Modelling the transmission of infectious diseases inside hospital bays: implications for COVID-19

David Moreno Martos, Benjamin J. Parcell, Raluca Eftimie (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
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Healthcare associated transmission of viral infections is a major problem that has significant economic costs and can lead to loss of life. Infections with the highly contagious SARS-CoV-2 virus have been shown to have a high prevalence in hospitals around the world. The spread of this virus might be impacted by the density of patients inside hospital bays. To investigate this aspect, in this study we consider a mathematical modelling and computational approach to describe the spread of SARSCoV-2 among hospitalised patients. We focus on 4-bed bays and 6-bed bays, which are commonly used to accommodate various non-COVID-19 patients in many hospitals across the United Kingdom (UK). We investigate the spread of SARS-CoV-2 infections among patients in non-COVID bays, in the context of various scenarios: placing the initially-exposed individual in different beds, varying the recovery and incubation periods, 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. Our results show that 4-bed bays reduce the spread of SARS-CoV-2 compared to 6-bed bays. Moreover, we show that the position of a new (not infected) patient in specific beds in a 6-bed bay might also slow the spread of the disease. Finally, we propose that regular SARS-CoV-2 testing of hospitalised patients would allow appropriate placement of infected patients in specific (COVID-only) hospital bays.

Original languageEnglish
Pages (from-to)8084-8104
Number of pages21
JournalMathematical Biosciences and Engineering
Issue number6
Early online date12 Nov 2020
Publication statusPublished - 2020


  • COVID-19
  • nosocomial Infections
  • Hospital bay size
  • Mathematical model
  • Computational predictions
  • Nosocomial infections


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