» Articles » PMID: 28373644

Modeling Nosocomial Infections of Methicillin-Resistant Staphylococcus Aureus with Environment Contamination<sup/>

Overview
Journal Sci Rep
Specialty Science
Date 2017 Apr 5
PMID 28373644
Citations 16
Authors
Affiliations
Soon will be listed here.
Abstract

In this work, we investigate the role of environmental contamination on the clinical epidemiology of antibiotic-resistant bacteria in hospitals. Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterium that causes infections in different parts of the body. It is tougher to treat than most strains of Staphylococcus aureus or staph, because it is resistant to some commonly used antibiotics. Both deterministic and stochastic models are constructed to describe the transmission characteristics of MRSA in hospital setting. The deterministic epidemic model includes five compartments: colonized and uncolonized patients, contaminated and uncontaminated health care workers (HCWs), and bacterial load in environment. The basic reproduction number R is calculated, and its numerical and sensitivity analysis has been performed to study the asymptotic behavior of the model, and to help identify factors responsible for observed patterns of infections. A stochastic epidemic model with stochastic simulations is also presented to supply a comprehensive analysis of its behavior. Data collected from Beijing Tongren Hospital will be used in the numerical simulations of our model. The results can be used to provide theoretical guidance for designing efficient control measures, such as increasing the hand hygiene compliance of HCWs and disinfection rate of environment, and decreasing the transmission rate between environment and patients and HCWs.

Citing Articles

Molecular characterization of Methicillin-resistant Staphylococcus aureus isolated in ready-to-eat food sold in supermarkets in Bobo-Dioulasso: case of charcuterie products.

Somda N, Traore A, Hien D, Bockarie Y, Tankoano A, Kabore D BMC Infect Dis. 2024; 24(1):722.

PMID: 39044137 PMC: 11264425. DOI: 10.1186/s12879-024-09603-7.


Examining the impact of ICU population interaction structure on modeled colonization dynamics of Staphylococcus aureus.

Mietchen M, Short C, Samore M, Lofgren E PLoS Comput Biol. 2022; 18(7):e1010352.

PMID: 35877686 PMC: 9352208. DOI: 10.1371/journal.pcbi.1010352.


System dynamic modelling of healthcare associated influenza -a tool for infection control.

Sansone M, Holmstrom P, Hallberg S, Norden R, Andersson L, Westin J BMC Health Serv Res. 2022; 22(1):709.

PMID: 35624510 PMC: 9136787. DOI: 10.1186/s12913-022-07959-7.


Transmission routes of antibiotic resistant bacteria: a systematic review.

Godijk N, Bootsma M, Bonten M BMC Infect Dis. 2022; 22(1):482.

PMID: 35596134 PMC: 9123679. DOI: 10.1186/s12879-022-07360-z.


Anti-Staphylococcal Activity of the Auranofin Analogue Bearing Acetylcysteine in Place of the Thiosugar: An Experimental and Theoretical Investigation.

Chiaverini L, Pratesi A, Cirri D, Nardinocchi A, Tolbatov I, Marrone A Molecules. 2022; 27(8).

PMID: 35458776 PMC: 9032686. DOI: 10.3390/molecules27082578.


References
1.
Bonten M, Austin D, Lipsitch M . Understanding the spread of antibiotic resistant pathogens in hospitals: mathematical models as tools for control. Clin Infect Dis. 2001; 33(10):1739-46. DOI: 10.1086/323761. View

2.
Sehulster L, Chinn R . Guidelines for environmental infection control in health-care facilities. Recommendations of CDC and the Healthcare Infection Control Practices Advisory Committee (HICPAC). MMWR Recomm Rep. 2003; 52(RR-10):1-42. View

3.
van den Driessche P, Watmough J . Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Math Biosci. 2002; 180:29-48. DOI: 10.1016/s0025-5564(02)00108-6. View

4.
Brauer F, van den Driessche P . Models for transmission of disease with immigration of infectives. Math Biosci. 2001; 171(2):143-54. DOI: 10.1016/s0025-5564(01)00057-8. View

5.
Bergstrom C, Lo M, Lipsitch M . Ecological theory suggests that antimicrobial cycling will not reduce antimicrobial resistance in hospitals. Proc Natl Acad Sci U S A. 2004; 101(36):13285-90. PMC: 516561. DOI: 10.1073/pnas.0402298101. View