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

Author:

Bridgen Jessica R. E.1ORCID,Lewis Joseph M.234ORCID,Todd Stacy2ORCID,Taegtmeyer Miriam24ORCID,Read Jonathan M.1ORCID,Jewell Chris P.5ORCID

Affiliation:

1. Centre for Health Informatics, Computing, and Statistics, Lancaster Medical School, Lancaster University, Lancaster, UK

2. Tropical and Infectious Diseases Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK

3. Department of Clinical Infection, Microbiology and Immunology, University of Liverpool, Liverpool, UK

4. Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK

5. Department of Mathematics and Statistics, Lancaster University, Lancaster, UK

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.

Funder

UK Research and Innovation

EPSRC

Publisher

The Royal Society

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Estimating disease transmission in a closed population under repeated testing;Journal of the Royal Statistical Society Series C: Applied Statistics;2024-05-06

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