A Bayesian approach to identifying the role of hospital structure and staff interactions in nosocomial transmission of SARS-CoV-2.

Bridgen, Jessica RE, Lewis, Joseph M ORCID: 0000-0002-3837-5188, Todd, Stacy, Taegtmeyer, Miriam, Read, Jonathan M and Jewell, Chris P (2024) A Bayesian approach to identifying the role of hospital structure and staff interactions in nosocomial transmission of SARS-CoV-2. Journal of the Royal Society Interface, 21 (212). p. 20230525.

Access the full-text of this item by clicking on the Open Access link.

Official URL: http://dx.doi.org/10.1098/rsif.2023.0525

Abstract

Nosocomial infections threaten patient safety, and were widely reported during the COVID-19 pandemic. Effective hospital infection control requires a detailed understanding of the role of different transmission pathways, yet these are poorly quantified. Using patient and staff data from a large UK hospital, we demonstrate a method to infer unobserved epidemiological event times efficiently and disentangle the infectious pressure dynamics by ward. A stochastic individual-level, continuous-time state-transition model was constructed to model transmission of SARS-CoV-2, incorporating a dynamic staff-patient contact network as time-varying parameters. A Metropolis-Hastings Markov chain Monte Carlo (MCMC) algorithm was used to estimate transmission rate parameters associated with each possible source of infection, and the unobserved infection and recovery times. We found that the total infectious pressure exerted on an individual in a ward varied over time, as did the primary source of transmission. There was marked heterogeneity between wards; each ward experienced unique infectious pressure over time. Hospital infection control should consider the role of between-ward movement of staff as a key infectious source of nosocomial infection for SARS-CoV-2. With further development, this method could be implemented routinely for real-time monitoring of nosocomial transmission and to evaluate interventions.

Item Type:	Article
Uncontrolled Keywords:	Humans, Cross Infection, Bayes Theorem, Hospitals, Pandemics, COVID-19, SARS-CoV-2
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Infection, Veterinary and Ecological Sciences
Depositing User:	Symplectic Admin
Date Deposited:	11 Mar 2024 08:44
Last Modified:	14 Mar 2024 19:08
DOI:	10.1098/rsif.2023.0525
Open Access URL:	https://doi.org/10.1098/rsif.2023.0525
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3179244