title: Modeling Nosocomial Infections of Methicillin-Resistant Staphylococcus aureus with Environment Contamination<sup/>.
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 R0is 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.
authors.affiliation: Department of Mathematics, University of Miami, Coral Gables, FL, 33146, USA.
authors.forename: Lei
authors.initials: L
authors.lastname: Wang
authors.affiliation: Department of Mathematics, University of Miami, Coral Gables, FL, 33146, USA. ruan@math.miami.edu.
authors.forename: Shigui
authors.initials: S
authors.lastname: Ruan
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003428:Cross Infection; D004783:Environmental Microbiology; D004787:Environmental Pollution; D006282:Health Personnel; D006801:Humans; D006920:Hygiene; D055624:Methicillin-Resistant Staphylococcus aureus; D008962:Models, Theoretical; D013203:Staphylococcal Infections / Q000382:microbiology* / Q000635:transmission*; D013269:Stochastic Processes