A unified stochastic modelling framework for the spread of nosocomial infections

J R Soc Interface. 2018 Jun;15(143):20180060. doi: 10.1098/rsif.2018.0060.


Over the last years, a number of stochastic models have been proposed for analysing the spread of nosocomial infections in hospital settings. These models often account for a number of factors governing the spread dynamics: spontaneous patient colonization, patient-staff contamination/colonization, environmental contamination, patient cohorting or healthcare workers (HCWs) hand-washing compliance levels. For each model, tailor-designed methods are implemented in order to analyse the dynamics of the nosocomial outbreak, usually by means of studying quantities of interest such as the reproduction number of each agent in the hospital ward, which is usually computed by means of stochastic simulations or deterministic approximations. In this work, we propose a highly versatile stochastic modelling framework that can account for all these factors simultaneously, and which allows one to exactly analyse the reproduction number of each agent at the hospital ward during a nosocomial outbreak. By means of five representative case studies, we show how this unified modelling framework comprehends, as particular cases, many of the existing models in the literature. We implement various numerical studies via which we (i) highlight the importance of maintaining high hand-hygiene compliance levels by HCWs, (ii) support infection control strategies including to improve environmental cleaning during an outbreak and (iii) show the potential of some HCWs to act as super-spreaders during nosocomial outbreaks.

Keywords: Markov chain; antibiotic-resistant bacteria; hospital-acquired or nosocomial infections; infection control; reproduction number; stochastic model.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cross Infection* / epidemiology
  • Cross Infection* / transmission
  • Disease Outbreaks*
  • Humans
  • Models, Biological*
  • Stochastic Processes