Press Release

Researchers show how patient placement in hospitals can slow coronavirus spread

Published on 13 November 2020

A University of Dundee team have identified the most appropriate placement of patients in hospitals to reduce the risk of spread of Covid-19 infections.

On this page
Sunset shot of University of Dundee Medical School

A University of Dundee team have identified the most appropriate placement of patients in hospitals to reduce the risk of spread of Covid-19 infections. 

Healthcare associated transmission of viral infections is a major problem that has significant economic costs and can lead to loss of life. Covid-19 infections have been shown to have a high prevalence in hospitals around the world, and the spread of the virus might be impacted by the density of patients inside hospital bays. 

While many studies show that healthcare associated infectious diseases seem to spread slower in single (one-bed) rooms versus shared bays, some studies show no clear evidence of a reduction in healthcare associated infections through single rooms. 

To investigate this, a team from the University’s School of Medicine and School of Science and Engineering used mathematical modelling and computational approaches to identify the most appropriate placement of patients and role of periodic testing to reduce the risk of spread of Covid-19. 

In the study, the researchers focused on the transmission of Covid-19 in patients admitted to hospital who were not thought to have Covid-19 and therefore did not meet the criteria for testing.  

They looked at the probability that an infectious disease would spread among patients in single rooms, and in shared hospital bays with four or six beds, which are commonly used to accommodate various non-Covid-19 patients in many hospitals across the UK. 

“In our theoretical study we have shown that, from an epidemiological point of view, there are differences in the disease transmission when there is a single patient per room, or multiple patients per room,” explained Dr Benjamin Parcell, from the University’s School of Medicine. 

“This is an important aspect to be addressed as this virus is highly transmissible. It can result in serious infection in some patients, patients may not always have symptoms, there are high associated hospital costs, and a very large number of patients need to be hospitalised (with or without disease) in the context of a pandemic. 

“We observed that healthcare associated SARS-CoV-2 infections spread more slowly in single (one-bed) rooms versus shared bays on non-Covid-19 wards, and that the infection spreads slower in four-bed bays compared to six-bed bays. 

“When there are multiple bed occupancies in bays, it is important to keep the number of patients as low as possible. These results could inform national guidance on appropriate placement of patients to slow down the spread of Covid-19.” 

To explore the probability than an infectious disease would spread among patients hospitalised in single rooms and in shared hospital bays with four or six beds, the team used a simple network model that considered the probability that each bed had a susceptible, exposed, infected, or recovered individual. 

With the help of this model, they looked at the spread of infection in the context of various scenarios: changes in the number of contacts with infected patients and staff, having symptomatic vs. asymptomatic patients, removing infected individuals from these hospital bays once they are known to be infected, and the role of periodic testing of hospitalised patients. 

The results also support current guidance on routine testing for various staff and patient groups, showing that periodic testing combined with isolating individuals from the bays reduce the probability of exposure and infection. 

The paper, entitled Modelling the transmission of infectious diseases inside hospital bays: implications for COVID-19, is published online on the AIMS Press site.  

Enquiries

Jessica Rorke

Media Relations Officer

+44 (0)1382 388878

jrorke001@dundee.ac.uk

Story category

Research