Skip to main navigation Skip to search Skip to main content

Stochastic disease dynamics of a hospital infection model

  • Xi'an Jiaotong University
  • Affiliated hospital of Inner Mongolian Medical College
  • Capital Medical University

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

A stochastic model for hospital infection incorporating both direct transmission and indirect transmission via free-living bacteria in the environment is investigated. We examine the long term behavior of the model by calculating a stationary distribution and normal approximation of the distribution. The quasi-stationary distribution of the model is studied to investigate the models' behavior before extinction and the time to extinction. Numerical results show agreement between the calculated distributions and results of event-driven simulations. Hand hygiene of volunteers is more effective in terms of reducing the mean (or standard deviation) of the stationary distribution of colonized patients and the expected time to extinction compared to hand hygiene of health care workers (HCWs), on the basis of our parameter values. However, the indirect (or direct) transmission rate can lead to either increase or decrease in the standard deviation of the stationary distribution, but the impact of the indirect transmission is much greater than that of the direct transmission. The findings suggest that isolation of new admitted colonized patients is most effective in reducing both the mean and standard deviation of the stationary distribution and measures related to indirect transmission are secondary in their effects compared to other interventions.

Original languageEnglish
Pages (from-to)115-124
Number of pages10
JournalMathematical Biosciences
Volume241
Issue number1
DOIs
StatePublished - Jan 2013

Keywords

  • Modeling
  • Normal approximation
  • Stationary distribution
  • Stochastic simulation
  • Time to extinction

Fingerprint

Dive into the research topics of 'Stochastic disease dynamics of a hospital infection model'. Together they form a unique fingerprint.

Cite this