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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/8251| A Bayesian approach to identifying the role of hospital structure and staff interactions in nosocomial transmission of SARS-CoV-2 | |
| Jessica Bridgen Joseph Lewis Stacy Todd Miriam Taegtmeyer Jonathan Read Chris Jewell | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| https://doi.org/10.1101/2023.09.11.23295353 | |
| https://www.medrxiv.org/content/10.1101/2023.09.11.23295353v1 | |
| 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 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. Competing Interest Statement The authors have declared no competing interest. Funding Statement JREB is supported by a Lancaster University Faculty of Health and Medicine doctoral scholarship. JMR and CPJ acknowledge support from UK Research and Innovation, through the JUNIPER modelling consortium grant MR/V038613/1 (JMR & CPJ), and through grant COV0357/MR/V028456/1 (JMR). CPJ acknowledges support from EPSRC grant EP/V042866/1. | |
| bioRxiv | |
| 11-09-2023 | |
| Preimpreso | |
| Inglés | |
| Público en general | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Materiales de Consulta y Comunicados Técnicos |
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