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia
Emma S. McBryde*
Affiliation:
Victorian Infectious Diseases Service, Royal Melbourne Hospital, Melbourne, Victoria, Australia
*
Victorian Infectious Diseases Service, Royal Melbourne Hospital, Peter Doherty Institute for Infection and Immunity, Level 4, 792 Elizabeth Street, Melbourne, Victoria 3000, Australia (emma.mcbryde@mh.org.au); or, David C. M. Kong, PhD, Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, 381 Royal Parade, Melbourne, Victoria 3052, Australia (david.kong@monash.edu).
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Abstract

Multidrug-resistant bacteria are major causes of nosocomial infections and are associated with considerable morbidity, mortality, and healthcare costs. Preventive strategies have therefore become increasingly important. Mathematical modeling has been widely used to understand the transmission dynamics of nosocomial infections and the quantitative effects of infection control measures. This review will explore the principles of mathematical modeling used in nosocomial infections and discuss the effectiveness of infection control measures investigated using mathematical modeling.

Infect Control Hosp Epidemiol 2014;35(12):1521–1530

Type
Research Article
Information
Infection Control & Hospital Epidemiology , Volume 35 , Issue 12 , December 2014 , pp. 1521 - 1530
Copyright
© 2014 by The Society for Healthcare Epidemiology of America. All rights reserved.

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