A unified stochastic modelling framework for the spread of nosocomial infections.
Martín López-GarcíaTheodore KypraiosPublished in: Journal of the Royal Society, Interface (2019)
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.
Keyphrases
- acinetobacter baumannii
- methicillin resistant staphylococcus aureus
- human health
- klebsiella pneumoniae
- case report
- healthcare
- risk assessment
- systematic review
- acute care
- drug resistant
- multidrug resistant
- drinking water
- adverse drug
- emergency department
- staphylococcus aureus
- computed tomography
- pseudomonas aeruginosa
- climate change
- magnetic resonance
- molecular dynamics
- cross sectional
- magnetic resonance imaging
- drug induced
- long term care